NCI/DOE Collaboration JDACS4C

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Table summarizing the NCI/DOE Cellular Level Pilot team's aims and accomplishments during the first calendar year of JDACS4C. Text reads "AIM. Devlop reliable machine learning-based predictive models of drug response that enable the projection of screening results from among cell line, cell-line xenographfts, and PDX models. Increase the efficacy with which cancer drugs are selected for use with increasingly distinct patient populations, while accelerating the identification and evaluation of promising new cancer drugs. Extend herory and tools for QU in multiple spatial and temporal scales, advance development of a computational framework for integrating theory, simulation, and wet-lab experiments with UQ. Integrate UQ and optimal experimental design for assert Accomplishments. Integrate UQ and optimal experimental design to assert quantitative limits on predictions and to recommend experiments that will improve predictions. Given existing data, conduct details error analysis, and develop initail UQ framework. Established a database populated wiht NCI data sources (CANDLE release). Build hybrid modeling frameworks for shallow and deep learning version of problems. Applied multiple methods of feature selection for each agent, or class of agents, to generate compact molecular signatures that retain predictive performace- reduced less than 50,000 characteristic features to 50 or fewer while retaining an accuracy less than .97.Developed new methods for model abstraction and hypothesis formation. Determined approaches to sample-size problems. Explored means of countering negative effects of small sample sizes. Methods examined included aggregating data from multiple sources; augmenting data by oversampling; generating synthetic data; using semi-servised, transfer, and multi-task learning. Developed new methods for model abstraction and hypothesis formation. Surveyed featured selection methods (Principle Components Analysis, Bayesian, RandomForest, etc.). Performed Rademacher confidence analysis for low N models. Performed sample size analysis for confidence interval (CI). CACTVS software technologies. Built modeling freamworkds for both shallow and deep learning versions of problems. Completed signature computation using a variety of feature types for NCI-60 data. Dertermined approaches to sample-size problems. Developed new methods for model abstration and hypothesis iinformation. Developed deep learning models for drug combinations.

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