Akanksha Arora

This paper identifies seven distinct sets of prognostic biomarkers for predicting high-risk SKCM patients using transcriptomics data and machine learning models.

The paper develops models to predict exosomal miRNAs using alignment-based and AI approaches, achieving improved performance with an ensemble method.

This paper identifies a panel of eight salivary exRNA biomarkers for gastric cancer detection using machine learning, achieving high accuracy and outperforming existing biomarker panels.

This paper develops AdmirePred, a method to predict abundant miRNAs in exosomes using machine learning and hybrid approaches, achieving an AUC of 0.854.

This paper introduces ToxinPred 3.0, an improved method for predicting peptide toxicity using alignment-free machine/deep learning and ensemble approaches, achieving high accuracy and sensitivity.

This paper develops a deep learning model using single-cell transcriptomics to classify HNSCC and distinguish HPV+ from HPV- patients, achieving high AUROC scores on a validation dataset.

The paper develops a hybrid model combining machine learning and similarity-based methods to predict hormonal peptides, achieving high accuracy on a dataset from plants and animals.

This paper develops a hybrid model combining motif-based and machine learning approaches to predict exosomal proteins with high accuracy, achieving AUROC of 0.85 on an independent dataset.

This study develops a deep learning model to predict Alzheimer's disease using single-cell transcriptomics data, achieving an AUC of 0.75-0.84 on validation datasets.

Hmrbase2 is an updated database containing hormone and receptor information from 803 organisms, with improved data content and search features.

This paper develops an ensemble model to predict phage-host interactions for phage therapy, achieving higher accuracy than existing methods across multiple taxonomic levels.

Nfeature computes a large number of features for DNA and RNA sequences, providing a standalone package and web platform for researchers.