Vinita Periwal

发表

Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets

Vinod Scaria, Vinita Periwal, Shireesha Kishtapuram, 2012, BMC pharmacology.

A comprehensive overview of computational resources to aid in precision genome editing with engineered nucleases

Vinita Periwal, Vinita Periwal, 2016, Briefings Bioinform..

Bioactivity assessment of natural compounds using machine learning models based on drug target similarity

Kiran Raosaheb Patil, Vinita Periwal, Athanasios Typas, 2020, bioRxiv.

Insights into structural variations and genome rearrangements in prokaryotic genomes

Vinod Scaria, Vinita Periwal, V. Scaria, 2015, Bioinform..

Machine Learning Approaches Toward Building Predictive Models for Small Molecule Modulators of miRNA and Its Utility in Virtual Screening of Molecular Databases.

Vinod Scaria, Vinita Periwal, V. Scaria, 2017, Methods in molecular biology.

Predictive modeling of anti-malarial molecules inhibiting apicoplast formation

Salma Jamal, Vinod Scaria, Vinita Periwal, 2013, BMC Bioinformatics.

Computational analysis and predictive modeling of small molecule modulators of microRNA

et al., Vinita Periwal, Salma Jamal, 2012, Journal of Cheminformatics.

periwaletal drug-food similarity ML data

Vinita Periwal, 2021 .

Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

Vinod Scaria, Vinita Periwal, V. Scaria, 2011, BMC Research Notes.

De novo identification of viral pathogens from cell culture hologenomes

Ashok Patowary, Vinod Scaria, Vinita Periwal, 2012, BMC Research Notes.

Comparative Whole-Genome Analysis of Clinical Isolates Reveals Characteristic Architecture of Mycobacterium tuberculosis Pangenome

Shamsudheen Karuthedath Vellarikkal, Ashok Patowary, Vinod Scaria, 2015, PloS one.