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. »

Inference of population structure from ancient DNA

“Inference of population structure from ancient DNA” by Tyler A. Joseph and Itsik Pe’er. https://doi.org/10.1101/261131 I selected this article for review for a few reasons: Inference of populaton structure is of fundamental interest Abundance of ancient DNA samples have increased; the data is challenging and has complexities I wanted to get an update on the state-of-the-art in the field in this space This paper focuses on tackling the problem of inference of structure in ancient DNA. »

Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma

“Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma” by Reuben Moncada, Marta Chiodin, Joseph C. Devlin, Maayan Baron, Cristina H. Hajdu, Diane Simeone, and Itai Yanai. https://doi.org/10.1101/254375 I selected this article for review for a few reasons: I thought the spatial aspects of the work were intriguing. I have an interest in methods that use single cell data to deconvolve bulk samples. I was curious about heterogeneity in this disease based on some of our previous work with FNA-derived PDX models. »

Welcome to biOverlay

I’m excited to be able to announce the impending arrival of biOverlay, which is similar to an overlay journal for the natural sciences. Like academic journals, we perform peer review of scientific literature. However, our overlay doesn’t publish papers. Authors do not know that papers are selected and sent out for review, and journals should not consider manuscripts that we selected for biOverlay as published. Because our process directly mirrors academic peer review other than the stage of submission, one could imagine that our assessments may be useful to journals when they select which papers to send out for review. »