ࡱ> !kmi`  !bjbj 8& W/O x!x!x!!8П|,!z...VSg K trtttttt$uhݑ9x!S  @.(Vc_ьtc:c:c:l Vx!Vrc:rc:c: L,x!fV ڢ+<ETZ a34axfax!f0 c:m   9p   !!0"Np.!!0"p!!!  Title: Semantic and Machine Learning Methods for Mining Connections in the UMLS Specific Aims The Unified Medical Language System (UMLS) is an invaluable resource for the biomedical community. By integrating biomedical ontologies and terminologies that are used for annotation, organization, sharing and retrieval of biomedical data, the UMLS provides a fundamental resource that drives both basic biomedical discoveries and realization of the nationwide interoperable healthcare infrastructure. One of the intended uses of the UMLS Metathesaurus (Meta) is to support the translation of terms from a source terminology into terms in a target terminology. The concept orientation feature of UMLS provides a direct mechanism for translation - the source term corresponds to a concept that also has a corresponding term in the target terminology. More general types of "translation", as evident from the research literature on UMLS NOTEREF _Ref146371092 \h  \* MERGEFORMAT 2,,,, involve: (1) finding target terms with the closest meaning to the source terms (also known as "mapping"), (2) finding target terms that are related to the source terms (e.g. organisms to diseases caused by the organism) that can serve as proxies for the source terms for various functions (such as information retrieval or knowledge discovery), and (3) finding target terms that satisfy some semantic or structural constraint (e.g. information theoretic distance). The methods for finding such "translations" or connections between terms in Meta (other than the case of one-to-one synonymy) are not at all clear. In fact, the S in the UMLS the "system" - has not yet been fully characterized, let alone realized. Previous attempts NOTEREF _Ref146371176 \h  \* MERGEFORMAT 5, NOTEREF _Ref146371411 \f \h  \* MERGEFORMAT 6, to exploit such connections have depended on either manual selection of relevant connections, or problem-specific algorithms that use expert knowledge about the relative suitability of various inter-concept relationships. Where these attempts have succeeded, they have remained either labor intensive, difficult to maintain, or unscalable beyond their original domains. We believe that machine learning techniques offer automated, generalizable approaches that are appropriate for use with the UMLS, given the large set of potential connections and the need for a problem-independent approach. We are especially interested in solutions that exploit the respective advantages of logical and statistical reasoning. As we will describe in this proposal, there are fundamental challenges to using machine learning approaches with datasets such the Meta, including the complex relationships that exist in any potential training samples, and the relatively unexplored implications of semantic aspects in the data set (i.e. the conceptual and relational nature of the Semantic Network and Meta). In this proposal, we will describe prior work with methods such as statistical relational learning, probabilistic graphical models and social network analysis NOTEREF _Ref146378232 \h  \* MERGEFORMAT 42, that have provided some initial leads, and we propose ways to extend those methods for extracting connection pathways from the UMLS. We hypothesize that learning strategies that exploit the relational features, scale free properties and probabilistic dependencies of connections in the UMLS will identify meaningful inter-term relationships and that a combined approach will perform better across different problem domains when compared to any of the approaches in isolation. The specific aims of this project are to: Characterize the relational features in the UMLS and develop machine learning algorithms to identify appropriate term connections, based on the semantics, probabilistic dependencies and scale free property of the UMLS. Evaluate the proposed approaches with training datasets from a variety of problem domains. Implement algorithms into the UMLS Knowledge Source (UMLSKS) API toolkit for mining and visualizing the connections. There has not been, to our knowledge, any concerted effort to perform machine learning for mining connections over a large semantic resource such as the UMLS. While we have obtained some initial success with some of the proposed approaches, a generalized set of connection mining methods applicable to different problem domains remains to be explored. We believe that UMLS provides a unique fertile ground to develop novel semantic relational mining methods and advance our understanding of mining large biomedical concept graphs. Furthermore, if our methods prove successful, they should be adaptable to a variety of applications that need to translate, or otherwise find connections among, terms across disparate terminologies, thus putting the "S" in "UMLS". Background and Significance In order to describe our proposed approach, we first provide some brief background information about the various machine learning techniques we will employ. We also describe the aspects of the UMLS that are relevant to the relational features that will serve as input to our methods, as well as prior relevant work that has studied extraction of connections from the UMLS. 1. Machine Learning and Relational Data Mining In general, the machine learning or data mining approaches can be categorized into supervised and unsupervised methods. The cases where explicit training data (or labeled examples) are used to learn a model (classification or regression) for making future predictions fall under supervised learning approach, whereas the cases where the algorithms learn a model from unlabeled data (without any notion of output) constitute an unsupervised approach. In semi-supervised learning, the classifier is constructed using both labeled and unlabeled examples. The field of relational data mining10 uses methods from Inductive Logic Programming (ILP) to learn over datasets by modeling the data, schema and background knowledge as first order logic predicates; generalized rules are inductively learned from these facts, which can then be applied on future instances. The methods from relational data mining have been successfully applied in molecular structure activity prediction, constructing biological pathways, etc. Relational learning approaches that are more statistically oriented (rather than logic-based) have also been explored. One core underlying assumption is that the training examples are not independent and identically distributed (i.i.d) but related in a complex network fashion. There has been convergence of methods from probabilistic learning24, web mining, social network analysis NOTEREF _Ref146378232 \h  \* MERGEFORMAT 42 to mine the aforementioned datasets for predicting protein-protein interactions, ranking web pages, finding links between friends or terrorist, etc. The presence of links across data instances requires careful consideration