Max Libbrecht

🧬 Excited to share our paper "Robust chromatin state annotation," now in Genome Research, @mforoozz, @marjanfarahbod, @maxlibbrecht. Paper: bit.ly/scgrp Blog Post: bit.ly/scmbp Follow this thread for details. (1/6)👇

New preprint: "Integrative chromatin state annotation of 234 human ENCODE4 cell types using Segway reveals disease drivers" @marjanfarahbod @mforoozz @HDaneshpajouh @bonscotthoughts @JMichaelCherry1 @maxlibbrecht biorxiv.org/content/10.110…

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

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_Math birs.ca/events/2022/5-…

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…

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 @NatureRevGenet⁩ nature.com/articles/s4157…

Review on "Obtaining genetics insights from deep learning via explainable artificial intelligence" out now in @NatureRevGenet! Thanks to co-authors @NovakovskyG, Nick Dexter, @WyWyWa, @sara_mostafavi nature.com/articles/s4157…

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

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

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

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.

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

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\ 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

@maxlibbrecht, @rachelcwchan and @michaelhoffman 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

@Gurkan_Yardimci @OHSUKnight @ENCODE_NIH Congratulations Gurkan!

Excited to announce I have joined @OHSUKnight as an Assistant Professor. Looking forward to starting new projects and collaborations here and continuing my existing collaborations at the @ENCODE_NIH project.

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…

@michaelhoffman Welcome @minisciencegirl!

@maxlibbrecht Y'all should meet!