Living in a world of federated knowledge challenges, principles, tools and solutions
Download as PPTX, PDF0 likes151 views
The document discusses a chemistry data platform, OSDR, which supports FAIR data principles and integrates machine learning to handle specific use cases. It emphasizes an extensible micro-service architecture utilizing open-source toolkits, enabling continuous evolution and improvement while facilitating data curation and validation. The platform aims to remove proprietary software barriers and enhance the management of complex scientific data.
![Chemical data[base]](https://image.slidesharecdn.com/livinginaworldoffederatedknowledge-challengesprinciplestoolsandsolutions-170823235132/85/Living-in-a-world-of-federated-knowledge-challenges-principles-tools-and-solutions-7-320.jpg)
Living in a world of federated knowledge challenges, principles, tools and solutions
- 1. Living in a world of federated knowledge: Challenges, principles, tools and solutions Fall ACS 2017, Washington, DC Rick Zakharov1, Valery Tkachenko1 1 Science Data Software, Rockville, MD, United States
- 2. We live in a hyperconnected World
- 4. Dimensions and complexity of scientific data
- 6. Traditional data – relational
- 8. Why is it so hard to…. Competitors? What’s the structure? Are they in our file? What’s similar? What’s the target?Pharmacology data? Known Pathways? Working On Now? Connections to disease? Expressed in right cell type? IP?
- 9. Big Data Integration 9 OpenPHACTS
- 13. D a t a Data Lake Social Media Electronic Notebooks Databases Sensor Med Dev IoT Curated Repository Models Curation & Integration Validation Decision Support Analysis & Modeling Open Data Science Platform Mining USERS Model-driven experimental studies
- 14. Organize your data in a natural way ● Now-natural folder structure ● Organize your data into collections ● You have an option to download anything to your local drive as long as the security context allows etc
- 15. Chemical processing ● Support for chemical formats ● Chemistry validation and standardization ● Automatic processing and visualization
- 16. OSDR - documents • Integrated text-mining
- 17. Other formats
- 18. Convert between formats ● Integrated format transformation ● 50+ various data formats
- 19. OSDR - mapping and conversion
- 20. OSDR - import
- 21. OSDR - export
- 22. Predefined or custom metadata Tagging Attributes Taxonomies Ontologies Metadata Harvesting Industry Standards Metadata
- 23. Collaborative data authoring and curation ● Datacite.org support ● Other formats ● Audit trail ● Notifications
- 24. Extensive search options ● Search language ● Elasticsearch technology ● Domain-specific search modules ● Search ranking
- 25. Built-in Machine Learning ● Automated ML pipeline ● Pre-built ML modules ● Comparison between different ML algorithms ● NB, NN, RF, SVM, LR ● DNN
- 27. Datasets used for evaluating multiple computational methods for activity chemical properties prediction Model Datasets used and references Cutoff for active Number of molecules and ratio solubility Huuskonen J. J Chem Inf Comput Sci 2000 Log solubility = −5 1144 active, 155 inactive, ratio 7.38 probe-like Litterman N. et al. J Chem Inf Model 2014 described in reference 253 active, 69 inactive, ratio 3.67 hERG Wang S. et al. Mol Pharm 2012 described in reference 373 active, 433 inactive, ratio 0.86 KCNQ1 PubChem BioAssay: AID 2642 98 using actives assigned in PubChem 301,737 active, 3878 inactive, ratio 77.81 Bubonic plague (Yersina pestis) PubChem single-point screen BioAssay: AID 898 active when inhibition ≥50% 223 active, 139,710 inactive, ratio 0.0016 Chagas disease (Typanosoma cruzi) Pubchem BioAssay: AID 2044 with EC50 <1 μM, >10-fold difference in cytotoxicity as active 1692 active, 2363 inactive, ratio 0.72 TB (Mycobacterium tuberculosis) in vitro bioactivity and cytotoxicity data from MLSMR, CB2, kinase, and ARRA datasets Mtb activity and acceptable Vero cell cytotoxicity selectivity index = (MIC or IC90)/CC50 ≥10 1434 active, 5789 inactive, ratio 0.25 malaria (Plasmodium falciparum) CDD Public datasets (MMV, St. Jude, Novartis, and TCAMS) 3D7 EC50 <10 nM 175 active, 19,604 inactive, ratio 0.0089 Note the active/inactive ratios for hERG and KCNQ1 are reversed as we are trying to obtain compounds that are more desirable (active = non inhibitors).
