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![Problem: gene selection instability
instability: different methods, different datasets produce
different gene sets for the same phenotype [1]
[1] Griffith, Obi L., et al. "A robust prognostic signature for hormone-positive node-negative breast cancer.”
Genome Medicine 5.10 (2013).](https://image.slidesharecdn.com/20156bd2kbiobranchknowbio-150731143408-lva1-app6891/85/2015-6-bd2k_biobranch_knowbio-25-320.jpg)
![Eg: 21 Gene Signature from
OncoType Dx
Proliferation
Ki67
STK15
Survivin
CCNB1 (cyclin B1)
MYBL2
Invasion
MMP11
CTSL2
HER2
GRB7
HER2
Estrogen
ER
PGR
BCL2
SCUBE2
GSTM
1
Reference
ACTB(b-actin)
GAPDH
RPLPO
GUS
TFRC
Recurrence Score Algorithm
1. HER2 group score = 0.9 x GRB7+ 0.1 x HER2 (if the result is less than 8, then the GRB7 group
score is considered 8);
2. ER group score = (0.8x ER +1.2 x PGR + BCL2+ SCUBE2)÷4
3. Proliferation group score = ( Survivin + KI67 + MYBL2 + CCNB1 [the gene encoding cyclin B1]+
STK15 )÷5 (if the result is less than 6.5, then the proliferation group score is considered 6.5)
4. Invasion group score=( CTSL2 [the gene encoding cathepsin L2] + MMP11 [the gene encoding
stromolysin 3])÷2.
RSU=0.47* HER2- 0.34* ER +1.04*
PROLIFERATION + 0.10* INVASION +0.05* CD68 -
0.08* GSTM1 -0.07* BAG1
*A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer
CD68
BAG1](https://image.slidesharecdn.com/20156bd2kbiobranchknowbio-150731143408-lva1-app6891/85/2015-6-bd2k_biobranch_knowbio-42-320.jpg)
Uploaded byBenjamin Good
2015 6 bd2k_biobranch_knowbio
The document outlines three tools for capturing and sharing community knowledge regarding gene-related research: Gene Wiki, Knowledge.bio, and Biobranch. Gene Wiki integrates gene, disease, and drug data into Wikipedia and Wikidata, while Knowledge.bio provides a concept-centric platform for scientific literature to support hypothesis generation. Biobranch aims to facilitate the use of large datasets for predictive modeling in breast cancer survival outcomes through an intuitive interface for non-programmers.
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2015 6 bd2k_biobranch_knowbio
- 1. Three TSRI Toolsfor capturing, sharing, and applying community knowledge Benjamin Good The Scripps Research Institute @bgood
- 2. Outline • Gene wiki,quick recap, update • Introducing: – http://knowledge.bio – http://biobranch.org
- 3. Gene Wiki (onWikipedia) 3 Protein structure Symbols and identifiers Tissue expression pattern Gene Ontology annotations Links to structured databases Gene summary Protein interactions Linked references Huss, PLoS Biol, 2008
- 4.
- 5. A computable Gene(& Disease & Drug) Wiki 5 Structured data Here now Soon Downstream (but exciting potential..) ? ? ? Wikipedia(s)
- 6. Status Update • Genes,diseases (and any minute.. Drugs) are in wikidata • Demonstrations of incorporating this content in Wikipedia are functional • We’ve been slowed a little bit by wikidata governance policies.. (they blocked our bot temporarily)
- 7. Wikidata activities • YOUcan help! • https://www.wikidata.org/wiki/User:ProteinBoxBot Join in one of these discussions and voice your support
- 8. Outline • Gene wiki •knowledge.bio • biobranch.org
- 9. Knowledge.bio • Provides aconcept-centric view of the scientific literature. – You search and interact with concepts rather than documents. • Main purpose is hypothesis generation • 2 data sources mined from PubMed – 70 million Explicit semantic relations (‘triples’) – 200 million Implicit gene-disease associations
- 10.
