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dkNET Webinar: Illuminating The Druggable Genome With Pharos 10/23/2020

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dkNET Webinar: Illuminating The Druggable Genome With Pharos 10/23/2020

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Abstract
Pharos (https://pharos.nih.gov/) is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied “dark” targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. In this webinar, Dr. Tudor Oprea will introduce how to use Pharos to find targets of interest for drug discovery.

The top 3 key questions that Pharos can answer:
1. What are the novel drug targets that may play a role in a specific disease?
2. What are the diseases that are related directly or indirectly to a drug target?
3. Find researchers that are related directly or indirectly to a drug target.

Presenter: Tudor Oprea, MD, PhD, Professor of Medicine, Chief of Translational Informatics Division & Internal Medicine, University of New Mexico

dkNET Webinar Information: https://dknet.org/about/webinar

Abstract
Pharos (https://pharos.nih.gov/) is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied “dark” targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. In this webinar, Dr. Tudor Oprea will introduce how to use Pharos to find targets of interest for drug discovery.

The top 3 key questions that Pharos can answer:
1. What are the novel drug targets that may play a role in a specific disease?
2. What are the diseases that are related directly or indirectly to a drug target?
3. Find researchers that are related directly or indirectly to a drug target.

Presenter: Tudor Oprea, MD, PhD, Professor of Medicine, Chief of Translational Informatics Division & Internal Medicine, University of New Mexico

dkNET Webinar Information: https://dknet.org/about/webinar

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dkNET Webinar: Illuminating The Druggable Genome With Pharos 10/23/2020

