MIA: Umut Eser, Fiddle: integrative deep learning framework for genomics; Alex Wiltschko, Auto diff

October 26, 2016

MIA Meeting: https://youtu.be/pcLTUsOm5pc?list=PLl...

Umut Eser
Churchman Lab
Harvard Medical School

FIDDLE: An integrative deep learning framework for functional genomic data inference


Abstract: Numerous advances in sequencing technologies have revolutionized genomics through generating many types of genomic functional data. Statistical tools have been developed to analyze individual data types, but there lack strategies to integrate disparate datasets under a unified framework. Moreover, most analysis techniques heavily rely on feature selection and data preprocessing which increase the difficulty of addressing biological questions through the integration of multiple datasets. Here, we introduce FIDDLE (Flexible Integration of Data with Deep LEarning) an open source data-agnostic flexible integrative framework that learns a unified representation from multiple data types to infer another data type. As a case study, we use multiple Saccharomyces cerevisiae genomic datasets to predict global transcription start sites (TSS) through the simulation of TSS-seq data. We demonstrate that a type of data can be inferred from other sources of data types without manually specifying the relevant features and preprocessing. We show that models built from multiple genome-wide datasets perform profoundly better than models built from individual datasets. Thus, FIDDLE learns the complex synergistic relationship within individual datasets and, importantly, across datasets.

Alex Wiltschko
Twitter Cortex

Primer: Automatic differentiation, the algorithm behind all deep neural networks

Abstract: A painful and error-prone step of working with gradient-based models (deep neural networks being one kind) is actually deriving the gradient updates. Deep learning frameworks, like Torch, TensorFlow and Theano, have made this a great deal easier for a limited set of models — these frameworks save the user from doing any significant calculus by instead forcing the framework developers to do all of it. However, if a user wants to experiment with a new model type, or change some small detail the developers hadn’t planned, they are back to deriving gradients by hand. Fortunately, a 30+ year old idea, called “automatic differentiation”, and a one year old machine learning-oriented implementation of it, called “autograd”, can bring true and lasting peace to the hearts of model builders. With autograd, building and training even extremely exotic neural networks becomes as easy as describing the architecture. We will also address two practical questions — "What's the difference between all these deep learning libraries?" and "What does this all mean to me, as a biologist?" — as well as providing some detail and historical perspective on the topic of automatic differentiation.

For more information visit: http://www.broadinstitute.org/mia

Copyright Broad Institute, 2016. All rights reserved.

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