Autism , Whole Genome Sequencing , noncoding , deep learning

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

Update: This paper has now been published at Nature Genetics “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. »
Lucia Peixoto Author image
single cell , imputation , deep learning , RNA Sequencing , machine learning

Single cell RNA-seq denoising using a deep count autoencoder

Update: This paper has now been published at Nature Communications. “Single cell RNA-seq denoising using a deep count autoencoder” 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. »
Stephanie C. Hicks Author image
massively parallel reporter assay , deep learning , protein , UTR , RNA Sequencing , noncoding , polysome profiling

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

Update: This paper has now been published at Nature Biotechnology. “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. »
Stephanie C. Hicks Author image
deep learning , unsupervised , acute myeloid leukemia , cancer , transcriptome

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

Update: The preprint discussed in this has been updated since these reviews were posted. These reviews may no longer apply to the current version of the manuscript. “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. »
Casey S. Greene Author image