Benjamin A. Logsdon

TL;DR: It is shown that schizophrenia is polygenic and the utility of this resource of gene expression and its genetic regulation for mechanistic interpretations of genetic liability for brain diseases is highlighted.

TL;DR: An algorithm is introduced that uses prior information about each gene’s importance in AML to identify the most predictive gene-drug associations from transcriptome and drug response data from 30 AML samples, which outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately.

TL;DR: Co-expression analyses identify a gene module that shows enrichment for genetic associations and is thus relevant for schizophrenia, paving the way for mechanistic interpretations of genetic liability for schizophrenia and other brain diseases.

TL;DR: A consensus atlas of the human brain transcriptome in Alzheimer’s disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples, is presented, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies.

TL;DR: It is found that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble.