Author

R G SHARATHCHANDRA

Tumkur UniversityORCID 0000-0002-1156-9986Unclaimed 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:bulk RNA-seq· 3 Research Focus:differential expression· 3 Research Focus:gene regulatory network inference· 3 Organism:human· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Approach:machine learning (classical)· 1

Frequent Co-Authors

Ateeq Muhammed KhaliqiD· 2 shared Meenakshi RajamohaniD· 2 shared Prasad GandhamiD· 1 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

3 top collaborators · 1 mutual co-author link

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2 shared papersBioinformatics

Ateeq Muhammed KhaliqORCID

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3 papers

Recent·Most cited

In-silico analysis reveals hub genes and enriched pathways in Type 2 Diabetes Mellitus

Prasad Gandham, R G SHARATHCHANDRA

This study identifies hub genes and enriched pathways in type 2 diabetes using bioinformatics analysis of T2D-associated genes.

bioRxiv · Aug 21, 2023 · PreprintBioinformatics

Prediction of best features in heterogeneous Lung adenocarcinoma samples using Least Absolute Shrinking and Selection Operator.

Ateeq Muhammed Khaliq, R G SHARATHCHANDRA, Meenakshi Rajamohan

This study uses LASSO to predict key features in LUAD subtypes based on RNA-Seq data from TCGA, showing improved survival prediction through unsupervised clustering and feature selection.

International Journal of Life Science and Pharma Research · Oct 31, 2019Peer-reviewedBioinformatics

Gene prediction in heterogeneous cancer tissues and establishment of Least Absolute Shrinking and Selection Operator model of lung squamous cell carcinoma.

Ateeq Muhammed Khaliq, R G SHARATHCHANDRA, Meenakshi Rajamohan

The paper uses LASSO modeling on RNASeq data from TCGA to identify subtype-specific genes in lung squamous cell carcinoma based on tumor heterogeneity.

International Journal of Life Science and Pharma Research · Oct 15, 2019Peer-reviewedBioinformatics