of statistical issues while mining connections. The methods in this domain are still evolving and the networks studied are relatively poor in the variety of semantics of nodes or edges when compared to Meta. An interesting analysis shows how the presence of semantic types for nodes and edges can be exploited for relationship detection applications. Another effort relevant to our work is the semantic association ranking approach; the authors use an association ranking score that is based on semantics of relationships, concept depth, etc, for mining connections across semantic entities. We now describe the general classes of problems involved in mining connections and later discuss how these problems instantiate to specific connection mining problems in Meta Link Classification: A set of connection paths that are judged as "correct" (the training set) is used to train the system to classify\label additional connections (the test set) in supervised manner. Once classified, links can be used to classify the nodes in the links, for example, determining the category of a Web page based on the outgoing hyperlinks to a set of labeled Web pages. Connection Clustering/Community Identification: In this case we are interested in identifying hidden clusters of nodes based on the connections they are involved in (hence connections are used as features). This set of problem appears in uncovering virtual communities within social networks. Link Prediction: The goal is to predict whether a connection exists between two nodes and determine the strength of the connection based on some score. This has been traditionally used for studying terrorism networks and protein interactions. Collective Inference: In network datasets, defining separate training and testing sets is difficult, since all the entities are typically connected in the network. The problem of collective inference deals with situations wherein the values/labels of a few nodes in the network are known and the goal is to determine labels of other all other nodes in the network. This problem class is also relevant in semi-supervised settings, where only positive training data are available. One prominent use-case of collective inference is studying regulatory networks of cell. Several of the connection mining problems in Meta become special instances for these general problem classes. The terminology translation can be modeled as link prediction or link classification problem as described in the preliminary work section. The problem of clustering phenotype concepts based on their links to genotype concepts NOTEREF _Ref146371092 \h  \* MERGEFORMAT 2 is an instance of connection clustering application. Link prediction can be used for finding connections suitable for information retrieval tasks such as linking patient data to information resources and also for knowledge discovery applications. 2. Machine Learning Models Explored in this Proposal One method for modeling and inferring connections in complex structures such as the UMLS is probabilistic graphical modeling, NOTEREF _Ref146378168 \h  \* MERGEFORMAT 12,, which integrates probability theory and graph theory. Several other important biomedical problems,,,, can be easily modeled using this framework. Bayesian networks or belief networks are one of the most widely studied instantiation of probabilistic graphical models. The core idea behind Bayesian networks is simple: given a directed acyclic graph, the nodes represent random variables and the edges (or arcs) represent the probabilistic dependency among the variables. Some current research challenges include selecting the best Bayesian network structure (from all possible permutations) that maximizes the likelihood of observed data and estimating the conditional probability distribution for variables when the training data is not complete21. One promising application of learning Bayesian network structure over large graph-based datasets uses association rule mining (counting frequent items that occur often together) to model co-authorships networks. Once the network is constructed, the next step involves inference or answering queries wherein we are given values for a set of variables (evidence) and the goal is to find probabilities for other variables in the network. To handle tractability issues, the inference algorithms tend to trade off accuracy for efficiency. Bayesian networks can be used to perform connection mining, as demonstrated with the development of Probabilistic Relational Models (PRM). The essential idea behind a PRM is that a random variable can considered as an object (in the object-oriented sense) that can have attributes which are themselves random variables with probabilistic dependencies across other objects. PRMs have been successful in representing Bayesian networks over relational databases by directly modeling database tables as objects, the columns as attributes and the key constraints as attribute dependencies. However, it is not clear how PRMs can be used over network or graph datasets where there is no distinct notion of schema and instances. Support Vector Machines (SVMs) embody a supervised learning approach used for classification and regression problems. The groundwork leading to SVMs can be traced to the statistical learning theory developed in 1970s due to Vapnik and Chervonenkis. Recently, SVMs have been receiving increased attention which can be attributed to two of their interesting properties. Firstly, SVMs perform structured risk minimization, which implies that of all possible solutions (i.e. the planes separating positive and negative samples), SVMs try to find the best solution whereas traditional algorithms such as neural networks stop when they find the first solution. Secondly, SVMs use kernels to map the data input into a higher dimensional feature space where the dataset becomes linearly separable. More concretely, the kernel, k, is a function that takes as input data vectors and produces a scalar value by performing a dot product,  EMBED Equation.3  where, x and y are input vectors and ( is the mapping function that maps the input values to an expanded feature space. The most important property of kernels (the kernel trick) is that they calculate the dot product without expanding the mapping functions ((). One of the applications of kernel-based methods for connection mining paradigm uses concept graphs as the input vectors. Recently, SVMs have been shown to be closely related to classical regularization theory and spectral graph theory. Promising applications of these theories for semi-supervised learning and ranking over graph-based data are being actively studied. Learning over semantically typed graphs such as Meta remains relatively unexplored in the machine learning community. The field of social network analysis (SNA) provides methods to study the social relations among a set of actors (people, groups or organizations). SNA considers various properties of networks, such as betweenness, centrality, clustering coefficient that are directly applicable for the proposed connection mining problem over Meta (see section D5). An important discovery from SNA is that most real world networks are scale-free - there are few nodes with a high degree of connectivity (hubs) with most other nodes having a low degree of connectivity. Some examples of scale free networks are the Internet, the World Wide Web, the Hollywood actor network (Kevin Bacon being the most famous example), metabolic networks etc. A precise metric for measuring scale-freeness was proposed by Li et al. Analyzing the network structure to identify modular structures responsible for specific function or property has recently led to range of novel discoveries,,. 