Ashok Palaniappan

GenomicSign identifies unique, amplifiable genomic signatures for target sequences by analyzing k-mer uniqueness and designing primers for potential amplicons.

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

miR2Trait integrates miRNA-disease associations from two databases, offering a detailed resource for analyzing miRNA enrichment in diseases and disease-related miRNA profiles.

The paper develops an end-to-end computational approach to design RNA biosensors for miRNA biomarkers of cervical cancer, using public data and predictive models to guide sensor design and simulation.

This paper identifies potential drug repurposing candidates targeting P-glycoprotein homologues in neglected tropical diseases using computational methods.

The paper introduces a deep learning model called Riboflow that classifies riboswitches with over 99% accuracy using CNN and LSTM architectures.

PromoterPredict models Escherichia coli σ70 promoter strength using sequence data, finding a logarithmic relationship between sequence features and promoter strength.

This study investigates how MDR1 polymorphisms affect P-glycoprotein structure and drug binding, revealing potential mechanisms for antiepileptic drug resistance.