Accelerating discovery : mining unstructured information for hypothesis generation /: mining unstructured information for hypothesis generation. (2015)

• Record Type: Book
• Further Information: Note: Scott Spangler.
• Authors: Spangler, Scott
• Contents: Introduction Why Accelerate Discovery?; Scott Spangler and Ying Chen; THE PROBLEM OF SYNTHESIS; THE PROBLEM OF FORMULATION; WHAT WOULD DARWIN DO?; THE POTENTIAL FOR ACCELERATED DISCOVERY: USING COMPUTERS TO MAP THE KNOWLEDGE SPACE; WHY ACCELERATE DISCOVERY: THE BUSINESS PERSPECTIVE; COMPUTATIONAL TOOLS THAT ENABLE ACCELERATED DISCOVERY; ACCELERATED DISCOVERY FROM A SYSTEM PERSPECTIVE; ACCELERATED DISCOVERY FROM A DATA PERSPECTIVE; ACCELERATED DISCOVERY IN THE ORGANIZATION; CHALLENGE (AND OPPORTUNITY) OF ACCELERATED DISCOVERY Form and Function; THE PROCESS OF ACCELERATED DISCOVERY; CONCLUSION Exploring Content to Find Entities; SEARCHING FOR RELEVANT CONTENT; HOW MUCH DATA IS ENOUGH? WHAT IS TOO MUCH?; HOW COMPUTERS READ DOCUMENTS; EXTRACTING FEATURES; FEATURE SPACES: DOCUMENTS AS VECTORS; CLUSTERING; DOMAIN CONCEPT REFINEMENT ; MODELING APPROACHES; DICTIONARIES AND NORMALIZATION; COHESION AND DISTINCTNESS; SINGLE AND MULTIMEMBERSHIP TAXONOMIES; SUBCLASSING AREAS OF INTEREST; GENERATING NEW QUERIES TO FIND ADDITIONAL RELEVANT CONTENT; VALIDATION; SUMMARY Organization; DOMAIN-SPECIFIC ONTOLOGIES AND DICTIONARIES; SIMILARITY TREES; USING SIMILARITY TREES TO INTERACT WITH DOMAIN; EXPERTS; SCATTER-PLOT VISUALIZATIONS; USING SCATTER PLOTS TO FIND OVERLAPS BETWEEN NEARBY ENTITIES OF DIFFERENT TYPES; DISCOVERY THROUGH VISUALIZATION OF TYPE SPACE Relationships; WHAT DO RELATIONSHIPS LOOK LIKE?; HOW CAN WE DETECT RELATIONSHIPS?; REGULAR EXPRESSION PATTERNS FOR EXTRACTING; RELATIONSHIPS; NATURAL LANGUAGE PARSING; COMPLEX RELATIONSHIPS; EXAMPLE: P53 PHOSPHORYLATION EVENTS; PUTTING IT ALL TOGETHER; EXAMPLE: DRUG/TARGET/DISEASE RELATIONSHIP; NETWORKS; CONCLUSION Inference ; CO-OCCURRENCE TABLES; CO-OCCURRENCE NETWORKS; RELATIONSHIP SUMMARIZATION GRAPHS; HOMOGENEOUS RELATIONSHIP NETWORKS; HETEROGENEOUS RELATIONSHIP NETWORKS; NETWORK-BASED REASONING APPROACHES; GRAPH DIFFUSION; MATRIX FACTORIZATION; CONCLUSION Taxonomies; TAXONOMY GENERATION METHODS; SNIPPETS; TEXT CLUSTERING; TIME-BASED TAXONOMIES; KEYWORD TAXONOMIES; NUMERICAL VALUE TAXONOMIES; EMPLOYING TAXONOMIES Orthogonal Comparison; AFFINITY; COTABLE DIMENSIONS; COTABLE LAYOUT AND SORTING; FEATURE-BASED COTABLES; COTABLE APPLICATIONS; EXAMPLE: MICROBES AND THEIR PROPERTIES; ORTHOGONAL FILTERING; CONCLUSION Visualizing the Data Plane; ENTITY SIMILARITY NETWORKS; USING COLOR TO SPOT POTENTIAL NEW HYPOTHESES; VISUALIZATION OF CENTROIDS; EXAMPLE: THREE MICROBES; CONCLUSION Networks; PROTEIN NETWORKS; MULTIPLE SCLEROSIS AND IL7R; EXAMPLE: NEW DRUGS FOR OBESITY; CONCLUSION Examples and Problems; PROBLEM CATALOGUE; EXAMPLE CATALOGUE Problem: Discovery of Novel Properties of Known Entities; ANTIBIOTICS AND ANTI-INFLAMMATORIES; SOS PATHWAY FOR ESCHERICHIA COLI; CONCLUSIONS Problem: Finding New Treatments for Orphan Diseases from Existing Drugs; IC50:IC50 Example: Target Selection Based on Protein Network Analysis; TYPE 2 DIABETES PROTEIN ANALYSIS Example: Gene Expression Analysis for Alternative Indications; NCBI GEO DATA; CONCLUSION Example: Side Effects Example: Protein Viscosity Analysis Using Medline Abstracts; DISCOVERY OF ONTOLOGIES; USING ORTHOGONAL FILTERING TO DISCOVER IMPORTANT RELATIONSHIPS Example: Finding Microbes to Clean Up Oil Spills; ENTITIES; USING COTABLES TO FIND THE RIGHT COMBINATION OF FEATURES; DISCOVERING NEW SPECIES; ORGANISM RANKING STRATEGY; CHARACTERIZING ORGANISMS; CONCLUSION Example: Drug Repurposing; COMPOUND 1: A PDE5 INHIBITOR; PPARα/γ AGONIST Example: Adverse Events; FENOFIBRATE; PROCESS; CONCLUSION Example: Discovering New P53 Kinases; AN ACCELERATED DISCOVERY APPROACH BASED ON ENTITY SIMILARITY; RETROSPECTIVE STUDY; EXPERIMENTAL VALIDATION; CONCLUSION Conclusion and Future Work; ARCHITECTURE; FUTURE WORK; ASSIGNING CONFIDENCE AND PROBABILITIES TO ENTITIES, RELATIONSHIPS, AND INFERENCES; DEALING WITH CONTRADICTORY EVIDENCE; UNDERSTANDING INTENTIONALITY; ASSIGNING VALUE TO HYPOTHESES; TOOLS AND TECHNIQUES FOR AUTOMATING THE DISCOVERY PROCESS; CROWD SOURCING DOMAIN ONTOLOGY CURATION; FINAL WORDS References appear at the end of most chapters.
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2015
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 006.312; Data mining; Science -- Information resources; Science -- Methodology
• Languages: English
• ISBNs: 9781482239140
• Related ISBNs: 9781482239133
• Notes: Note: Description based on CIP data; item not viewed.
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