Category Archives: scilit

New algorithm can create movies from just a few snippets of text | Science | AAAS

Interesting paper by alumnus Renqiang Min on “Video Generation from Text,” using a generative #MachineLearning model. (Press report by @SilverJacket: New algorithm can create movies from just a few snippets of text )

Video Generation from Text Yitong Li†∗, Martin Renqiang Min‡ , Dinghan Shen† , David Carlson† , Lawrence Carin† †

JClub by BW on “3D clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets”, Genome Medicine

3D clusters of somatic mutations…reveal numerous rare mutations as functional targets Introduces 3DHotSpots, which is one of a number of recent approaches (incl. CLUMPS, Hotspot3D, Mutation3D & HotMAPS) for finding groupings of somatic SNVs via structure

Journal Club Paper

Zhou, J. and Troyanskaya, O.G. (2015). Predicting effects of noncoding variants with deep learning–based sequence model. Nature Methods, 12, 931–934.

Predicting (& prioritizing) effects of noncoding variants w. [DeepSEA] #DeepLearning…model Trained w #ENCODE data

NAR Breakthrough Article: denovo-db: a compendium of human de novo variants

.@denovodb: a compendium of [initially ~33K] human de novo variants w. phenotype, freely downloadable as a TSV table

As of July 2016, denovo-db contained 40 different studies and 32,991 de novo variants from 23,098 trios. Database features include basic variant information (chromosome location, change, type); detailed annotation at the transcript and protein levels; severity scores; frequency; validation status; and, most importantly, the phenotype of the individual with the variant.

Genes, environment, and “bad luck” | Science

Quite relevant….

Genes, environment & bad luck To what degree are #cancer mutations due to replication error (3rd factor), not 1st 2?

discusses R v D correlation

Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention Cristian Tomasetti1,2,*, Lu Li2, Bert Vogelstein3,*
Science 24 Mar 2017:
Vol. 355, Issue 6331, pp. 1330-1334
DOI: 10.1126/science.aaf9011