- 11. Explicit relations view Searchfor concept View related concepts (67 results) Filter results
- 12. View text wheretriple was extracted
- 13. Diseases implicitly relatedto queried concept: CYP2R1
- 14. Concepts linking CYP2R1 toSmith-Lemli Opitz Syndrome
- 15. Table views complementedby a Network view for taking notes..
- 16. Network (“Map”) view Cytoscape.jscanvas Auto and manual layout Save Map as local text file Load saved map
- 17. Step 1: findcandidate relation What new diseases might be related to CYP2R1? Implicit prediction
- 18. Step 2: findlinking concepts How is CYP2R1 related to SLO syndrome?
- 19. Step 3: Startbuilding a hypothesis to explain the predicted relation Do CYP2R1 and DHCR7 participate in a process related to SLO syndrome? Explicit relations view
- 20. Warning, may proveaddictive..
- 21. Next steps forknowledge.bio • Enhanced community sharing • Integration with http://ndexbio.org from the Cytoscape consortium • Allow user actions to feedback into underlying NLP systems • Include access to other structured knowledge sources e.g. Gene Ontology
- 22. Outline • Gene wiki •knowledge.bio • biobranch.org
- 23. Breast cancer prognosis: 10year survival? find patterns Inferring class predictors No van't Veer, Laura J., et al. "Gene expression profiling predicts clinical outcome of breast cancer.” Nature 415.6871 (2002): 530-536. Yes make predictions on new samples No Yes 10 year survival?
- 24. find patterns make predictions inferringsurvival predictors 1) select genes 2) infer predictor from data (e.g. decision tree, SVM, etc.) Out of the 25,000+ genes, which small set works together the best? No Yes 10 year survival?
- 25. Problem: gene selectioninstability instability: different methods, different datasets produce different gene sets for the same phenotype [1] [1] Griffith, Obi L., et al. "A robust prognostic signature for hormone-positive node-negative breast cancer.” Genome Medicine 5.10 (2013).
- 26. Problem: the validationgap training data, test data validation validation: predictive signatures often perform worse on independent data created for validation. Photograph by Richard Hallman, National Geographic Adventure Blog
- 27. find patterns make predictions Addingprior knowledge to the discovery algorithm <10 yr survival >10 yr survival
- 28. Ex.) Network guidedforests Use protein interaction network to find good gene combinations Dutkowski & Ideker (2011) Protein Networks as Logic Functions in Development in Development and Cancer. PLoS Computational Biology
- 29. But most knowledgeis not structured 500000 550000 600000 650000 700000 750000 800000 850000 900000 950000 1000000 Number articles added to PubMed >100 publications/hour >194715 publications linked to “breast cancer” since 2000 http://tinyurl.com/brsince2000
- 30. How can weuse unstructured knowledge to improve predictors? Need a distributed network of intelligent systems that are good at reading and hypothesizing Like you and your friends
- 31. A game witha purpose: The Cure • http://genegames.org/cure • http://games.jmir.org/2014/2/e 7/ • The Cure: Design and Evaluation of a Crowdsourcing Game for Gene Selection for Breast Cancer Survival Prediction JMIR Serious Games PMID: 25654473
- 32. People wanted tocontrol the trees
- 33.
- 34. Branch Goals • Provideeasy, visual way for non-programmers to use large datasets to answer questions • Construct libraries of manually crafted predictive models • Use the collected models to generate ensemble predictors that incorporate the knowledge of the users
- 35. Branch walkthrough: Choose adataset
- 36.
- 37.