3. The Unified Medical Language System The Metathesaurus (Meta) of the Unified Medical Language System NOTEREF _Ref146378530 \h  \* MERGEFORMAT 1(UMLS) is a collection of terminologies in which synonymous terms (from the same different terminologies) are mapped to single concepts, each of which has a Concept Unique Identifier (CUI). The concepts are related to each other through relationships largely obtained from the source terminologies. The semantic types themselves are organized into a hierarchical structure that includes relations among the types. The 2006AC version of Meta integrates 138 biomedical terminologies and there are currently total 1,352,403 unique concepts in the Meta. The SN provides top-level concepts (e.g. Clinical Drug, Laboratory Test) to categorize the concepts in Meta. The relationships present in the source terminologies are asserted between the concepts (and not between the terms) in Meta, hence we obtain relationships across different terminologies. We will be using following UMLS files: MRSTY (for concepts and corresponding Semantic Types), the MRREL (for the relationships and corresponding source terminology), and the MRCONSO (concepts and their source terminologies). We define source concepts and target concepts as being a Meta Concept to which terms from a source or target terminology (respectively) are mapped. We define a connection as a set of transitive relationships between a source concept and a target concept connected by zero or more intermediate terminologies through links in MRREL. 0-Step Connection The source concept and the target concept are identical. 1-Step Connection A direct relationship between a source concept and a different, unique target concept. 2-Step Connection A set of two transitive relationships, wherein the source concept is related to a second concept (with terms from some other terminologies), which in turn is related to the target concept. Connection set All possible connections between a set of source concepts and a set of target concepts. 4. The UMLS and Connection Mining UMLS researchers have traditionally focused on "deterministic" algorithms that use some domain or expert knowledge for discovering cross-terminology paths. Cimino et al. NOTEREF _Ref146371176 \h  \* MERGEFORMAT 5 first proposed an algorithmic approach to convert ICD-9 to MeSH terms using the UMLS. Bodenreider and colleagues NOTEREF _Ref146371411 \h  \* MERGEFORMAT 6 used a deterministic graph traversal algorithm to exploit the "semantic locality" in UMLS to map any concept in the UMLS to MeSH. Zang and colleagues described an approach for using associative relationship patterns to map different ontologies. These associative patterns were "mined" using simple frequency counts based on occurrences, but no methodical approach was used to measure the support or confidence for the patterns. More recently, Fung et al.9 showed another deterministic approach that uses the synonymy, ancestor expansion and descendant expansion methods over SNOMED-CT to ICD-9-CM mappings as gold-standard. Some UMLS applications have implicitly used some form of connection mining. In their recent work on phenotype analysis, Butte et al. NOTEREF _Ref146371092 \h  \* MERGEFORMAT 2 used the relationships in Meta to create a genome-phenome network. A terminological approach to discovery of gene and disease relationships was shown in Genestrace application NOTEREF _Ref146378610 \h  \* MERGEFORMAT 7 by traversing the relationship paths in the UMLS. Various projects, dealing with ontology integration and maintenance often can be solved using connecting mining approaches. Existing methods generally rely on simple lexical matches, structural matches, or deterministic path traversal algorithms largely driven by domain specific heuristics. 5. Significance With nearly 170,000 concepts and 22,000,000 relationships from about 140 terminologies, the Unified Medical Language System has grown into an enormous biomedical computational resource. The UMLS is now used in a variety of domains ranging from basic biomedical sciences to building national healthcare infrastructure. Existing efforts using the UMLS are only scratching the surface - the situation aptly illustrated by the Indian proverb about reconstructing an elephant by touching only a part, the leg makes it a tree trunk, the tail makes it a snake. We believe that using a machine learning approach, more specifically, relational connection mining is a promising direction to realize full potential of the UMLS for supporting inter-terminology translation. The UMLS is also uniquely positioned to serve as a testbed for understanding, evaluating and advancing the development of relational connection mining methods that could be applicable to any large network. Nearly two decades of largely manual efforts have been poured into the construction and maintenance of the UMLS. The rich semantics of concepts and relationships in the UMLS demand special consideration when developing statistical learning approaches over them. The important challenge lies in developing methods that can coherently integrate the statistical and semantic approaches to mine the connections. Furthermore, the network structure of UMLS also requires careful investigation; the concept hubs that arise due to the scale free property of the Meta network have important implications in generating the connection paths. Another aspect is concerned with modeling and inferencing over the probabilistic dependencies over the large graph such as the Meta. Given the range of connection mining problems, we propose using a combined strategy that takes into consideration all aforementioned important characteristics for mining connections in the UMLS. With the widespread pervasiveness of applications using the UMLS or its constituent terminologies, we believe that notion of using connection training sets is not a very far fetched assumption. One of the goals of the proposed approach is to allow end-users to input training connections (supervised or semi-supervised) or their datasets annotated with terminology concepts and then query for concept connections in the UMLS. Alternatively, we will provide users with a library of connections datasets and methods with flexible semantic constraints to query for connections. To bring the proposed approaches