Ammar has posted a preprint of his paper “rfPhen2Gen: A machine learning based association study of brain imaging phenotypes to genotypes” on arXiv. In this paper, Ammar evaluated random forest regression as a method to identify biologically relevant associations by learning models to predict SNPs from imaging features, using data for 518,484 SNPs and 56 brain imaging traits from the ADNI study.
Our paper “Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders” has been published in G3. In this paper we analyse the mathematical structure of a class of statistical models for learning hidden factors influencing gene expression data and show that a new algorithm based on the analytical results is orders of magnitude faster than the standard algorithms for solving this class of models.
Congratulations to Ramin, whose paper “A graph feature auto-encoder for the prediction of unobserved node features on biological networks” has been published in BMC Bioinformatics!
Congrats to Ramin for posting no less than two preprints on arXiv:
Both papers result from a collaboration between Ramin and Dariush Salami at the Ambient Intelligence group at Aalto University, and introduce new graph neural network-based methods for analyzing point cloud data.
Great to see when students independently initiate projects and push them through to completion (exposing the limits of their supervisor’s knowledge in the process 🙂
Congratulations to Adriaan for having his article (and art work!) selected for the cover of the April issue of Molecular Omics!
Adriaan’s paper “Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast” has been posted on biorXiv. In this paper we used Findr to compare several methods for causal inference from genomics and transcriptomics data, using a recently published dataset of 1,012 segregants from a cross between two budding yeast strains.
Lingfei’s paper, “High-dimensional Bayesian network inference from systems genetics data using genetic node ordering” has been published in Frontiers in Genetics, in a Special Topic on Machine Learning and Network-Driven Integrative Genomics.
In this paper, we present a highly efficient approach for reconstructing Bayesian gene regulatory networks when prior information for the inclusion of edges exists or can be inferred from the available data. The method is implemented in the Findr software.