Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism

“Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism” by Jian Zhou, Christopher Park, Chandra Theesfeld, Yuan Yuan, Kirsty Sawicka, Jennifer Darnell, Claudia Scheckel, John Fak, Yoko Tajima, Robert Darnell, Olga Troyanskaya https://doi.org/10.1101/319681 It is well known that Autism has a strong genetic component. In recent years the discovery of genetic factors linked to Autism has skyrocketed, powered by next generation sequencing. Exome sequencing studies have allowed the discovery of hundreds of coding genetic risk variants. »

Single cell RNA-seq denoising using a deep count autoencoder

“Single cell RNA-seq denoising using a deep count autoencode” by Gokcen Eraslan, Lukas M. Simon (both first-authors contributed equally), Maria Mircea, Nikola S. Mueller, Fabian J. Theis. https://doi.org/10.1101/300681 I selected this article for review for two reasons: I work with single-cell RNA-Sequencing (scRNA-seq) data quite a bit and I have a general interest in methods that remove technical sources of variation from it. I have a general interest in methods that use machine learning approaches (in this case an autoencoder network) with genomic data. »

Human 5' UTR design and variant effect prediction from a massively parallel translation assay

“Human 5’ UTR design and variant effect prediction from a massively parallel translation assay” by Paul J. Sample, Ban Wang, David W. Reid, Vlad Presnyak, Iain McFadyen, David R. Morris, and Georg Seelig. https://doi.org/10.1101/310375 I selected this article for review for two reasons: I am excited to learn about applications of massively parallel reporter assays (MPRAs). I have a general interest in methods that use deep learning (in this case convolutional neural networks) with genomic data. »

DeepProfile: Deep learning of patient molecular profiles for precision medicine in acute myeloid leukemia

“DeepProfile: Deep learning of patient molecular profiles for precision medicine in acute myeloid leukemia” by Ayse Berceste Dincer, Safiye Celik, Naozumi Hiranuma, and Su-In Lee. https://doi.org/10.1101/278739 I selected this article for review for a few reasons: I was intrigued by the compilation of a large dataset from many small datasets before applying unsupervised learning, which we have had success with in other settings. I have an interest in methods that use variational autoencoders to learn low-dimensional representations. »