Biology subjects

Elhefnawi, M.

Publications and source records attributed to Elhefnawi, M..

3 recordsLinked to original sources

bioRxiv2026

PathoBERT: A Hybrid Attention-Based Genomic Language Model for Read-Level Bacterial Pathogenicity Prediction

Motivation Although recent deep learning models have achieved promising results on read level classification tasks, their robustness to realistic sequencing conditions, sensitivity to read length, and ability to support reliable genome level inference remain incompletely characterized. Here, we present PathoBERT, a hybrid deep learning framework that integrates a LoRA adapted DNABERT encoder with convolutional feature extraction, a modified Convolutional Block Attention Module (MCBAM), and Multiscale Convolutional Attention (MSCA) for bacterial pathogenicity prediction. Results The model demonstrated near complete strand invariance and maintained robust performance under simulated sequencing errors, highlighting its suitability for real world next generation sequencing applications. The model operates on individual sequencing reads and supports genome level inference through a read-aggregation strategy based on the Pathogenic Fraction (PathFrac), which combines read level predictions using a majority vote framework. At the read level, PathoBERT outperformed DeePaC at short and moderate fragment lengths (100 and 150 bp), achieving peak performance at 150 bp. At the genome level, PathoBERT achieved perfect separation between pathogenic and non pathogenic genomes using PathFrac based aggregation, resulting in perfect classification performance on the evaluated benchmark compared with competing approaches, namely DeePaC and PathogenFinder 2. Representation level analyses further demonstrated that a progressive refinement of pathogen-associated features throughout the architecture, culminating in highly separable and biologically meaningful latent representations within the final attention pooled embedding space. These findings demonstrate that integrating contextual genomic language models with attention guided multiscale feature extraction provides a robust framework for pathogenicity prediction from short read sequencing data.

bioinformatics↗

bioRxiv2020

Systematic auditing is essential to debiasing machine learning in biology

Representational biases that are common in biological data can inflate prediction performance and confound our understanding of how and what machine learning (ML) models learn from large complicated datasets. However, auditing for these biases is not a common practice in ML in the life sciences. Here, we devise a systematic auditing framework and harness it to audit three different ML applications of significant therapeutic interest: prediction frameworks of protein-protein interactions, drug-target bioactivity, and MHC-peptide binding. Through this, we identify unrecognized biases that hinder the ML process and result in low model generalizability. Ultimately, we show that, when there is insufficient signal in the training data, ML models are likely to learn primarily from representational biases.

systems biology↗

bioRxiv2018

Deep feature selection for Identification of Essential Proteins of Learning and Memory in Mouse Model of Down Syndrome

Down syndrome is a chromosomal abnormality related to intellectual disabilities that affects 0.1% of live births worldwide. It occurs when an individual has a full or partial extra copy of chromosome 21. This chromosome trisomy results in the overexpression of genes that is believed to be sufficient to interfere normal pathways and normal responses to stimulation, causing learning and memory deficiency. Therefore, by studying these proteins and the disturbance in pathways that are involved in learning and memory, we can consider drugs that would correct the observed perturbations, and therefore assist in enhancing the memory and learning. Here, from genes based on an earlier study that identified 77 proteins differentially expressed in normal and trisomic wild mice exposed to context fear conditioning (CFC), we provide a quantitative protein selection based on different feature selection techniques to select the most important proteins related to learning and memory. These techniques include Fisher score, Chi score, and correlation-based subset. In addition, a deep feature selection is utilized to extract high order proteins using deep neural networks. Three main experiments are carried out:studying the control mices response, studying the trisomy mices response, and studying the control-trisomy mices response. In each experiment, support vector machine classifier is used to assess these selected proteins ability to distinguish between learned and not-learned mice to the fear conditioning event. By applying the deep feature selection, fifteen proteins were selected in control mice, nine in trisomy mice, and seven in control-trisomy mice achieving distinguishing accuracies of 93%, 99%, 84% respectively compared to 74%, 78%, and 71% average accuracies of other selection methods. Some of these proteins have important biological function in learning such as CaNA, NUMb, and NOS.

bioinformatics↗