closer to the UMLS users, we propose to extend the UMLS KS (Knowledge Source) API NOTEREF _Ref146378454 \h  \* MERGEFORMAT 13 to perform connection mining. Preliminary Studies Our preliminary studies relevant to the proposed work stretch back over two decades, beginning with work on frame-based representations of Dxplain terms that were used by collaborators for term translation,, and frame-based representations of ICD, MeSH, SNOMED and CPT terms to achieve automated translation. Additional work has involved methods for linking clinical systems to Medline, building knowledge bases, and exploiting the UMLS knowledge to support terminology maintenance of local terminologies, as well as the UMLS itself. We have used the dataset used in Fung et al NOTEREF _Ref146378759 \h  \* MERGEFORMAT 10 to evaluate the possibility of using machine learning approaches for terminology translation. We could trace paths from 98.9% of SNOMED-CT terms to ICD9CM term using the UMLS 2005AA. We trained a Nave Bayes classifier using the USSR training set (Section D1) and Semantic Path features (Section D3b), which resulted in positive classification of 99.82% of translation paths. A cross-validation with random negative paths (strict 2-step, Section D1) resulted in an AUC score NOTEREF _Ref146385617 \h  \* MERGEFORMAT 78 of 0.94. We have shown how the UMLS terminologies can be used to support contextual information retrieval in electronic health records for Infobuttons. To show the applicability of connection mining for the problem of linking electronic health records and information resources, we used the OHSUMED information retrieval gold standard and then showed that we were able to recover 94% of gold-standard MeSH concepts after classification. We investigated the UMLS network structure using methods from social network analysis to understand whether Meta exhibits scale-free properties. We evaluated the source terminologies separately and Meta in its entirety. The results showed that the overall Meta structure exhibits scale-free characteristics as depicted in the Figure 1 with few concept hubs grabbing most of the relationships and large number of concepts connected by only a small number of relationships.  SHAPE \* MERGEFORMAT  Research Design and Methods In this research, we will develop and investigate approaches for mining connections in the UMLS. The overall workflow and major components of the grant proposal are sketched in Figure 2. Given the complexity of mining relational connections over the UMLS, we hypothesize that an approach that combines the following strategies will perform better than any single approach in isolation across different problem domains:- a). Relational Features: Develop or use features that capture or summarize the properties of the connection network and use them with standard machine learning algorithms. b). Scale Free Abstraction: Exploit the power-law property of the UMLS to abstract the network into a Hub Graph and mine the abstract graph structure to generate connections. c). Probabilistic Graphical Inference: Learn a Bayesian network structure from training connections and use semantic traversal to prune the number of concepts in the network to perform inference.  SHAPE \* MERGEFORMAT 1. Training Connection Datasets An important aspect of this proposal is to use or develop a wide range of training datasets and also to ensure the applicability of algorithms over a variety of connection mining problems that arise in the biomedical domain. We now describe each of the training connection datasets and then discuss the suitability of the datasets with proposed relational mining approaches. SNOMED-CT to ICD9CM (SCT2ICD): The current version of SNOMED-CT provides mappings NOTEREF _Ref146378825 \h  \* MERGEFORMAT 49 from SNOMED-CT to ICD9CM terms. These mappings exist as separate relationships in the UMLS with relationship attribute type mapped_from and mapped_to. We can isolate these relationships from Meta and evaluate the performance of proposed approaches on version of Meta without these mapping relationships. We found 72,976 mappings from the 2005AA distribution of UMLS. Dxplain Connections (DxConn): The Medical Entities Dictionary (MED), the terminology used at the Columbia Presbyterian Medical Center, contains relationships from laboratory tests to possible disorder concepts, originally derived from DXplain knowledge base. In addition, the MED contains mappings to the UMLS and LOINC terms. We will extract these UMLS laboratory-concept-to-disorder-concept connections and use them as gold standard for evaluation. Information Retrieval Gold standard, (OHSUMED): The OHSUMED NOTEREF _Ref146378888 \h  \* MERGEFORMAT 62 is a widely used test collection of biomedical query-document pairs for evaluating information retrieval systems. The collection was prepared by capturing 106 clinical information needs/queries along with the history and context of patients while physicians were using a decision support system. Based on the query and context, a set of medical librarians and physicians searched MEDLINE articles from given time period (1987-1991, 348,566 articles) and classified them in to three relevancy classes definitely(d), probably(p) and not relevant(n). An example OHSUMED record is: Context/History 60 year old menopausal woman without hormone replacement therapy Query Are there adverse effects on lipids when progesterone is given with estrogen replacement therapy MEDLINE UI 87097544(d), 87157536(p), 87157537(n), We parsed the context and query into the UMLS concepts using MetaMap (MMTx) (with 1000 score) and extracted MeSH headings for corresponding MEDLINE articles. We have constructed a gold-standard set of connections from 385 SNOMED-CT concepts to 2252 MeSH concepts for mining connections suitable for information retrieval applications. UMLS Sampling Standard for Relations (USSR): A training set should ideally be representative of the underlying distribution of the dataset; using training examples outside of UMLS would create models overfitted to the training set or problem domain. We propose here methods for sampling the UMLS to create training sets that exploit specific properties of the UMLS structure to determine relative connection strength i). Connection Distance Approach: The presence of a relationship between two concepts in the UMLS signifies the fact these concepts are relevant or related based on some source biomedical terminology. Hence, we assume that concepts related by 1-Step links have a stronger relationship as compared to concepts related only by greater than 1-Step links. This approach is similar to the notion of Data Processing Inequality. In general, N-step Connection >relevant Strict (N+1)-step Connection By Strict (N+1)-step connection we mean that the corresponding concepts dont have any M-step connections, where Md"N. Using this inequality, we can label learning class for the concept pairs, such as N-step=Positive and strict (N+1)-step=Negative. We used this gold-standard in