  1. 1. Tudor I. Oprea University of New Mexico, Albuquerque NM 10/23/2020 dkNET: Connecting Researchers to Resources Via Zoom Funding: NIH U24 CA224370 & NIH U24 TR002278 http://druggablegenome.net/ http://datascience.unm.edu/
  2. 2. 75% of protein research still focused on 10% genes known before human genome was mapped AM Edwards et al, Nature, 2011 This prompted NIH to start the Illuminating the Druggable Genome Initiative
  3. 3.  Informatics, Data Science and Machine Learning (“AI”) can be used as follows:  Diseases: EMR processing, nosology, ontology, & EMR-based ML  Targets: drug target selection & validation, phenotype associations, ML  Drugs: Identifying novel therapeutic modalities using in silico methods  IDG is developing methods applicable to each of these 3 areas 8/24/20 revision Diseases image credit: Julie McMurry, Melissa Haendel (OHSU). All other images credit: Nature Reviews Drug Discovery cover page
  4. 4. 2/4/20 revisionR. Santos et al., Nature Rev.Drug Discov. 2017, 16:19-34 link We curated 667 human genome-derived proteins and 226 pathogen-derived biomolecules through which 1,578 US FDA- approved drugs act. This set included 1004 orally formulated drugs as well as 530 injectable drugs (approved through June 2016). Data captured in DrugCentral (link)
  5. 5. 2/4/20 revision RFA-RM-16-026 (DRGC) GPCRs U24 DK116195: Bryan Roth, M.D., Ph.D. (UNC) Brian Shoichet, Ph.D. (UCSF) Ion Channels U24 DK116214: Lily Jan, Ph.D. (UCSF) Michael T. McManus, Ph.D. (UCSF) Kinases U24 DK116204: Gary L. Johnson, Ph.D. (UNC) RFA-RM-16-025 (RDOC) Outreach U24 TR002278: Stephan C. Schürer, Ph.D. (UMiami) Tudor Oprea, M.D., Ph.D. (UNM) Larry A. Sklar, Ph.D. (UNM) RFA-RM-16-024 (KMC) Data U24 CA224260: Avi Ma’ayan, Ph.D. (ISMMS) U24 CA224370: Tudor Oprea, M.D., Ph.D. (UNM) RFA-RM-18-011 (CEIT) Tools U01 CA239106: N Kannan, PhD & KJ Kochut (UGA) U01 CA239108: PN Robinson, MD PhD (JAX), CJ Mungall (LBL), T Oprea (UNM) U01 CA239069: G Wu, PhD (OHSU), PG D’Eustachio PhD (NYU), Lincoln D Stein, PhD (OICR) T. Oprea et al., Nature Rev.Drug Discov. 2018, 17:317-332 link
  6. 6.  Most protein classification schemes are based on structural and functional criteria.  For therapeutic development, it is useful to understand how much and what types of data are available for a given protein, thereby highlighting well-studied and understudied targets.  Tclin: Proteins annotated as drug targets  Tchem: Proteins for which potent small molecules are known  Tbio: Proteins for which biology is better understood  Tdark: These proteins lack antibodies, publications or Gene RIFs T. Oprea et al., Nature Rev.Drug Discov. 2018, 17:317-332 link 2/10/20 revision 2020 Update: Tdark 31.2%;Tbio 57.7%;Tchem 8%;Tclin 3.1%
  7. 7. 4/25/19 revisionT. Oprea, Mammalian Genome, 2019, 30:192-200 https://bit.ly/2NUK0BK Further information Email: idg.rdoc@gmail.com Follow: @DruggableGenome URLs: https://druggablegenome.net/ https://commonfund.nih.gov/idg/ IDG Knowledge User-Interface Email: pharos@mail.nih.gov Follow: @IDG_ Pharos URL: https://pharos.nih.gov/
  8. 8. 2/4/20 revisionMathias SL et al., IDG F2F Poster 2019
  9. 9.  Tclin proteins are associated with drug Mechanism of Action (MoA) – NRDD 2017  Tchem proteins have bioactivitis in ChEMBL and DrugCentral, + human curation for some targets  Kinases: <= 30nM  GPCRs: <= 100nM  Nuclear Receptors: <= 100nM  Ion Channels: <= 10μM  Non-IDG Family Targets: <= 1μM 10/19/16 revision Bioactivities of approved drugs (by Target class) ChEMBL: database of bioactive chemicals https://www.ebi.ac.uk/chembl/ DrugCentral: online drug compendium http://drugcentral.org/ R. Santos et al., Nature Rev.Drug Discov. 2017, 16:19-34 link
