Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions

A nice review on GxE interactions.

Ritchie MD, Davis JR, Aschard H, Battle A, Conti D, Du M, Eskin E, Fallin MD, Hsu L, Kraft P, Moore JH, Pierce BL, Bien SA, Thomas DC, Wei P, Montgomery SB. Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol. 2017 Oct 1;186(7):771-777. [PubMed] 

Abstract

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.

Data-driven Advice for Applying Machine Learning to Bioinformatics Problems

As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. In this arXiv paper, we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems.

Reproducibility of research results in the context of a complex system

I have always believed that it is unrealistic to expect research results to replicate across studies when the underlying biology is complex. This is a great piece in Nature that highlights this very point within the context of C. elegans. I highly recommend anyone interested in results replication and research reproducibility read this.

Automated Machine Learning (AutoML)

I posted a new website providing general information about AutoML and our own PennAI and TPOT projects. Feel free to contact me if you have something you think should be listed.

PennAI

In my role as Director of the Penn Institute for Biomedical Informatics I am leading a project to develop an accessible artificial intelligence system called PennAI. More info can be found on the PennAI website launched to provide updates and info about the method and software. We are looking forward to using this in my research lab for data science.

Is GWAS a hoax?

There is a great new Cell paper out from Jonathan Pritchard's group making the point that nearly the entire genome is connected to genes that impact risk of common human diseases. The implication is that genome-wide association studies (GWAS) are mostly finding incidental variants that happen to be involved in gene regulation or other genomic processes that only indirectly impact disease and thus might not make good drug targets. The identification of drug targets was the 'new' reason for doing GWAS replacing the old reason that focused on predicting risk which we now know doesn't work so well. The Cell paper is important but the point that it is really making is that disease risk is about systems and pathways rather than individual variants. This has been the focus of systems biology and complex adaptive systems all along. Human geneticists are perhaps finally waking up to this. I have written extensively about  a complex systems approach to human genetics for 20 years as partially documented in this blog. Here is a piece in Nature about this Cell paper and a thoughtful blog post by Dr. Ken Weiss from Penn State. Hopefully this is an indication that the univiariate approach to human genetics is finally over.

Automated Machine Learning Competition

We launched today a Kaggle-based competition for our tree-based pipeline optimization tool (TPOT) method for automated machine learning (AutoML). More information can be found here. Please spread the word!

The NIH rule of 21

I have worked my ass off my entire career publishing nearly 500 scientific papers (H-index = 73), training more than 50 undergraduate researchers, graduating 20 PhD students, training numerous postdocs, employing dozens of technical staff, mentoring dozens of faculty, and providing extensive service to the NIH as a good citizen. Despite all of this, Francis Collins and the NIH want to take grants away from me because I have not been productive enough. By all accounts this seems very likely. As you can tell I am quite steamed about this. I am not sure I will ever be able to forgive them should this come to pass [UPDATE: this policy was abandoned - see link below]. I have helped my institution respond to this. Not sure it will make a difference.

Here is the announcement from the NIH.

Here is a report on changes in response to the concerns of the research community.

Here is the announcement from the NIH about their plans to back away from this policy.

Here is information about the NIH Next Generation Researcher Initiative that seems like a much more sensible solution to the problem.

Detecting Statistical Interactions from Neural Network Weights

A new preprint on detecting interactions from deep learning models:

Detecting Statistical Interactions from Neural Network Weights 

Michael Tsang Dehua Cheng Yan Liu 
University of Southern California 
May 16, 2017 

Abstract 

Interpreting deep neural networks can enable new applications for predictive modeling where both accuracy and interpretability are required. In this paper, we examine the underlying structure of a deep neural network to interpret the statistical interactions it captures. Our key observation is that any input features that interact with each other must follow strongly weighted connections to a common hidden unit before the final output. We propose a novel framework for detecting feature interactions of arbitrary order by interpreting neural network weights. Our framework, which we call Neural Interaction Detector (NID), accurately identifies meaningful interactions without an exhaustive search on an exponential solution space of interaction candidates. Empirical evaluation on both synthetic and real-world data showed the effectiveness of NID, which can uncover interactions omitted by other methods in orders of magnitude less time.

Accessible Artificial Intelligence

We are developing an open-source and user-friendly AI system for machine learning analysis of data. We call this PennAI. We have posted a preprint on arXiv. This paper will be presented and published as part of the 2017 Genetic Theory and Practice Workshop (GPTP) that will take place later this month. Our project was written up in a nice piece published by Motherboard.