Link Analysis of Life Sciences Linked Data

Link Analysis of Life Sciences Linked Data

• 1.
  Link Analysis ofLife Science Linked Data 1 Wei Hu1, Honglei Qiu1, and Michel Dumontier2 1State Key Laboratory for Novel Software Technology, Nanjing University, China 2Center for Biomedical Informatics Research, Stanford University @micheldumontier::ISWC 2015
• 2.
  Linked Data offerslinks between datasets, but they are often incomplete and may contain errors. @micheldumontier::ISWC 20152
• 3.
  Network Analysis • Networkanalysis has long been used to study link structures – The structure of the Web – Network medicine: cellular networks and implications @micheldumontier::ISWC 20153 Power law is scale free A graph demonstrates the small world phenomenon, if its clustering coefficient is significantly higher than that of a random graph on the same node set, and if the graph has a shorter average distance. BTC2010 The clustering coefficient quantifies how close its neighbors are to be a clique. The average distance is the average shortest path length between all nodes in the graph.
• 4.
  Dataset link analysis (usingRDF data model) Entity link analysis (using cross-references) Term link analysis (using ontology matching) @micheldumontier::ISWC 20154
• 5.
  @micheldumontier::ISWC 2015 Linked Datafor the Life Sciences 5 Bio2RDF is an open source project to unify the representation and interlinking of biological data using RDF. chemicals/drugs/formulations, genomes/genes/proteins, domains Interactions, complexes & pathways animal models and phenotypes Disease, genetic markers, treatments Terminologies & publications • Release 3 (June 2014) • 35 datasets • 11B RDF triples • 1B entities • 2K classes • 4K properties
• 6.
  Dataset Links @micheldumontier::ISWC 20156 NetworkProperties 1. Well linked 2. Hubs and authorities 3. small-world phenomenon Average distance = 2.77 vs 6 Clustering coefficient = 0.22 vs 0.13 4. robust on systematic removal of nodes
• 7.
  Entity Link Analysis Howwell do entities link to each other? • 76% entity links involve a special kind of RDF triples – e.g. <kegg:D03455, kegg:x-drugbank, drugbank:DB00002> – x-relations have under-specified semantics • May be truly identical, may refer to another related entity … • Degree distribution – Some do not follow power law • Exponent is too large (close to 5) 7 BTC2010 @micheldumontier::ISWC 2015
• 8.
  symmetry of entitylinks varies between different pairs of datasets • Over 99% of links are reciprocated in DrugBank-PharmGKB and OMIM-HGNC – Suggests link sharing and synchronization • Only 58% of links in DrugBank-KEGG and 51% of OMIM-Orphanet links are reciprocal – Suggests incomplete mapping • 28% of OMIM-Orphanet links are malposed – Suggests variation in model (omim:Phenotype to orphanet:Disorder) 8 @micheldumontier::ISWC 2015
• 9.
  Transitivity Analysis: Find mismatchesand discover new links @micheldumontier::ISWC 20159
• 10.
  Evaluation of EntityMatching How accurate are current entity matching approaches? • Built a benchmark from the reciprocal links between similarly-typed entities • Evaluated several entity matching approaches – Label similarity: Levenstein, Jaro-Winkler, N-gram, Jaccard – Machine learning: Linear regression, logistic regression with 5 properties • Many-to-one links are difficult to be discovered 10 @micheldumontier::ISWC 2015
• 11.
  Term Link Analysis Howsimilar are the topics in the data network? • Use ontology matching to generate term link graph – Falcon-AO (linguistic matchers + structural matcher + synonyms) • Created 83K class mappings, 1.5K object property mappings, and 858 data property mappings – Similarity threshold = 0.9 – Top-5 popular labels for classes and properties • Significant overlap in topics, does not follow power law as in broader SW 11 @micheldumontier::ISWC 2015
• 12.
  Correlation of LinkGraphs To what degree are each of the three link graphs are correlated? • Spearman’s rank correlation coefficient: – Entity link graph  dataset pairs: entity links / entities – Term link graph  dataset pairs: term mappings / terms – Dataset link graph  dataset pairs: shortest path length • All positively correlated – Closer datasets in distance have more linked entities and terms – Number of linked entities contributes little to overlap of topics 12 @micheldumontier::ISWC 2015
• 13.
  Summary of Findings •Dataset, entity and term link graphs do not necessarily share the same characteristics with the Hypertext / Semantic Web – Degree distribution of entity links does not follow power law – Data hubs • A significant number of entities have been linked using x-relations, but their intended semantics differs – Classes are identical or equivalent  entity links represent logical equivalence • Symmetric and transitive entity links do exist, but their utility is weakened due to their small number – Meanings of entity links may shift during transitive closure • Only matching the labels of entities may fail, while combining different properties and using simple learning algorithms achieve good accuracy 13 @micheldumontier::ISWC 2015
• 14.
  dumontierlab.com [email protected] Website: http://dumontierlab.com Presentations: http://slideshare.com/micheldumontier 14@micheldumontier::ISWC 2015