Interpretability in deep learning for computational biology

09:00-12:30, January 25 @ 5A

Workshop / Overview

The recent application of deep neural networks to long-standing problems such as the prediction of functional DNA sequences, the inference of protein-protein interactions or the detection of cancer cells in histopathology images has brought a break-through in performance and prediction power.

However, high accuracy often comes at the price of loss of interpretability, i.e. many of these models are built as black-boxes that fail to provide new biological insights.

This tutorial focuses on illustrating some of the recent advancements in the field of Interpretable Artificial Intelligence. We will show how explainable, smaller models can achieve similar levels of performance than cumbersome ones, while shedding light on the underlying biological principles driving model decisions.

We will demonstrate how to build and extract knowledge using interpretable approaches in different domains of computational biology, including analysis of single-cell data, functional sequences of raw DNA, and drug sensitivity prediction models.

The choice of these applications is motivated by the availability of adequately large datasets that can support deep learning (DL) approaches and by their high relevance for personalized medicine. We will exploit both publicly available deep learning models as well as in-house developed models. 

Workshop / Outcome

The tutorial has been designed to provide the participants not only with an overview on current research in interpretability, but also to offer a set of frameworks, tools and real-life examples that they can implement in their own projects.

Specifically, the participants will acquire/refresh basic knowledge on DL models for computational biology by both a brief technical introduction and a showcase of established models for specific practical applications. Next, several techniques to enhance model interpretability will be explored.

At the end of the tutorial, participants are expected to understand which interpretability methods they can apply to their own problem and data, and how to implement/use them in python.

Workshop / Difficulty

Beginner level

Workshop / Prerequisites

  • Basic programming knowledge of Python
  • Basic knowledge of Machine Learning/Deep Learning
  • Kaggle account (only for one exercise)
  • Own Laptop (with working Docker or Conda setup)
  • We recommend setting up the necessary environment for the exercises at least one day before the workshop.
    Further information can be found at: https://github.com/IBM/depiction/tree/master/workshops/20200125_AMLD2020

Track / Co-organizers

Maria Rodriguez Martinez

Technical Lead of Systems Biology, IBM Research Zurich

An-phi Nguyen

PhD Student, IBM Research Zurich & ETH Zurich

Matteo Manica

Research Staff Member, IBM Research

AMLD EPFL 2020 / Workshops

A Conceptual Introduction to Reinforcement Learning

With Kevin Smeyers, Katrien Van Meulder & Bram Vandendriessche

09:00-12:30 January 251ABC

Applied Machine Learning with R

With Dirk Wulff, Markus Steiner & Michael Schulte-Mecklenbeck

09:00-17:00 January 25Foyer 6

Augmenting the Web browsing experience using machine learning

With Oleksandr Paraska, Vasily Kuznetsov, Tudor Avram & Levan Tsinadze

09:00-12:30 January 253A

AMLD / Global partners