ProfessorsProfessors of Columbia University in the City of New YorkChaitanya Rastogi

Chaitanya Rastogi

Columbia University in the City of New York

H-index: 12

North America-United States

About Chaitanya Rastogi

Chaitanya Rastogi, With an exceptional h-index of 12 and a recent h-index of 11 (since 2020), a distinguished researcher at Columbia University in the City of New York, specializes in the field of Protein engineering, Computational biology, Machine learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Optimisation of TP53 reporters by systematic dissection of synthetic TP53 response elements

Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning

Transcription factor paralogs orchestrate alternative gene regulatory networks by context-dependent cooperation with multiple cofactors

Systems and methods for analyzing sequencing data

Context-dependent gene regulation by homeodomain transcription factor complexes revealed by shape-readout deficient proteins

Chaitanya Rastogi Information

University	Columbia University in the City of New York
Position	Postdoctoral Scholar
Citations(all)	815
Citations(since 2020)	603
Cited By	401
hIndex(all)	12
hIndex(since 2020)	11
i10Index(all)	12
i10Index(since 2020)	11
Email	Access Email
University Profile Page	Columbia University in the City of New York
Google Scholar	View Google Scholar Profile

Chaitanya Rastogi Skills & Research Interests

Protein engineering

Computational biology

Machine learning

Top articles of Chaitanya Rastogi

Title	Journal	Author(s)	Publication Date
Optimisation of TP53 reporters by systematic dissection of synthetic TP53 response elements	Nucleic Acids Research	Max Trauernicht Chaitanya Rastogi Stefano G Manzo Harmen J Bussemaker Bas van Steensel	2023/10/13
Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning	Nature Biotechnology	H Tomas Rube Chaitanya Rastogi Siqian Feng Judith F Kribelbauer Allyson Li ...	2022/10
Transcription factor paralogs orchestrate alternative gene regulatory networks by context-dependent cooperation with multiple cofactors	Nature Communications	Siqian Feng Chaitanya Rastogi Ryan Loker William J Glassford H Tomas Rube ...	2022/7/1
Systems and methods for analyzing sequencing data			2021/12/30
Context-dependent gene regulation by homeodomain transcription factor complexes revealed by shape-readout deficient proteins	Molecular cell	Judith F Kribelbauer Ryan E Loker Siqian Feng Chaitanya Rastogi Namiko Abe ...	2020/4/2