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Functional & Integrative Genomics|October 25, 2024
ANPS: machine learning based server for identification of anti-nutritional proteins in plantsSanchita Naha, Sarvjeet Kaur, Ramcharan Bhattacharya, et al.
Functional & Integrative Genomics|March 31, 2023
ASLncR: a novel computational tool for prediction of abiotic stress-responsive long non-coding RNAs in plantsUpendra Kumar Pradhan, Prabina Kumar Meher, Sanchita Naha, et al.
Protein Science : a Publication of the Protein Society|May 15, 2024
ProkDBP: Toward more precise identification of prokaryotic DNA binding proteinsUpendra Kumar Pradhan, Prabina Kumar Meher, Sanchita Naha, et al.
Briefings in Bioinformatics|November 23, 2022
PlDBPred: a novel computational model for discovery of DNA binding proteins in plantsUpendra Kumar Pradhan, Prabina Kumar Meher, Sanchita Naha, et al.
Computational and Structural Biotechnology Journal|April 25, 2024
RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotesUpendra Kumar Pradhan, Sanchita Naha, Ritwika Das, et al.
Plant Molecular Biology|September 24, 2024
PredPSP: a novel computational tool to discover pathway-specific photosynthetic proteins in plantsPrabina Kumar Meher, Upendra Kumar Pradhan, Padma Lochan Sethi, et al.
Briefings in Functional Genomics|May 9, 2023
RBPLight: a computational tool for discovery of plant-specific RNA-binding proteins using light gradient boosting machine and ensemble of evolutionary featuresUpendra K Pradhan, Prabina K Meher, Sanchita Naha, et al.
Briefings in Functional Genomics|August 31, 2023
DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organismsUpendra K Pradhan, Prabina K Meher, Sanchita Naha, et al.
Functional & Integrative Genomics|March 20, 2023
ASmiR: a machine learning framework for prediction of abiotic stress-specific miRNAs in plantsUpendra Kumar Pradhan, Prabina Kumar Meher, Sanchita Naha, et al.
Biochimica Et Biophysica Acta. General Subjects|March 15, 2024
ASPTF: A computational tool to predict abiotic stress-responsive transcription factors in plants by employing machine learning algorithmsUpendra Kumar Pradhan, Anuradha Mahapatra, Sanchita Naha, et al.
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