  10. 10.  Tbio proteins lack small molecule annotation cf.Tchem criteria, and satisfy one of these criteria:  protein is above the cutoff criteria for Tdark  protein is annotated with a GO Molecular Function or Biological Process leaf term(s) with an Experimental Evidence code  protein has confirmed OMIM phenotype(s)  Tdark (“ignorome”) have little information available, and satisfy these criteria:  PubMed text-mining score from Jensen Lab < 5  <= 3 Gene RIFs  <= 50 Antibodies available according to antibodypedia.com 8/20/15 revisionT. Oprea et al., Nature Rev.Drug Discov. 2018, 17:317-332 link
  11. 11. Tdark parameters differ from the other TDLs across the 4 external metrics cf.Kruskal-Wallis post-hoc pairwise Dunn tests 2/23/18 revisionT. Oprea et al., Nature Rev.Drug Discov. 2018, 17:317-332 link
  12. 12. https://rpubs.com/ cbologa/TDL7 Tdark: 9199 proteins in 2013 7658 proteins in 2016 6368 proteins in 2020 Tclin: 601 proteins in 2013 592 proteins in 2016 659 proteins in 2020 10/12/20 revisionT. Sheils, S.L. Mathias et al., Nucleic Acids Research 2021 doi:10.1093/nar/gkaa993
  13. 13. T. Sheils, S.L. Mathias et al., Nucleic Acids Research 2021 doi:10.1093/nar/gkaa993 10/12/20 revision
  14. 14. 2/4/20 revisionHaendel M, et al. Nature Rev.Drug Discov. 2020 19:77-78 link  We revised the number of RDs from ~7,000 to 10,393 using Disease Ontology, OrphaNet, GARD, NCIT, OMIM and the Monarch Initiative MONDO system  We also pointed out the lack of a uniform definition for rare diseases, and called for coordinated efforts to precisely define them  We surveyed therapeutic modalities available to translate advances in the scientific understanding of rare diseases into therapies, and discussed overarching issues in drug development for rare diseases.
  15. 15. Tambuyzer E, et al. Nature Rev.Drug Discov. 2020 19:93-111 link 2/4/20 revision
  16. 16.  6077 human proteins are associated with at least one Rare Disease.  Sources: Disease Ontology (RD-slim), eRAM and OrphaNet  ~50% agreement (gene level)  Contrast:Tclin at 3% & Tchem at 7% overall vs. RD subset: 6.94% Tclin and 14.1% for Tchem.  20% of the RD proteome is Tclin & Tchem. This means hope for cures.  Potentially significant opportunities for target & drug repurposing. 2/4/20 revisionTambuyzer E, et al. Nature Rev.Drug Discov. 2020 19:93-111 link
  17. 17. 3/12/18 revision ~35% of the proteins remain poorly described (Tdark) ~11% of the Proteome (Tclin & Tchem) are currently targeted by small molecule probes With help from rare disease patient advocacy groups, rare disease research is likely to witness a significant increase in translation
  18. 18. IN GOD WE TRUST. All others bring Data. Quote attributed to W. Edwards Deming, controversial: Other attributions: George A. Box and Robert W. Hayden. Bernhard Fisher, MD has said this to a journalist
  19. 19. https://pharos.nih.gov/targets/KCNJ11 The IDG KMC tracks 11 information channels for protein-disease associations, accessible via the Pharos portal. Our challenge is to harmonize disease concepts, and to enable computational use: e.g., KCNJ11 with ABCC8 form the Sulfonylurea 1 Kir6.2 receptor, MoA drug target for glibenclamide (type 2 diabetes). 10/23/20 revision The challenge for ML & AI: How to prioritize targets? i.e., which protein- disease associations are clinically actionable? (involved is not the same as committed)
  20. 20. Sorin Avram et al., Nucl Acids Res, database issue, 2021, doi: 10.1093/nar/gkaa997 10/23/20 revision
  21. 21. 10/23/20 revisionhttp://drugcentral.org/drugcard/1679
  22. 22. 9/09/20 revisionG. KC, G Bocci et al., Nature Machine Intell 2020, submitted link We used data from the NCATS COVID19 portal to develop a suite of ML models for six assays related to SARS-CoV-2 activities: • viral entry (Spike/ACE2 via AlphaLISA; counterscrens TruHit & ACE2 inhibition) • viral replication (3CL or Mpro) • live virus infectivity (CPE & cytotoxicity) REDIAL-2020 prediction workflow Input: SMILES Drug Name PubChem CID ML: Fingerprints Pharmacophores Phys-chem based on: RDKit scikit-learn External set predictions a) CPE, 24 actives; b) CPE, 14 actives; c) 3CL, 6 actives. http://drugcentral.org/Redial
