Author

Neha Periwal

Jamia Hamdard

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Organism:human· 2 Assay:proteomics· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Assay:long-read sequencing· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

Pooja AroraiD· 4 shared Vikas SoodiD· 4 shared Baljeet KauriD· 3 shared Shravan B. RathodiD· 2 shared Avantika Jain· 1 shared Gundeep S. Johar· 1 shared Jinal C. Soni· 1 shared Kinshuk Raj SrivastavaiD· 1 shared

Activity by Year

2022-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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4 shared papersBioinformatics

Pooja AroraORCID

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4 papers

Recent·Most cited

Effective prediction of IL-17 inducing peptides using hybrid approach: iIL17pred

Pooja Arora, Neha Periwal, Lakshay Aggarwal, Vikas Sood, Baljeet Kaur

This paper introduces a hybrid method for classifying IL-17 inducing peptides, achieving higher accuracy and MCC than existing methods.

bioRxiv · Jan 8, 2025 · PreprintBioinformatics

Time series analysis of SARS-CoV-2 genomes and correlations among highly prevalent mutations

Neha Periwal, Shravan B. Rathod, Sankritya Sarma, Gundeep S. Johar, Avantika Jain, Ravi Pratap Barnwal, Kinshuk Raj Srivastava, Baljeet Kaur, Pooja Arora, Vikas Sood

The paper analyzes SARS-CoV-2 genomic sequences over time to identify significant mutations and their effects on viral proteins.

Microbiology Spectrum · Sep 7, 2022Peer-reviewedBioinformatics

A novel binary k-mer approach for classification of coding and non-coding RNAs across diverse species

Neha Periwal, Priya Sharma, Pooja Arora, Saurabh Pandey, Baljeet Kaur, Vikas Sood

The paper introduces a binary k-mer approach to classify coding and non-coding RNAs across species, showing improved performance metrics compared to traditional ATGC methods.

Biochimie · Apr 25, 2022Peer-reviewedGenomics

Human blood plasma proteins modeling and binding affinities with Δ9-tetrahydrocannabinol active metabolites: In silico approach

Shravan B. Rathod, Jinal C. Soni, Priyanshu Verma, Yogita Rawat, Neha Periwal, Pooja Arora, Vikas Sood, Mohmedyasin F. Mansuri

This study uses molecular docking to analyze the binding affinities of Δ9-THC and its metabolites with human blood plasma proteins, finding Δ9-THC has the strongest binding.

bioRxiv · Apr 13, 2022 · PreprintBioinformatics