- 38. Split node builder Eachbutton is a different way to compose a split node in your decision tree
- 39. Split node Predictions at leafnodes 100% correct 56% accurate View data, adjust split point If age less than 34.5 Predict relapse If greater, Predict no relapse
- 40. Single feature splits Pickfrom genes or clinical features Type-ahead search Statistical ranker
- 41. Custom feature combination BRCA2 TOP2B BRCA2+ TOP2B Allows user to use a manually composed linear combination of other features
- 42. Eg: 21 GeneSignature from OncoType Dx Proliferation Ki67 STK15 Survivin CCNB1 (cyclin B1) MYBL2 Invasion MMP11 CTSL2 HER2 GRB7 HER2 Estrogen ER PGR BCL2 SCUBE2 GSTM 1 Reference ACTB(b-actin) GAPDH RPLPO GUS TFRC Recurrence Score Algorithm 1. HER2 group score = 0.9 x GRB7+ 0.1 x HER2 (if the result is less than 8, then the GRB7 group score is considered 8); 2. ER group score = (0.8x ER +1.2 x PGR + BCL2+ SCUBE2)÷4 3. Proliferation group score = ( Survivin + KI67 + MYBL2 + CCNB1 [the gene encoding cyclin B1]+ STK15 )÷5 (if the result is less than 6.5, then the proliferation group score is considered 6.5) 4. Invasion group score=( CTSL2 [the gene encoding cathepsin L2] + MMP11 [the gene encoding stromolysin 3])÷2. RSU=0.47* HER2- 0.34* ER +1.04* PROLIFERATION + 0.10* INVASION +0.05* CD68 - 0.08* GSTM1 -0.07* BAG1 *A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer CD68 BAG1
- 43. Classifier nodes Classifier Node Class B Class A …... …... …... …... …... …... …... …... …... …... …... …... Use atrained predictive model such as A Support Vector Machine as a node in your tree Use Build
- 44. biobranch tree nodes Branch decisiontree Class B Class A …... …... …... …... …... …... …... …... …... …... …... …... Use a previously constructed tree as node
- 45. Visually set decision boundarynodes Visual split Class B Class A …... …... …... …... …... …... …... …... …... …... …... …...
- 46. Creating a visualsplit Draw polygon Add to tree Select feature Select feature
- 47. Teach students aboutoverfitting..
- 48.
- 49. Evaluation panel View trainingand testing sets Performance metrics Confusion matrix ROC curve
- 50.
- 51. Tree Collection Open andedit shared tree Search trees you create and trees shared with the community
- 52. Editing shared tree Trackswhich user created each node
- 53. Next steps • Moreuser testing • More datasets • Lots of users? • Better models? training data, test data validation
- 54. Even more information! •Screencasts • http://tinyurl.com/branch-cast • Open source code – https://bitbucket.org/sulab/biobranch – https://bitbucket.org/starinformatics/gbk – https://bitbucket.org/sulab/wikidatabots
- 55. Thanks Funding and Support BioGPS:GM83924 Gene Wiki: GM089820 BD2K COE: GM114833 Andra Waagmeester Sebastian Burgstaller Elvira Mitraka Lynn Schriml Gang Fu Evan Bolton Paul Pavlidis Peter Robinson Many WikiDatans Richard Bruskiewich http://starinformatics.com Karthik Gangavarapu Vyshakh Babji Andrew Su The Prince of Crowdsourcing Implicitome Kristina Hettne, Leiden University Contact: bgood@scripps.edu @bgood
Editor's Notes
- #4 Active MCB community at WP had already developed ~650 gene articles Can we accelerate this process through stub creation? In total, created 9000 new articles and edited 650 previously existing articles.
