Abdollah Dehzangi

TEDEdb: a large-scale resource and multi-cohort analysis of transposable element differential expression in cancer

The paper re-analyzes over 7,000 RNA-seq samples to study transposable element differential expression in cancer and finds TE expression is more prone to batch effects and can be induced by various compounds.

Population analysis and immunologic landscape of melanoma in people living with HIV indicate impaired antitumor immunity

This paper analyzes melanoma in people living with HIV, finding impaired antitumor immunity, delayed ICI treatment, and an immunosuppressive tumor microenvironment.

Deep Learning for Protein Peptide bindingPrediction: Incorporating Sequence, Structural andLanguage Model Features

This paper introduces PepCNN, a deep learning model for predicting protein-peptide binding that improves specificity, precision, and AUC over existing methods.

ACP-MHCNN: An Accurate Multi-Headed Deep-Convolutional Neural Network to Predict Anticancer peptides

ACP-MHCNN is a deep convolutional neural network for anticancer peptide identification that outperforms existing methods in multiple metrics.

CluSem: Accurate Clustering-based Ensemble Methodto Predict Motor Imagery Tasks from Multi-channelEEG Data

CluSem improves MI-EEG classification accuracy by 5-15% using a clustering-based ensemble method and is evaluated in a brain game called CluGame.