our preliminary studies. ii). Connection Voting Approach: In the UMLS, several source terminologies can assert the same or different relationship between two given concepts, all of which are stored in the UMLS. Using a simple voting approach based on number of connection paths between two concepts in the UMLS, we can determine the strength of relatedness for given concepts. The higher the connection count (CC) between two concepts (given n-step connections), we assume the higher link strength or relevancy. The learning classes for concept pairs can be created by using a threshold bins k1..m (where k1>k2>km) for connection counts, where if CC > k1 and CCk2 and CC. Based on our preliminary experiments, the accuracy of classification beyond 3-step drops down considerably. However, further analysis of this feature in combination with other relational features remains unexplored. We also observed that strong connections have higher numbers of n-step connections as compared to weaker connections. In other words, stronger relationships are more likely to be asserted across different source terminologies constituting the UMLS. b) Relational Feature: Semantic Path The UMLS adds broad semantic categories for all concepts (Semantic Types, TUI) and relationships (REL, RELA) derived from the source vocabularies. A connection can be possibly abstracted into these broad semantic categories, which then creates learning tuples of the form <(RELi, RELAi, IiTUI, RELi+1, RELAi+2)+:Decision Class> where, RELi, Relationship Types: The relationship type (REL) for the ith step relationship in a given connection e.g. 2-step connection having REL1 = CHD, REL2 = SIB. RELAi, Relationship Attributes: Various qualifiers are assigned to REL that provide a more specific semantic meaning for the relationship e.g. REL1=RO, RELA1=clinically_associated_with. IiTUI, Intermediate Semantic Types: The Semantic Type of an intermediate concept at a given depth, i, in the connection. Some concepts can have more than one Semantic Type - for such cases we create different independent links for each corresponding Semantic Type. c) Relational Feature: CDist In our previous work, we proposed a metric, CDist NOTEREF _Ref146379245 \h  \* MERGEFORMAT 8, which measures the conceptual distance between two concepts in the UMLS Metathesaurus. This feature uses taxonomic relationships to calculate the distance between concepts. This feature can be applied locally between every two neighboring concepts in the given connection path or it can be used as a global measure between the source and target concept. This feature is suitable for link classification tasks wherein the source and target concepts are available from the training set. d) Other Potential Relational Features of the UMLS In this section, we briefly enlist other potential relational features of the UMLS, which can be relevant for specific training sets or mining tasks. Semantic Depth/Concept Granularity: This feature calculates the minimum hierarchical depth of a given concept in the taxonomic hierarchy of UMLS Metathesaurus. It represents the granularity of the concept, for example, general a concept such as Neoplasm would have a smaller semantic depth as compared to Breast Cancer which is a more specific concept. Concept Degree (CD): The number of relationships for a given concept in the connection path. REL Types and CD ratio: In the UMLS there are different types of relationships (REL); the ratio of number of different REL types and total number of relationships provides a measure of the "semantic complexity" of the concept. Common "Friends": For any two given concepts, the count of overlapping friends or adjacent concepts (this measure is known as the clustering coefficient in network analysis literature). Further, link clustering coefficient NOTEREF _Ref146371033 \h  \* MERGEFORMAT 20 can also be applied. Semantic Types Taxonomic Distance: The taxonomic distance count or information theoretic distance based on minimum subsumer of Semantic Types in the Semantic Network corresponding to any two given concepts in the connection path. Mutual Information: A probabilistic notion of calculating similarity based on how often two concepts are connected by an edge and how often they occur independently. e) Feature Selection For supervised learning tasks with several of the aforementioned relational features, a feature selection pre-processing step is required to eliminate features that contribute noise or are found redundant, given the training set. Given the relational learning problem with UMLS, the number of relational features can easily range into order of hundreds with all pairs of concepts in the connection paths (of different lengths). We will use a feature selection process involving variable ranking (where each feature is individually ranked using methods such as correlation coefficient, information theoretic gain etc) followed by variable subset selection involving a directed search for variable subsets starting with top ranked variables. 4. Learning Method: Relational Kernels - relSVM The idea of minimizing guaranteed risks and classification at higher dimensions (using kernels) makes Support Vector Machines (SVM) an attractive approach for machine learning problems. One intriguing question that we intend to explore is can we reproduce SVM-like properties for classifying relational connections? The challenges in developing kernel-based algorithms for relational datasets such as the UMLS include: a). Developing principles of regularization theory for relational datasets. In the regularization theory, for classifying and ranking over graph data, the central algorithm relies on using laplacian graph matrix, L (equivalent to the regularizer), which is defined as follows,  EMBED Equation.3  where D is the diagonal matrix, Di,i = degree of ith node and W is the weight matrix and Wi,j represents the weight of the edge ei,j. The approach can be extended for classifying UMLS connections, however the weight matrix, W, has to be populated using a strategy driven by the relational mining problem under consideration. Consider for example, we are interested in mining connections for performing terminology translation, the relationship edges represented by SY (synonym), mapped_from, mapped_to would be weighed higher than hierarchical relationships. Existing graph regularization theories only exploit the degree distribution and edge weights to produce a score for graph nodes as the final output. This approach does not explore combination of features arising in higher dimensions to perform classification as described next. b). Developing kernels for relational datasets. Another significant challenge lies in developing kernels that can map the relational features into higher dimensional space where the connections become linearly separable. In the case of relational learning with UMLS, we propose the following two kernel functions: Semantic Kernels will exploit the semantics of relationships between given a set of concepts in