MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments

Alinejad-Rokny, H; Modegh, RG; Rabiee, HRR; Sarbandi, ER; Rezaie, N; Tam, KT; Forrest, ARR

Alinejad-Rokny, H; Forrest, ARR (通讯作者)，Univ Western Australia, Harry Perkins Inst Med Res, QEII Med Ctr, Perth, Australia.;Alinejad-Rokny, H; Forrest, ARR (通讯作者)，Univ Western Australia, Ctr Med Res, Perth, Australia.;Alinejad-Rokny, H (通讯作者)，UNSW Sydney, Grad Sch Biomed Engn, Bio Med Machine Learning Lab BML, Sydney, Australia.;Alinejad-Rokny, H (通讯作者)，Macquarie Univ, Alenabled Proc AIP Res Ctr, Hlth Data Analyt Program, Sydney, Australia.

PLOS COMPUTATIONAL BIOLOGY, 2022; 18 (6):

Abstract

Author summaryMaxHiC is a robust machine learning based tool for identifying significant interacting regions from both Hi-C and capture Hi-C data. All......

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