Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Biomarkers brain regions

344 views

Published on

Learn how to use Pathway Studio to explore biomarkers and brain regions. With the addition of highly sophisticated visualization tools, users can interactively explore the vast number of connections created to help unravel disease biology. In addition, an innovative new taxonomy based on brain region identifications will be presented. Together, these innovations can be applied to rapidly increase the knowledge of diseases based on published findings.

Published in: Health & Medicine
  • Be the first to comment

  • Be the first to like this

Biomarkers brain regions

  1. 1. Biomarkers, Brain Regions, and Data Reproducibility Chris Cheadle PhD, Hongbao Cao PhD March 7, 2016
  2. 2. | 2 Outline of this discussion • Introduction to Pathway Studio® • CellEffect™ Module • Biomarker Identification • Literature Quality Metrics • Brain Regions
  3. 3. Introduction to Pathway Studio Helping researchers understand the biology of disease.
  4. 4. | 4 The volume of life scientific literature is exploding Rapidly approaching 1M new citations/year in Medline – HOW TO KEEP UP? One way is to automatically extract relevant information from scientific publications on a massive scale Using Elsevier’s proprietary MedScan® NLP technology
  5. 5. | 5 How does it all work? Natural language processing (NLP) • syntactic and semantic analysis of text • synthesize a structured representation. Essential facts are extracted • predefined fact types • information triplets (subject–verb–object). Domain ontologies identify types, properties, and interrelationships of relevant entities in the biomedical literature.
  6. 6. | 6 Where does it all come from? 25M+ abstracts from Medline® and 10,000 journal titles covered 4M+ full text journal articles from Elsevier and other leading publishers 6.1M+ unique relations (biological facts) supported by 35M+ references (articles) Big Data Updated weekly
  7. 7. | 7 Information Extraction (IE) Vs Information Retrieval
  8. 8. | 8 • What is done by IE?  Take a natural language text from a document source, and extract essential facts about one or more predefined fact types  Represent each fact with a template whose slots are filled on the basis of what is found from the text Information Extraction (IE)
  9. 9. | 9 Pathway Studio NLP: Entities and Relations
  10. 10. | 10 Pathway Studio: networks and pathways
  11. 11. | 11 Proteins/genes associated with Schizophrenia: relation table view
  12. 12. | 12 Drill down
  13. 13. | 13 Pathway Studio databases grow and evolve: domain addition Mammal protein-centered Proteins + • diseases • clinical parameters • small molecules • cell processes • treatments ChemEffect drug-centered +Drugs DiseaseFX biomarker-centered +Biomarker facts • genetic change • state change • quantitative change CellEffect cell-centered +Cell facts Cells
  14. 14. CellEffect™ and Biomarker Identification
  15. 15. | 15 Have you seen this cell? full name nickname aka formerly known as scars and marks for short
  16. 16. | 16 Defining cell types: from inconsistent names to standard names Epitope Basic cell“Attribute” CD4+ CD25+ regulatory T cell T-lymphocyte leukocyte T-cell leucocyte hemopoetic hemopoietic haemopoetic haemopoietic hematopoetic hematopoietic haematopoetic haematopoietic regulatory immunoregulatory CD4+CD25+ CD25+FOXP3+ CD4+ CD25+ FOXP3+
  17. 17. | 17 Adding cell processes to the mixture proliferation of death of migration of human polarization cytotoxicity quantity Standard cell name Allows Pathway Studio to: • Have more specific information about cells and associated cell processes in the database • Assign specific cell processes to rare cell types
  18. 18. | 18 Recognizing cell processes in text • Information about more specific cell types • Doubles the number of cell processes compared to Gene Ontology + EmTree
  19. 19. | 19 Biomarker Identification
  20. 20. | 20 Biomarker Identification
  21. 21. | 21 The common denominator: biomarker candidates for behavior are present across multiple psychiatric disorders
  22. 22. | 22 Not all relations are equal! 0 50 100 150 200 250 0 500 1000 1500 2000 Reference# Relation # Majority of Biomarker Relations are supported by a single reference Anxiety Disorders Depressive Disorder, Major Bipolar Disorder Schizophrenia 1 10 100 1000 Biomarker Biomarker GeneticChange GeneticChange GeneticChange GeneticChange GeneticChange GeneticChange GeneticChange GeneticChange GeneticChange GeneticChange QuantitativeChange QuantitativeChange QuantitativeChange QuantitativeChange QuantitativeChange QuantitativeChange QuantitativeChange QuantitativeChange StateChange Reference# Behavioral gene relations: reference support Relation Type A large percentage (83.3±9.3%) of the relations reported in Pathway Studio have only one reference; and most of the relations (94.7±5.7%) have 1~3 references.