  23. 23. 9/09/20 revisionG. KC, G Bocci et al., Nature Machine Intell 2020, submitted link http://drugcentral.org/Redial
  24. 24.  IDG KMC2 seeks knowledge gaps across the five branches of the “knowledge tree”:  Genotype; Phenotype; Interactions & Pathways; Structure & Function; and Expression, respectively.  We can use biological systems network modeling to infer novel relationships based on available evidence, and infer new “function” and “role in disease” data based on other layers of evidence  Primary focus on Tdark & Tbio O. Ursu,T Oprea et al., IDG2 KMC 2/01/18 revision
  25. 25. O. Ursu et al., manuscript in preparation Data source Data type Data points CCLE Gene expression 19,006,134 GTEx Gene expression 2,612,227 Protein Atlas Gene & Protein expression 949,199 Reactome Biological pathways 303,681 KEGG Biological pathways 27,683 StringDB Protein-Protein interactions 5,080,023 Gene ontology Biological pathways & Gene function 434,317 InterPro Protein structure and function 467,163 ClinVar Human Gene - Disease/Phenotype associations 881,357 GWAS Gene - Disease/Phenotype associations 54,360 OMIM Human Gene - Disease/Phenotype associations 25,557 UniProt Disease Human Gene - Disease/Phenotype associations 5,365 JensenLab DISEASE Gene - Disease associations from text mining 44,829 NCBI Homology Homology mapping of human/mouse/rat genes 70,922 IMPC Mouse Gene - Phenotype associations 2,153,999 RGD Rat Gene - Phenotype associations 117,606 LINCS Drug induced gene signatures 230,111,315 We developed automated methods for data collection (TCRD), visualization (Pharos) and data aggregation. These aggregated datasets were used to build machine learning models for 20+ disease and 73 mouse phenotype. Each knowledge graph contains ~22,000 metapaths and 284 million path instances. 10/07/18 revision
  26. 26.  a meta-path is a path consisting of a sequence of relations defined between different object types (i.e., structural paths at the meta level)  Our metapaths encode type- specific network topology between the source node (e.g., Protein) and the destination node (e.g., Disease).  This approach enables the trans- formation of assertions/evidence chains of heterogeneous biological data types into a ML ready format. G. Fu et al., BMC Bioinformatics 2016, 17:160 is an early example for drug-target interactions 10/01/18 revision Similar assertions or evidence form metapaths (white). Instances of metapath (paths) are used to determine the strength of the evidence linking a gene to disease/phenotype/function.
  27. 27. one protein-disease association at the time O. Ursu,T Oprea et al., IDG2 KMC 2/01/18 revision Genes associated with a disease/phenotype are positive examples, whereas genes lacking the same association are negative examples. The Metapath approach transforms assertions/evidence chains into classification problems that can be solved using suitably designed machine learning algorithms.
  28. 28. All datasets are merged, via R scripts, into a PostgreSQL. Python under development. Graph embedding transforms evidence paths into vectors, converting data into matrices. Input genes are positive labels. OMIM (not input) are negative labels (we prefer true negatives where possible). XGBoost runs 100 models.The “median model” (AUC, F1) is then selected for analysis and prediction to avoid overfitting. 10/15/19 revisionJ.J.Yang, P. Kumar, D. Byrd et al., IDG2 KMC
  29. 29. A soccer match at RoboCup, Nagoya 2017 Image searching for “Bad AI”
  30. 30. Build data matrix from “Alzheimer’s disease” in TCRD subset  protein knowledge graph along metapaths:  Protein – Protein Interactions  Pathways  GO terms  Gene expression  ...  Training set: 53 genes associated with Alzheimer’s disease (positives); 3,952 genes associated with other pathologies from OMIM were assumed to be negative  Test set: 23 genes associated with Alzheimer's (positives) and 200 genes not associated with Alzheimer's (negatives)  from Text Mining  “Complete forest” binary classifier using XGBoost & 5-fold cross-validation.  Weighted model is better than balanced model 2/14/18 revisionML work by Oleg Ursu Bal. Predicted Actual Pos Neg Pos 16 7 Neg 94 106 Wtd Predicted Actual Pos Neg Pos 20 3 Neg 41 159
