Author

Rakesh Busi

Indian Institute of Technology BombayORCID 0009-0001-0899-6653Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Assay:protein sequence· 3 Research Focus:benchmarking· 2 Research Focus:gene regulatory network inference· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:fine-mapping· 1 Research Focus:protein folding & dynamics· 1

Frequent Co-Authors

N. HemachandraiD· 3 shared Petety V. BalajiiD· 3 shared Pranav MachingaliD· 3 shared

Activity by Year

2024-2025 · peak 2/yr

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3 top collaborators · 3 mutual co-author links

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3 shared papersBioinformatics

N. HemachandraORCID

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3 papers

Recent·Most cited

Systematic assessment of homology-based methods for fine-grained functional annotation using diverse protein families

Rakesh Busi, Pranav Machingal, N. Hemachandra, Petety V. Balaji

The paper evaluates BLASTp and profile HMMs for fine-grained functional annotation, finding BLASTp struggles with remote homologs while HMMs improve sensitivity but may miss some family members due to functional divergence.

bioRxiv · Sep 17, 2025 · PreprintBioinformatics

How suitable are clustering methods for functional annotation of proteins?

Rakesh Busi, Pranav Machingal, N. Hemachandra, Petety V. Balaji

This study evaluates nine clustering algorithms for unsupervised protein function annotation using pseudo-amino acid composition features, finding some methods effective for functional family segregation but less so for fine-grained differences.

bioRxiv · Dec 26, 2024 · PreprintBioinformatics

Alignment-free sequence composition based features can distinguish paralogous proteins

Pranav Machingal, Rakesh Busi, N. Hemachandra, Petety V. Balaji

The paper uses sequence composition features to classify paralogous proteins and identifies key amino acids driving classification.

bioRxiv · Apr 26, 2024 · PreprintBioinformatics