  23. 23. Three weights and three scores 23 Three weights for each article: Quality weight (QW), evaluated by publication age Citation weight (CW), evaluated by citation number Novelty weight (NW) , specifies the novelty Note: All weights use [0,1] scale Three scores for each relation: Quality Score (QScore), related to #reference, CW, and QW Novelty Score (NScore), related to #reference, CW , and NW Citation Score (CScore), related to #reference and #citations Note: The scores of a relation are defined by the weights of the associated articles When relations are supported by 1-3 references, how to evaluate (filter) for the most reliable observations without losing valuable information, for example novelty etc.
  24. 24. Novelty weight (NW) 24 NW with different NoveltyAge
  25. 25. | 25 Novelty score of a relation Balances both the number of references and how often they have been cited, restricted to those relations with references appearing only in the last (n)years. Table1 Top novel biomarker candidates for Schizophrenia by NScore (NoveltyAge = 2) NScore: the novelty of a literature-search-based relation CScore: the total citation number of the supporting references 𝑁𝑆𝑐𝑜𝑟𝑒 = (𝐶𝑊𝑖 + 𝑁𝑊𝑖)𝑛 𝑖=1 ∗ 𝑁𝑊𝑖 𝑛 𝑖=1 𝐶𝑆𝑐𝑜𝑟𝑒 = 𝑁𝑐𝑖𝑡𝑒 𝑖 𝑛 𝑖=1 Entity Entity Type NScore Reference # citNum CScore PubYear PMID MIR212 Disease -> Protein 3.811 2 12, 5 17 2014, 2015 24694668;25487174 CAMKK2 Disease -> Protein 3.697 2 8, 3 11 2014, 2015 23958956;25497042 STXBP1 Disease -> Protein 2.743 2 0, 2 2 2014, 2015 25069615;25662103 MIAT Disease -> Protein 1.973 1 38 38 2014 23628989;23628989 MIR181B1 Disease -> Protein 1.914 1 12 12 2014 24694668 HIVEP2 Disease -> Protein 1.870 1 8 8 2014 24525328 MIR26A1 Disease -> Protein 1.793 1 5 5 2014 24416161 FKBP5 Disease -> Protein 1.743 1 2 2 2015 25459892 Oligodendroglia Disease -> Cell 1.667 1 3 3 2014 25173695 r_LOC102547241 Disease -> Protein 1.541 1 1 1 2015 25667193 RAB3A Disease -> Functional Class 1.325 1 1 1 2014 25063582
  26. 26. Brain regions- Searching for imaging biomarkers using Pathway Studio
  27. 27. | 27 Data Source- Image papers
  28. 28. | 28 Brain regions are identified using neuroanatomical labels for locations in 3D space, Automated Anatomical Labeling (AAL) AAL is a software package and digital atlas of the human brain typically used in functional neuroimaging-based research. Amygdala Pallidum Angular gyrus Paracentral lobule Anterior cingulate cortex Parahippocampal gyrus Calcarine fissure Postcentral gyrus Caudate nucleus Posterior cingulate cortex Cerebelum Precentral gyrus Cuneus Precuneus Fusiform gyrus Putamen Gyrus rectus Rolandic operculum Heschl gyrus Superior frontal gyrus, dorsolateral Hippocampus Superior frontal gyrus, medial Inferior frontal gyrus, opercular Superior frontal gyrus, medial orbital Inferior frontal gyrus, orbital Superior frontal gyrus, orbital Inferior frontal gyrus, triangular Superior occipital gyrus Inferior occipital gyrus Superior parietal lobule Inferior parietal lobule Superior temporal gyrus Inferior temporal gyrus Supplementary motor area Lingual gyrus Supramarginal gyrus Median cingulate cortex Temporal pole: middle Middle frontal gyrus Temporal pole: superior Middle frontal gyrus, orbital Thalamus Middle occipital gyrus Vermis Middle temporal gyrus Insulary cortex Olfactory cortex
  29. 29. | 29 Network connections between ‘smoking’ and the brain region ‘Precentral gyrus’. Hundreds of individual literature references underlie the many relations presented here.
  30. 30. | 30 Connecting the dots: from SNPs to Disease
  31. 31. | 31 Enabling neuroscientists to better access high quality data related to brain regions and imaging biomarkers Estimates of as many as 150,000 new relations using 350- 400 unique brain regions will be added to the complete complement of the Pathway Studio mammalian database along with ChemEffect, DiseaseFx, and CellFx (in addition to the 6M+ relations there already). • Will be available as a customized Enterprise web- based solution stored on the Amazon cloud • Pilot tested at a discounted rate in the neuroimaging community (starting with the NIMH) • Starting point for the development of a new dedicated Neuroscience PS module including, for example, taxonomies for the recently defined NIMH RDoC biotypes (biomarker-based categories).
  32. 32. | 32 • Massive amounts of literature-based, biologically relevant information available in Pathway Studio • Over 1800 manually curated pathways • Highly interactive, dynamic user interface for de novo pathway construction • Manually curated domain ontologies identify defined categories of information (e.g. Biomarkers) • Coming soon – Literature Quality Metrics, Brain Region Database Discussion Summary
  33. 33. Thank you for your attention! c.cheadle@elsevier.com elsevier.com/solutions/pathway- studio-biological-research

×