At the #ICML19 Workshop on Computational Biology Rachel Chan will present a poster on a new approach for "Semi-supervised learning in Segway"! Work with Mickaël Mendez Max Libbrecht sites.google.com/view/icml-comp…

Genome annotations of 164 human cell types with Segway, plus new methods for analysis and visualization -- now out in Genome Biology! Michael Hoffman @michaelhoffman.bsky.social Zhiping Weng William Stafford Noble genomebiology.biomedcentral.com/articles/10.11…

New preprint from my student Faezeh Bayat on variance-stabilized units for sequencing-based genomics assays! We found that currently-used log and arcsinh transformations do not correctly stabilize variance and developed a method called VSS that does so. biorxiv.org/content/10.110…

Max Libbrecht, Rachel Chan and Michael Hoffman @michaelhoffman.bsky.social just submitted a review of segmentation and genome annotation (SAGA) algorithms such as Segway and ChromHMM. Check out the preprint here: arxiv.org/abs/2101.00688

1\ Kevin Dsouza, and Adam Li submitted a paper on " Latent representation of the human pan-celltype epigenome through a deep recurrent neural network". In this research, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the

Mohammad Sadegh Saberian defended his MSc thesis on Using DNNs in multiple instance learning to identify putative treatments effective against SARS-CoV-2 based on morphological analysis. Congratulations, Mohammad Sadegh!

1\ Einar Gabbassov, Miguel Moreno-Molina, Iñaki Comas, Maxwell Libbrecht, and Leonid Chindelevitch published their work on "SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data". Since the occurrence of multiple strains of a

2\ bacterial pathogen within a single human host has important implications for public health. In this research, we introduce SplitStrains, a novel method grounded in a rigorous statistical model, estimates mixed strains' proportions and deconvolves them.

3\ The paper is available here: pubmed.ncbi.nlm.nih.gov/34165419/

I'm honored to have been selected for the Michael Smith Scholar award -- thank you, @msfhr! msfhr.org/1/news_article…

Maxwell Libbrecht has joined our Editorial Board, and will be handling papers in our Genomics section! You can find out more about Max's research here: plos.io/3LLuoJb

Nice overview from ⁦⁦Sara Mostafavi⁩ on how to unlock genetic and mechanistic insights from biological data using explainable AI, with many applications in regulatory genomics Nature Reviews Genetics⁩ nature.com/articles/s4157…

Happy to share that our survey of the current interpretable methods for deep learning models in genomics is finally out in Nature Reviews Genetics! 1/7 nature.com/articles/s4157…

Review on "Obtaining genetics insights from deep learning via explainable artificial intelligence" out now in Nature Reviews Genetics! Thanks to co-authors Gherman Novakovsky (слава Україні! 🇺🇦), Nick Dexter, Wyeth Wasserman, Sara Mostafavi nature.com/articles/s4157…

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Presenting on "Accurate and interpretable prediction of drug resistance in M. tuberculosis using deep learning" at the Mathematics and Statistics of Genomic Epidemiology workshop. Thanks to the organizers for the opportunity to present. BIRS birs.ca/events/2022/5-…

New paper in @OUPBioinfo: "Integrative chromatin domain annotation through graph embedding of Hi-C data", Neda Shokraneh, Mariam Arab, Max Libbrecht.

It's official, I've been granted tenure and promoted to Associate Professor! A huge thank you to my students, colleagues, mentors and collaborators.

New preprint: "Integrative chromatin state annotation of 234 human ENCODE4 cell types using Segway reveals disease drivers" Marjan Farahbod Mehdi Foroozandeh Habib Daneshpajouh Ben Hitz J. Michael Cherry Max Libbrecht biorxiv.org/content/10.110…

🧬 Excited to share our paper "Robust chromatin state annotation," now in Genome Research, Mehdi Foroozandeh, Marjan Farahbod, Max Libbrecht. Paper: bit.ly/scgrp Blog Post: bit.ly/scmbp Follow this thread for details. (1/6)👇