CANDLE-UNO

CANDLE-UNO

CANDLE (Exascale Deep Learning and Simulation Enabled Precision Medicine for Cancer) project aims to implement deep learning architectures that are relevant to problems in cancer. These architectures address problems at three biological scales: cellular (Pilot1 P1), molecular (Pilot P2) and population (Pilot3).

Pilot1 (P1) benchmarks are formed out of problems and data at the cellular level. The high level goal of the problem behind the P1 benchmarks is to predict drug response based on molecular features of tumor cells and drug descriptors. Pilot2 (P2) benchmarks are formed out of problems and data at the molecular level. The high level goal of the problem behind the P2 benchmarks is molecular dynamic simulations of proteins involved in cancer, specifically the RAS protein. Pilot3 (P3) benchmarks are formed out of problems and data at the population level. The high level goal of the problem behind the P3 benchmarks is to predict cancer recurrence in patients based on patient related data.

Uno application from Pilot1 (P1): The goal of Uno is to predict tumor response to single and paired drugs, based on molecular features of tumor cells across multiple data sources. Combined dose response data contains sources: [‘CCLE’ ‘CTRP’ ‘gCSI’ ‘GDSC’ ‘NCI60’ ‘SCL’ ‘SCLC’ ‘ALMANAC.FG’ ‘ALMANAC.FF’ ‘ALMANAC.1A’]. Uno implements a deep learning architecture with 21M parameters in TensorFlow framework in Python. The code is publicly available on GitHub. The script in this repository downloads all required datasets. The primary metric to evaluate this applications is throughput (samples per second). More details on running Uno can be found here.

CANDLE-UNO Specific Benchmark Targets

1. Scientific objective(s):
   • Objective: Predictions of tumor response to drug treatments, based on molecular features of tumor cells and drug descriptors
   • Formula: Validation loss
   • Score: 0.0054
2. Data
   • Download: http://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Pilot1/uno/
   • Data Size: 6.4G
   • Training samples: 423952
   • Validation samples: 52994
3. Example implementation
   • Model: Multi-task Learning-based custom model
   • Reference Code: https://github.com/ECP-CANDLE/Benchmarks/tree/develop/Pilot1/Uno
   • Run Instructions: https://github.com/ECP-CANDLE/Benchmarks/blob/develop/Pilot1/Uno/README.AUC.md
   • Time-to-solution: 10667 samples/sec (batch size 64) on single A100