- 28. Solubility dataset: selected ROC
- 29. Solubility dataset: polar plots of the model evaluation metrics BNB - Bernoulli Naive Bayes, LLR - Logistic linear regression, ABDT - AdaBoost Decision Trees, RF - Random Forest, SVM - Support Vector Machines, DNN-N (N is number of hidden layers).
- 30. AUC for all tested datasets (FCFP6, 1024) Clark et al. J Chem Inf Model 2015 AUC values BNB LLR ABDT RF SVM DNN-2 DNN-3 DNN-4 DNN-5 Clark et al. solubility train 0.959 0.991 0.996 0.934 0.983 1.000 1.000 1.000 1.000 0.866 solubility test 0.862 0.938 0.932 0.874 0.927 0.935 0.934 0.934 0.933 probe-like train 0.989 0.932 1.000 0.984 0.995 1.000 1.000 1.000 1.000 0.757 probe-like test 0.636 0.662 0.658 0.571 0.665 0.559 0.563 0.565 0.563 hERG train 0.930 0.916 0.992 0.922 0.960 1.000 1.000 1.000 1.000 0.849 hERG test 0.842 0.853 0.844 0.834 0.864 0.840 0.841 0.841 0.840 KCNQ train 0.795 0.864 0.809 0.764 0.864 1.000 1.000 1.000 1.000 0.842 KCNQ test 0.786 0.826 0.801 0.732 0.832 0.861 0.856 0.852 0.848 Bubonic plague train 0.956 0.946 0.985 0.895 0.992 1.000 1.000 1.000 1.000 0.810 Bubonic plague test 0.681 0.767 0.643 0.706 0.758 0.754 0.752 0.753 0.753 Chagas disease train 0.812 0.847 0.865 0.815 0.926 1.000 1.000 1.000 1.000 0.800 Chagas disease test 0.731 0.763 0.768 0.732 0.789 0.790 0.791 0.790 0.789 Tuberculosis train 0.721 0.737 0.760 0.735 0.800 1.000 1.000 1.000 1.000 0.727 Tuberculosis test 0.671 0.681 0.676 0.679 0.695 0.687 0.684 0.688 0.685 Malaria train 0.994 0.993 0.999 0.979 0.998 1.000 1.000 1.000 1.000 0.977 Malaria test 0.984 0.982 0.966 0.953 0.975 0.975 0.975 0.974 0.974
- 32. Extensible micro-service based architecture
- 33. Micro-service ● Single responsibility ● Simple API ● One-pizza size team ● Independent development ● Independent deployment and scaling ● Different services can be implemented using different technologies
- 34. Technologies ● Mix of technologies connected through microservices architecture ● Open source toolkits and libraries with permissive licenses ● NoSQL Databases ● Containerization ● Leading practices in CI/CD ● Automated testing, rapid development
- 35. Summary • OSDR is a chemistry data platform • Supports FAIR data principles • Can handle specific use cases via modules • Integrated Machine Learning • Remove proprietary software barriers • Uses open source toolkits • Evolve and improve continuously
- 36. Thank you! On Web: scidatasoft.com Slides: https://www.slideshare.net/valerytkachenko16 Contact us: info@scidatasoft.com
Editor's Notes
- #5: What about science and chemistry in particular?
- #9: Remember this, some of these questions are easier to answer than others
- #10: Open PHACTS was developed to support the key questions of drug discovery Business questions have been at the heart of Open PHACTS and have driven the development of the platform Mx/psa, how calculated who did it? Mash up. With your data too, - top layer join together but need them all commercial Data provided by many publishers Originally in many formats: relational, SD files and RDF Worked closely with publishers Data licensing was a major issue Over 5 billion triples – 14 datasets & growing Hosted on beefy hardware; data in memory (aim) Extensive memcaching Pose complex queries to extract data
- #30: The representative polar plots of the model evaluation metrics for the Solubility dataset.
- #31: In general the DNN models performed well for predictions except for the AUC performance of the probe-like dataset. For AUC DNN-3 outperforms BNB on 6 of 8 datasets