the connections (positive and negative). The idea here is to map the concepts to a semantic space where the connections become separable. To illustrate the approach, we define a SemDist kernel as, k(cpx, cpy) = SemDist(cpx, cpy) where cpx is the connection path of UMLS concepts {cpx1, cpx2, cpx3} the function SemDist calculates the pair wise information theoretic distance using the Resnicks measure NOTEREF _Ref146378332 \h  \* MERGEFORMAT 72 SemDist(cpx, cpy) =  EMBED Equation.3  where the probability, p, of the least common parent of concepts,  EMBED Equation.3  is calculated from the frequency of source terminologies contributing the concept. p(concept) =  EMBED Equation.3  The mapping function ( (cp) essentially defines the semantic neighborhood of concept, cpi i.e. the concepts that are semantically near (related by SY, SIB, mapped_from etc). Hence the kernel trick implies that we dont need to calculate pairwise semantic distance for all neighboring concepts. Intuitively, the approach is feasible because there is a good chance that at higher conceptual abstractions, the SVM is able to cleanly classify the connection paths. Consider for example two connection paths: Sodium ( Hypernatremia and Pneumococcus ( Pneumonia - the SemDist(Sodium, Hypernatremia) will have higher value (low p) as compared to SemDist(Sodium, Pneumococcus or Pneumonia), thereby classifying the connection paths with large margins. Connection Hub Kernel will use the modified version of median function (see Section D5) that takes as input two concept vectors and finds the average distance to hub. k(cpx, cpy) = JointMedian(cpx, cpy) where cpx is the connection path of UMLS concepts {cpx1, cpx2, cpx3} the function JointMedian finds the average pairwise distance to the common hubs between concepts The proposed relSVM approach tries to recreate the useful properties of SVM (structured risk minimization and classification at higher dimensions in relational datasets). We showed how the approach for graph regularization can be applied to UMLS connections and proposed a set of new kernel functions that rely on the relational connections. c) Limitations Although the proposed approach seems to be promising, a theoretical question remains unanswered, what are the statistical interpretations of the proposed semantic kernels and regularizations theories? Traveling through ISA edges or using Semantic Types is hard to visualize in a statistical sense. Nevertheless, we believe that an empirical analysis of the proposed approaches will help in getting a deeper understanding of algorithms exploiting statistical and semantic properties of the UMLS. 5. Learning Method: Scale Free Relational Learning (SaFREL) In our preliminary investigations, we observed a power law distribution in the network of UMLS Metathesaurus. Given the presence of large hub concepts having several relationships to other concepts make the UMLS a "small-world". Hence, given any two concepts from the UMLS, the average number of intermediate concepts in the connection path is very small. The implications of this observation are critical in developing relational mining algorithms over the UMLS. We now illustrate the Scale Free Relational Learning SaFREL approach that gives special consideration to the hubs in performing the learning and connection dataset generation. a) UMLS-HuG: Generating the UMLS Metathesaurus Hub Graph One of the preliminary steps necessary is to generate a UMLS Hub Graph (HuG) derived from the actual UMLS graph using a median function, M that maps each concept to the nearest hub concept (Figure 3). Formally, given the UMLS graph G= where C represents the set of all UMLS concepts and R represents set of all relationships, the goal is to find k subsets of the concept set, C1, C2Ck such that C1 ( C2 ( C3(Ck = C and H={h1,h2,..,hk} represents the respective hub concepts, i.e. hi ( Ci and M(c) = hi iff c ( Ci. There are several strategies to select an appropriate median function, Degree ranking: Select the top k nodes with highest number of relationships Betweenness-Centrality: Select top k nodes based on how many shortest paths pass through a given node. Moreover, we need an approach to select, k, the number of hub nodes in the HuG. An evaluation strategy described in uses criteria such as average distance to hub, average distance approximation error and path approximation error. The HuG will be used to generate connections that span across the hubs and data source to perform modular mining.  SHAPE \* MERGEFORMAT  Figure 3: Generating UMLS Hub Graph (HuG) from the actual UMLS Metathesaurus b) HuG-based Connection Generator HuG serves as a proxy graph to efficiently generate all sets of n-step outgoing connections for a given concept or connections between a set of concepts. Several heuristics can be used to generate paths with specific traversal patterns across the hub nodes. Semantic Hub Filtering: To eliminate the traversals of hub concepts based on their Semantic Types such as Qualitative Concept, Quantitative Concept or Functional Concept. The idea is to traverse only the hubs that are genuinely informational and not simply bookkeeping concepts. Avoid Hubs Constraint: Select path that passes through minimum number of hubs. The 2 heuristics to avoid connections through hubs Explore Same Hub First: Here we first explore the concepts in same hub before exploring the neighbors (bread first search). N-Step Neighbor Hub Exploration: In this approach, we perform a more depth first search where we first explore the neighbors followed by the local nodes. Max-Flow Connections: Perform traversal that passes through hub nodes that maximizes the sum of the number of edges between the hub nodes. Refinement Abstraction Connections: Perform traversal at a given level of refinement. Highly refined HuG will contain more hubs and potentially generate longer paths. d) HuG Modular Mining It has been shown in metabolic and regulatory networks that the hub nodes and related neighboring nodes form a functional module that is responsible for a higher level cell function. Identifying such functional modules allows clustering the network into meaningful chunks over which useful connections can be drawn. Although terminologies dont necessarily represent a biomedically functional modules but they do exhibit strong ontology patterns used for modeling specific classes of entities or events. For example, in SNOMED-CT the procedures have a set of method-attribute, devised-used or procedure-site (pointing to an anatomical location that can be part of finding-site from disorder hierarchy). In biomedical ontologies specific modules are connected by some common hubs (such as body location or causative-agent etc), hence one of the goal in modular mining is to traverse from one module to another without getting entangled in the same hub. The solution is to learn the traversal patterns from training connections such as knowing what types of hubs lead to negative connections? Is depth first or breadth first strategy more predominant? What level of refinement abstraction is necessary to find best connection paths? We will develop an approach to label such connection traversal patterns and learn the traversal patterns using the range of available training datasets. 