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Today we're joined by Jannis Born, Ph.D. student at ETH & IBM Research Zurich.
We caught up with Jannis a few weeks back at NeurIPS, where he was presenting his poster on "PaccMannRL: Designing anticancer drugs from transcriptomic data via reinforcement learning," a framework built to accelerate new anticancer drug discovery. In our conversation, Jannis details how his background in cognitive science and computational neuroscience applies to his current ML research. We then transition into a conversation about PaccMann, or Prediction of Anticancer Compound Sensitivity with Multimodal Attention-based Neural Networks, discussing how reinforcement learning fits into the goal of cancer drug discovery, how deep learning has changed this research, interesting observations made during the training of their DRL learner, and of course, Jannis offers us a step-by-step walkthrough of how the framework works to predict the sensitivity of cancer drugs on a cell and then discover new anticancer drugs.
PaccMann RL Project Page: Deep learning for cancer precision medicine — Designing anticancer drugs from omics profiles
Paper: PaccMann RL: Designing anticancer drugs from transcriptomic data via reinforcement learning
Paper: PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
Rosenblatt's perceptron, the first modern neural network
Simplified molecular-input line-entry system
Paper: Deep Sets