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Commonly used software tools produce conflicting and overly-optimistic AUPRC values genomebiology.biomedcentral.com/articles/10.11… 🧬🖥️ github.com/wychencuhk/AUP…
Now on Genome Biology: genomebiology.biomedcentral.com/articles/10.11…
1/6 Are you really winning? 🤔️ We dive into 10 ML toolkits used in 3K+ papers and discovered some🤯 facts: ❌ Different tools give different AUPRC 🚨 Only 2/10 tools correctly implemented AUPRC 👀 Winners of ML contests ($$$) changed if using different tools See more in 🧵:
1/6 Are you really winning? 🤔️ We dive into 10 ML toolkits used in 3K+ papers and discovered some🤯 facts: ❌ Different tools give different AUPRC 🚨 Only 2/10 tools correctly implemented AUPRC 👀 Winners of ML contests ($$$) changed if using different tools See more in 🧵:
Commonly used software tools produce conflicting and overly-optimistic AUPRC values biorxiv.org/cgi/content/sh… #bioRxiv
Glad to share our preprint "GET: a foundation model of transcription across human cell types" biorxiv.org/content/10.110… A interactive demo (beta) for regulatory analysis and structure catalog of transcription factor interactions can be viewed at huggingface.co/spaces/get-fou…
Commonly used software tools produce conflicting and overly-optimistic AUPRC values biorxiv.org/cgi/content/sh… #biorxiv_bioinfo
When I saw topologically associating domains (TADs) drawn as globular structures in review papers, I always wondered whether TADs really have functionally important 3D structures. If you also wonder about it, check out our new preprint: doi.org/10.1101/2022.0…
Graph neural networks can capture complex object relationships. Eg, they can identify cancer genes based on omic features and protein interactions. See our new NMI paper rdcu.be/cJcs4 by @xxmen21 and @cqhoneybear to find out the tricks!
👏👏👏
I'm glad to share a thread on my paper studying disease-associating noncoding regulatory regions, just published on @genomeresearch November issue: genome.cshlp.org/content/30/11/… (together with an unpublished sci-fi theme cover art 🤣)
Happy to share you our freshly published work on @NatMachIntell We proposed GEEK, a flexible, network-embedding-based framework integrating heterogeneous biological knowledge to study their joint effects on gene expression. Read full text: rdcu.be/b5R48
Super excited to share our new framework for the analysis of #noncoding #variants in #WGS! -> Whole-genome analysis of noncoding genetic variations identifies multigranular regulatory element perturbations with Hirschsprung disease disq.us/t/3nn5k18
HiDRA: Building on Sharpr-MPRA+STARR-Seq+ATAC-Seq to dissect millions of human regulatory regions in one experiment biorxiv.org/content/early/…
Non-coding Transcription Instructs Chromatin Folding and Compartmentalization to Dictate Enhancer-Promoter… dlvr.it/Ppvl49
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Norbert Piske
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Xi Fu
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Bʜᴀɢɪʀᴀᴛʜ Kᴜᴍᴀʀ Lᴀᴅᴇʀ 🌴🐅🐘🦒🦌
@bhagirathl -
Ruochi Zhang
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Peter Sims
@psimslab -
Dr Ronald Ma
@DrRonaldMa1 -
Gerstein Lab | Yale
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Douglas Yao
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Hongbo Liu
@Hongbo919Liu -
Steve Giguere
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Linying Zhang
@Z_Linying -
Han Yuan
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Alireza Karbalayghareh
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Xueqin (Sherine) Sun
@SunSherine -
David Ticoll
@dticoll
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