6. Learning Method: Relational Bayesian Networks (relBN) Probabilistic graphical models such as the Bayesian networks (BN) allow modeling of conditional independences among the features or random variables. In our proposal, we will use the Bayesian network approach to model probabilistic dependences across the concepts in the UMLS. The eventual goal is to answer queries on the dependency graph of the form, Given the Concept A from UMLS, what is the probability of finding a meaningful connection to Concept B? We will use the relational structure of UMLS, hence obviating the need for BN structured learning. We propose the notion of performing Semantic Traversal in the UMLS graph to scoup the portion of UMLS relevant to the training data and hence avoid scalability issues for inferencing. The parameters for Semantic Traversal will be calculated using the maximum likelihood approach. We now present in detail the relBN algorithm to learn probabilistic model from the UMLS. The joint probability of all concepts of the UMLS subgraph of size, n, can be represented as a product of factorized set of conditional probabilities: p(c1, c2, c3 cn) =  EMBED Equation.3  where pai represents the parents of concept ci in the subgraph generated by the Semantic Traversal algorithm described next. a) Semantic Traversal Algorithm The algorithm performs a traversal of the UMLS graph along the edges labeled either as associative relationships or hierarchical relationships as shown in Figure 4; the traversal along corresponding dimension is constrained by the Semantic Width Parameter ((w) and the Semantic Height Parameter ((h) respectively that define the upper limit for the number of edges that will be traversed. The traversal is seeded using a concept from the training connections and the algorithm is terminated when all the concepts from the training connections are found or the traversal exceeds the upper limit defined by the parameters. The essential idea behind Semantic Traversal is to create a minimal subgraph that represents the universe of training connections and the intermediary concepts (the slice represented in the Figure 4).  SHAPE \* MERGEFORMAT  Figure 4: A schematic showing the relBN algorithm b) Parameter Learning The conditional probability tables at each concept node will be calculated based on the frequency of training connection paths that pass through the concept. Each concept node can be considered as a random variable that takes on values {present, not present} along with other set of nodes in the connection, for example, p(Staphylococcus Infection=Present/Staphylococcus=Present) = 8/10 and p(MRSA=Present/Staphylococcus Infection= Not Present) = 1/10. Let (cpt represent conditional probability tables at each concept node, so the maximum likelihood formulation for the relational Bayesian network can be represented as Maximum Likelihood, L =  EMBED Equation.3  which says that given the conditional probability tables ((cpt), the semantic height ((h) and width((w) parameters, what is the probability of observing the training connection data (tci) and for what values of the parameters, the sum of log likelihood becomes maximum. The problem here is to determine the values of semantic height and width parameters since the rest of values are known to us ((cpt being calculated from frequency of training connection paths). We make an observation here that the values of (h and (w will always be discrete and limited by the diameter of the UMLS network (see Section D5). Hence instead of theoretically solving the max likelihood equation, we iterate through all possible values: (w, (h = 1 to D (diameter of the Meta network) Once the model parameters are calculated, the queries for possible connections can be performed over the network using probabilistic inference methods. The queries can be of the form what is the probability for a set of concepts being connected? Given the sheer size of Meta, we will use approximate inference techniques NOTEREF _Ref146379570 \h  \* MERGEFORMAT 35 such as MCMC (Markov Chain Monte Carto) simulation and variational approaches. c) Limitations and Caveats One of the trade-offs in limiting the size of the UMLS graph by using semantic traversal is that we would not be able to answer queries for concepts that are not part of the pruned graph. A possible solution is to use an approach wherein we dynamically relearn the parameters after adding connections from query concepts to the pruned graph. Another caveat is that the meaning of a probabilistic edge does not imply notion of causuality but a constrained interpretation given a training dataset and a concept what is the probability of observing the related concept in the connection. 7. Connection Ranking We now outline a strategy to combine the proposed approaches for mining connections relational features, relSVM, SaFREL and relBN. The underlying assumption here is that we performing supervised or semi-supervised learning with the proposed methods. Given the fact that all proposed algorithms will result in some form of classification error score or connection prediction score for the test connections, we can use these scores to rank the test connections and generate the top k list for each method. We propose to develop a Connection Intersection Function(CIF), that performs an intersection of the top k lists using a relational approach. The rationale behind CIF is that the set of top k connections can mean a single connection pathway or a subgraph of connected concepts, hence the task of calculating the intersection of results requires a more methodological approach than mere set intersection.  SHAPE \* MERGEFORMAT  Figure 5: The connection ranking component integrates the output from proposed algorithms to produce a final set of connections. The CIF will start by calculating simple set intersection of concepts and relationships and add them to final ranked list of connections (FC). Across this set of concepts in FC, all possible m-step connections will be retrieved from Meta, which can possibly result in addition of new intermediate concepts. Of these new intermediate concepts, only the concepts that were part of original top k connections will be retained and added to FC, the rest will be dropped. The process is then repeated until no new concepts are added to FC. The goal here is to calculate the closure of connections that will be included in the final ranked list. 8. Evaluation The evaluation of the proposed approaches is relatively straightforward, largely driven by the range of training connections/gold-standard datasets. For supervised and semi-supervised approaches, such as link prediction, link classification and collective inference, the evaluation method is straightforward where there is a clear notion