Author

S. Nambiar

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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Biophysics· 1 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Assay:protein sequence· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1

Frequent Co-Authors

A. Abikar· 1 shared D. Koner· 1 shared H. Bhukya· 1 shared K. A. Wagh· 1 shared N. H. Narayana· 1 shared P. Ranganathan· 1 shared P. V. Reddihalli· 1 shared R. S. Patil· 1 shared

Activity by Year

2023-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperCancer Biology

A. Abikar

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2 papers

Recent·Most cited

DeePNAP: A deep learning method to predict protein-nucleic acids binding affinity from sequence

U. Pandey, S. M. Behara, S. Sharma, R. S. Patil, S. Nambiar, D. Koner, H. Bhukya

DeePNAP predicts protein-nucleic acid binding affinity and mutation effects from sequence alone, achieving high accuracy using a large heterogeneous dataset.

bioRxiv · Dec 3, 2023 · PreprintBiophysics

microRNA profile of endometrial cancer from Indian patients-Identification of potential biomarkers for prognosis

S. Hegde, K. A. Wagh, A. Abikar, S. Nambiar, S. Ananthamurthy, N. H. Narayana, P. V. Reddihalli, S. Chandraiah, S. M. Narayana, P. Ranganathan

This study profiles microRNAs in Indian endometrial cancer patients, identifies 16 miRNA-mRNA pairs as potential prognostic biomarkers, and compares them to TCGA data.

bioRxiv · Aug 7, 2023Cancer Biology