Gökmen Altay

RNA-seq preprocessing and sample size considerations for gene network inference

This study evaluates preprocessing methods and sample size requirements for gene network inference using RNA-seq data, finding that no single method is optimal and recommending dataset-specific evaluations.

Disease mechanism, drug-target and biomarker prediction software: Application on prostate cancer and validation

The paper introduces an R software package for differential network analysis to predict disease mechanisms, drug targets, and biomarkers using gene expression data from two conditions, validated on prostate cancer datasets.

Tensorflow Based Deep Learning Model and Snakemake Workflow for Peptide-Protein Binding Predictions

The paper presents a TensorFlow-based deep learning model for MHC class I peptide binding prediction, along with code, benchmark datasets, and a Snakemake workflow for parallel analysis.

Gene Regulatory Cross Networks: Inferring Gene Level Cell-to-Cell Communications of Immune Cells

This study introduces two algorithms for inferring gene-level cell-to-cell communications between immune cell types and evaluates their performance using a membrane protein database.

Directed Conservative Causal Core Gene Networks

The paper introduces Ac3net, an improved version of C3NET for inferring large-scale directional gene/protein networks and shows it outperforms existing methods.

DepEst: an R package of important dependency estimators for gene network inference algorithms

DepEst provides 11 dependency score estimators for gene network inference algorithms, available as an R package with parallel and serial execution capabilities.