of training and testing data. We will use cross-validation approach to calculate a reliable metric such as the area under the ROC curve (AUC). A detailed evaluation matrix describing the suitability of connection mining problems for given training dataset and algorithms is shown in Table 1. The evaluation of community identification/clustering which is totally an unsupervised approach will require manual evaluation of results to interpret the outcome of clustering. Table 1. Evaluation matrix for the proposed approaches with training datasets and their suitability for class of connection mining problem. Training datasets X Proposed approaches X Suitable connection mining problemsTraining DatasetsRelational FeaturesrelSVMSaFRELrelBNConnection IntersectionNBSVMEMSCT2ICDLCLC-LCLC,LPCI, LPLC,LP,CIDxConnLC,LPLCCCLC,LPLCCILC,LP,CIOHSUMEDLC,LPLC,LPCCLC,LPLC,LPCILC,LP,CIUSSRLCLC-LCLC-LCICSLLCLC-LCLCCILC,CIPALC, LPLC,LPCCLC,LPLC,LPCI,LPLC,LP,CICCeNLC, LPLC,LPCCLC,LPLC,LPCI,LPLC,LP,CIG2PLCLCCCLC, LPLC, LPCI,LPLC,LP,CIExpGSLCLC-LCLC-LCNB = Nave Bayes, SVM = Support Vector Machines, EM = Expectation Maximization LC = Link Classification, LP = Link Prediction, CI = Collective Inference, CC=Community Indentification\Clustering To evaluate the proposed hypothesis that combined approach integrating relational features, relSVM, SaFREL and relBN will result in better performance than any single method in isolation, we will use the following approach - compare the output of ranked list from CIF, the ranked lists from individual methods and the gold-standard rankings. The hypothesis will be considered proven if the CIF consistently results in higher overlap with gold-standard than individual methods across all training datasets. 9. Implementation a) UMLSGraphGen One of the important challenges in implementing connection mining over an enormous graph such as the UMLS is to efficiently perform in-memory operations. We propose an implementation approach that combines disk and in-memory storage for generating and traversing connections. We have represented the UMLS as a 2 dimensional in-memory sparse connectivity matrix (in Matlab) for traversing connection pathways. We loaded the 2005AA version with 1,179,179 concepts that used 1.2G of RAM when completely loaded. The matrix elements point to disk-based database of 22,623,185 relationship (REL) and relationship attributes (RELA). The concept mappings to Semantic Type and source ontologies are also stored in a database, which require simple look up after costly transitive link traversals. Similarly, we can represent the UMLS Hub Graphs (HuG) where each matrix element is a cell corresponding to multi-level HuG. The Semantic Traversals can also be efficiently performed using this representation with an additional hash map containing semantics of relationships. b) Connection Mining using the UMLS-Knowledge Source API We propose to extend the UMLS Knowledge Source API NOTEREF _Ref146378454 \h  \* MERGEFORMAT 13 to support functions for mining connections from the UMLS. We believe that this is the best approach to disseminate the results of our investigation to the community at large. We will use a batch upload process and job request queues to allow transferring training connections and executing algorithms. We anticipate a need for a significant computational backbone to support the mining algorithms for multiple users. We will implement and simulate the multi-user scenario using the computing clusters at the Columbia University as described in the Resources section. c) ConnectionViz To allow users to manually explore, view or select the connections, we will provide a ConnectionViz toolkit that will show the results of connection mining plotted as a configurable graph using the Jung toolkit. The visualizations can be configured to display graphs containing concepts from a given terminology or prune connections by their Semantic Types and so on. We believe a connection visualization utility will provide end users a useful tool to analyze and mine connections. 10. Milestones and Timeline Year 1 MilestonesYear 2 MilestonesDevelop training datasets and UMLSGraphGen (1st quarter) Implement and evaluate the relational feature-based algorithms (2nd quarter) Develop and evaluate the relSVM approach (2nd -3rd quarter) Develop and evaluate the SaFREL approach (4th quarter)continue SaFREL (1st quarter) Develop and evaluate the relBN approach (2nd quarter - 3rd quarter) Evaluate all approaches with CIF and implement ConnectionViz and UMLSKS API interfaces (4th quarter)Human Subjects None Vertebrate Animals None Literature Cited   . Lindberg DA, Humphreys BL, McCray AT. The Unified Medical Language System. Methods Inf Med. 1993 Aug;32(4):281-91. . Butte AJ, Kohane IS. Creation and implications of a phenome-genome network. Nat Biotechnol. 2006 Jan;24(1):55-62. . National Health Information Infrastructure, http://www.aspe.hhs.gov/sp/nhii/index.html, Accessed 12 September 2006. . 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Jung Universal Network/Graph Framework,  HYPERLINK "http://jung.sourceforge.net/" http://jung.sourceforge.net/, Accessed 15 September 2006.  Concept hubs Figure 1: Analyzing scale freeness of the UMLS a) The power law due to the 13,809,697 relationships between 1,179,179 UMLS concepts. b). A slice of 2,000 records from the MRREL file of the UMLS distribution depicting hub concepts and the gray scale indicating the number of source terminologies contributing the terms for the concept. Training Datasets SNOMED-ICD Dxplain:Lab-Disorder PubMed abstracts ... Relational Features Scale Free Abstraction (SaFREL) Probabilistic Graph (relBN) UMLS Metathesaurus UMLSGraphGen ConnectionViz Connection Mining UMLSKS API D3 D5 D6 D9a D9b D9c D1 Connection Ranking D7 Mining Results Evaluation D8 Relational Kernels (relSVM) D4 C1 C3 C4 C2 C1 C3 C2 UMLS UMLS HuG Median function M(c) = Hub Concept 2 1 1 2 C2 Second Level Refinement Number of hubs(k) = 3 Number of hubs(k) = 4 (w (h Hierarchical Relationships (PAR, CHD, RN, RB) Associative Relationships (SY, RQ, AQ, QB, SIB) Nodes from the training connections Semantic Traversal Learn Parameters Max Likelihood = argmax ( log p(TC|(cpt, (h, (w) Relational Features relBN SaFREL relSVM ( top k connection set Connection Intersection Function (CIF) Final ranked list of connection set  a b Figure 2: The overview of major components anP^ 򿭓uuuufh6CJOJQJ^JaJhCJH*OJQJ^JaJh2nCJH*OJQJ^JaJ2jh hCJH*OJQJU^JaJ#h h CJH*OJQJ^JaJ,jh h CJH*OJQJU^JaJ'jh0JCJOJQJU^JaJhCJaJhCJOJQJ^JaJ!P^_ ?\F & FEƀF. ` $a$N & FhLEƀF.^h`L  r& !   !"Խv\vI5'jh0JCJOJQJU^JaJ$h2nh2n0JCJOJQJ^JaJ2jh5'hCJH*OJQJU^JaJ#h5'h5'CJH*OJQJ^JaJ,jh5'h5'CJH*OJQJU^JaJhCJH*OJQJ^JaJh2nCJH*OJQJ^JaJ,jh h CJH*OJQJU^JaJ#h h CJH*OJQJ^JaJ2j}h hCJH*OJQJU^JaJ"  "#3467򹧍~o`oQoMhhDp6CJOJQJ^JaJhK6CJOJQJ^JaJh6CJOJQJ^JaJh2nCJH*OJQJ^JaJ2jwhhCJH*OJQJU^JaJ#hhCJH*OJQJ^JaJ,jhhCJH*OJQJU^JaJ'jh0JCJOJQJU^JaJh"CJOJQJ^JaJhCJOJQJ^JaJvqooF & FEƀF.F & FEƀF.|"%>&'ZF & FEƀF. ` S & F EƀF.^`|()O P !!!" 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