  31. 31.  The top most important features are interactions with proteins mediating inflammatory processes (JAK2/Tclin, IL10 & IL2 / Tchem), response to oxidative stress (GSTP1/Tchem), nervous system development (BDNF/Tbio) and glycolysis (GAPDH/Tchem).  LINCS drug-induced gene expression perturbations are the largest category of features for these predictions.  Brain cortex expression is a necessary requirement.  One Reactome pathway (AU-rich mRNA elements binding proteins) is also important.  Weighted approached showed better performance in the test set for Alzheimer's Disease, Schizophrenia, and Dilated Cardiomyopathy. 4/23/18 revisionML work by Oleg Ursu
  32. 32.  We tested the top 20 genes identified by PKG/m-p/ML with a high- throughput validation system by measuring AD-relevant hyperphosphorylated (at S199/S202/T205) tau protein (AT8-Tau and AT180-Tau) using a Cellomics® high-content microscope; as well as gene expression and immunochemistry analysis via human AD induced pluripotent stem cells and human AD brain tissue 8/24/20 revisionAD validation work by Jessica Binder & Kiran Bhaskar,funded by U24CA224370-S2 supplement
  33. 33. 2/14/19 revisionAD validation work by Jessica Binder & Kiran Bhaskar,funded by U24CA224370-S2 supplement SHSY5Y’s in vitro siRNA knock-downs measuring ∆pTau (AT8) levels – unbiased cellomics qPCR gene expression Human induced pluripotent stem cells derived into neurons –AD vs Ctrl A K N A B C O 2 C C N Y C R T A M F A M 92B F O X P 4 F R R S 1 G R IN 2C IL 17R E L L IL R A 3 L M 04 N D R G 2 P IB F 1 R A B 40AS C G B 3A 1S L C 44A 2 S P O P S T A R D 3 T M E F F 2T X N D C 12 0 1 2 2.5 5.0 7.5 FoldChange(2^-∆∆Ct) RelativetoCtrl AX0018 sAD2.1 * **** ** ** ** **** ** * ** * **** **** **** **** **** **** *
  34. 34. A K N A B C O 2 C C N Y C R TA M FA M 92B FO XP4 FR R S1 G R IN 2C IL17R EL LILR A 3 LM 04 N D R G 2 PIB F1 R A B 40A SC G B 3A 1 SLC 44A 2 SPO P STA R D 3 TM EFF2 TXN D C 12 0.0 0.5 1.0 1.5 2.0 2.5 8 16 24 FoldChange(2^-∆∆Ct) RelativetoCtrl Control AD1 AD2 AD3 *** ** * & ** ** & ** & & ** * * * ** &# * ** # ** * * ** # # & & & & & & # # & & # # ** ** & # # ** ** # = p*** & = p**** 2/14/19 revisionAD validation work by Jessica Binder & Kiran Bhaskar,funded by U24CA224370-S2 supplement qPCR gene expression Human brain tissue 3 different AD patients vs 3 ctrl patients
  35. 35. 5/22/19 revisionAD validation work by Jessica Binder & Kiran Bhaskar,funded by U24CA224370-S2 supplement Top 20 Genes predicted by the XGBoost/Metapath model, clustered by functional roles
  36. 36. 8/24/20 revisionAD validation work by Jessica Binder & Kiran Bhaskar,funded by U24CA224370-S2 supplement We proposed to validation ML models for the top 20 genes: AKNA, BC02, CCNY,CRTAM, FAM92B, FOXP4, FRRS1, GRIN2C,1L17REL, LILRA3, LM04, NDRG2, PIBF1, RAB40A, SCGB3A1, SLC44A2, SPOP, STARD3,TMEFF2,TXNDC12 The most obvious effects based on the combined Cellomics & qPCR of iPSNs & autopsy brains suggests that AKNA, LILRA3, PIBF1 and TXNDC12 significantly increased pTau (as tracked by two different antibodies for T180, S202 and S205)  PIBF1, LILRA3 and CRTAM show the most significant effect on tau phosphorylation; two (CRTAM and LILRA3) novel genes are implicated in innate immune pathways
  37. 37. ML work by Tudor Oprea Genes 51 Source https://omim.org/entry/125853 AUC 0.72±0.02 1/16/19 revision First model: 51 OMIM genes associated with T2D vs. 3,954 OMIM genes associated with other pathologies. AUC = 0.72 ± 0.08. VIP-ranked variables include HFE & HMOX1, which relate to hemochromatosis (80% leads to T2D), and IL1B & IL10 (suggests an immune component).