- #24 a, Two-dimensional presentation of transcript ratios for 98 breast tumours. There were 4,968 significant genes across the group. Each row represents a tumour and each column a single gene. As shown in the colour bar, red indicates upregulation, green downregulation, black no change, and grey no data available. The yellow line marks the subdivision into two dominant tumour clusters. b, Selected clinical data for the 98 patients in a: BRCA1 germline mutation carrier (or sporadic patient), ER expression, tumour grade 3 (versus grade 1 and 2), lymphocytic infiltrate, angioinvasion, and metastasis status. White indicates positive, black negative and grey denotes tumours derived from BRCA1 germline carriers who were excluded from the metastasis evaluation. The cluster below the yellow line consists of 36 tumours, of which 34 are ER negative (total 39 ER-negative) and 16 are carriers of the BRCA1 mutation (total 18). c, Enlarged portion from a containing a group of genes that co-regulate with the ER- gene (ESR1). Each gene is labelled by its gene name or accession number from GenBank. Contig ESTs ending with RC are reverse-complementary of the named contig EST. d, Enlarged portion from a containing a group of co-regulated genes that are the molecular reflection of extensive lymphocytic infiltrate, and comprise a set of genes expressed in T and B cells. (Gene annotation as in c.)
- #25 a, Two-dimensional presentation of transcript ratios for 98 breast tumours. There were 4,968 significant genes across the group. Each row represents a tumour and each column a single gene. As shown in the colour bar, red indicates upregulation, green downregulation, black no change, and grey no data available. The yellow line marks the subdivision into two dominant tumour clusters. b, Selected clinical data for the 98 patients in a: BRCA1 germline mutation carrier (or sporadic patient), ER expression, tumour grade 3 (versus grade 1 and 2), lymphocytic infiltrate, angioinvasion, and metastasis status. White indicates positive, black negative and grey denotes tumours derived from BRCA1 germline carriers who were excluded from the metastasis evaluation. The cluster below the yellow line consists of 36 tumours, of which 34 are ER negative (total 39 ER-negative) and 16 are carriers of the BRCA1 mutation (total 18). c, Enlarged portion from a containing a group of genes that co-regulate with the ER- gene (ESR1). Each gene is labelled by its gene name or accession number from GenBank. Contig ESTs ending with RC are reverse-complementary of the named contig EST. d, Enlarged portion from a containing a group of co-regulated genes that are the molecular reflection of extensive lymphocytic infiltrate, and comprise a set of genes expressed in T and B cells. (Gene annotation as in c.)
- #26 Main reason statistically is inadequate sample size and correlated data structure. (Xu 2010). Makes it difficult to trust the predictors when different genes appear every time.
- #27 though progress is being made on this issue, e.g. Margolin showed very good agreement between cross-validation, test set, and validation performance for models submitted to Sage challenge.
- #28 a, Two-dimensional presentation of transcript ratios for 98 breast tumours. There were 4,968 significant genes across the group. Each row represents a tumour and each column a single gene. As shown in the colour bar, red indicates upregulation, green downregulation, black no change, and grey no data available. The yellow line marks the subdivision into two dominant tumour clusters. b, Selected clinical data for the 98 patients in a: BRCA1 germline mutation carrier (or sporadic patient), ER expression, tumour grade 3 (versus grade 1 and 2), lymphocytic infiltrate, angioinvasion, and metastasis status. White indicates positive, black negative and grey denotes tumours derived from BRCA1 germline carriers who were excluded from the metastasis evaluation. The cluster below the yellow line consists of 36 tumours, of which 34 are ER negative (total 39 ER-negative) and 16 are carriers of the BRCA1 mutation (total 18). c, Enlarged portion from a containing a group of genes that co-regulate with the ER- gene (ESR1). Each gene is labelled by its gene name or accession number from GenBank. Contig ESTs ending with RC are reverse-complementary of the named contig EST. d, Enlarged portion from a containing a group of co-regulated genes that are the molecular reflection of extensive lymphocytic infiltrate, and comprise a set of genes expressed in T and B cells. (Gene annotation as in c.)
- #50 A pathological hallmark of SLO is increased levels of 7-dihydrocholesterol [PMID: 8259166]. 7-dihydrocholesterol is a precursor of vitamin D3 and defects in CYP2R1 are known to affect vitamin D3 levels [PMID: 15128933]. Thus, LWAS implicates CYP2R1 in SLO syndrome since defects may potentially lead to 7-dihydrocholesterol accumulation.