  38. 38. From: Mark McCarthy <mark.mccarthy@drl.ox.ac.uk> Sent: Friday, December 7, 2018 11:10 AM The general summary is that we don’t see any enrichment for T2D associations in either exome or GWAS data from the predicted gene sets (however we slice them up). But having that we don’t really see anything in the TRAINING set either: No association in the exomes, and a weak (just nominal) association in the GWAS data. To be honest, I think, now we’ve taken a look at it, we’d all question the training set: I had missed that this came from OMIM, which is simply not a reliable source of information in this regard. 1/3/19 revision
  39. 39. ML work by Tudor Oprea Genes 54 Source Causal T2DM transcripts AUC 0.79±0.01 1/16/19 revision • Second model: 54 causal transcripts provided by Anuba Mahajan & Mark McCarthy vs. 3,954 OMIM genes. AUC = 0.79 ± 0.01.  Genes confirmed by GWAS (9 in top 24): C2CD4B, C2CD4A, JAZF1, ADAMTS9, CRY2, LINGO2, THADA, TMEM18 & SEC16B. 4 genes have GO terms for insulin secretion: CPLX1, ADRA2A, SYT7 & SYTL4  Top 4 VIP-ranked variables include 2 PPI nodes: SLC30A8 (rs13266634) and GIPR (rs8108269), which have GWAS-T2D associations.
  40. 40. Mouse Phenotype MP-ML models relevant for T2D Specific Mouse Phenotype MP Number Input Genes Top score pre- dicted genes Evidence supporting predictions (GWAS) abnormal circulating glucose level MP_ 0000188 155 98 7 (4) abnormal circulating insulin level MP_ 0001560 76 100 21 (5) abnormal glucose tolerance MP_ 0005291 146 100 12 (2) increased circulating glucose level MP_ 0005559 78 98 19 (5) decreased circulating glucose level MP_ 0005560 63 98 7 (4)  Human genes predicted from *glucose level MP-MLs: COX4I2, FOXQ1, DCD, APELA, FCRL3, PALM2, OSTN, NXNL1,TLL1, PYY, MAP3K14, EDIL3, DISC1, EPM2AIP1, PSD3, GFRA2, DDR2, ST3GAL3, MTURN, USP54, CPT1B,TYW1B, UGT1A5, UGT1A8, UGT1A3, UGT1A9, PPP1R15B, NUFIP2,TMEM167A, ITGA9, MRPL51, GBA, FOXRED1, DDIAS, BHLHA15, NAGS, RBM20, GKN1, C1orf43,TPGS2, MTPN, BEND3, CPEB3, ARHGAP40, CYSTM1  Human genes predicted from insulin level MP-ML: COQ8B,VAX1, SLC47A2, CCSER1, CMYA5, DNAH17, MTRNR2L12, IL17C, NLRP7, NLRP6, RASGRF2, ANKRD31, LAYN, UGT1A6, AMY2A, FAM19A2, FAM209B, RBM44, RNASE10, IL17RC, RLN2 3/28/19 revision
  41. 41.  Mackmyra tasked Microsoft and Fourkind to create novel whisky recipes using AI  From input of 75 recipes,“AI” could generate 70 million combinations.  Nr 36 on the AI ranked combinations was approved by humans  https://www.geekwire.com/2019/microsoft-got-creation-worlds-first-whisky-formulated-ai/ 9/22/19 revision
  42. 42. How long does it take to move from “natural” language processing to AI-driven large-dataset mining? Klingon, anyone? tlhIngan, vay'? 9/25/19 revision Tomáš Mikolov (Google), developed an efficient algorithm to compute the distributed representation of words, Word2Vec. It’s currently used for automatic translation, spam filtering and speech recognition. Word2vec encodes words using a distribution of weights across 100s of elements that compose the vectors. Each element contributes to many words. T. Mikolov et al.,ICLR 2013 10/10/19 revision
  43. 43. Alexahealth™: Given today’s health status and my calorie budget, what food should I shop/prepare today? Expanding on current models, IDG KMC could use AI/ML to integrate context- specific computational reasoning tools (“AMI”) with /real time –omics, biomarker and biomedical literature data. These could be plugged into hospital / EMR data to improve patient services. 10/10/19 revision
  44. 44. 8/24/20 revision Predictivity between different models for the same disease (even using the same ML methods) may differ due to input variations High quality data is really hard to obtain Weakest components: ‘Ground Truth’ (true negatives) and Domain Expertise

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