Biology subjects

El-Hadidi, M.

Publications and source records attributed to El-Hadidi, M..

3 recordsLinked to original sources

bioRxiv2025

Decoding Breast Cancer Heterogeneity via Multi-Omics Integration and Language Model-Based Interpretation

We present a novel pipeline combining Multi-Omics Factor Analysis (MOFA) and fine-tuned Large Language Models (LLMs) to predict breast cancer subtypes using proteomics, DNA methylation, and RNA-Seq data. Breast cancer is a heterogeneous disease characterized by diverse molecular alterations across multiple biological layers, necessitating integrative approaches for accurate subtype classification. Our methodology leverages MOFA for dimensionality reduction to identify key latent factors driving het-erogeneity, followed by LLM fine-tuning on these multi-omics signatures to enhance prediction accuracy. MOFA analysis identified five key latent factors capturing distinct biological processes: immune response, cell cycle regulation, metabolic reprogramming, tumor microenvironment interactions, and DNA repair mechanisms. We extracted the top features per omics layer for each factor and performed Gene Set Enrichment Analysis (GSEA) to characterize their biological significance. Our LLM, trained on curated multi-omics signatures and clinical metadata encoded as structured text prompts, significantly outperformed conventional statistical models in subtype classification, achieving AUC=0.93 and accuracy=0.89, compared to Random Forest (AUC=0.87, accuracy=0.82) and SVM (AUC=0.85, accuracy=0.80). The superior performance of our approach is attributed to the LLMs ability to capture complex, non-linear relationships and hierarchical feature interactions across omics layers. This integrative pipeline provides both improved predictive performance and interpretable biological insights, offering potential for enhanced clinical decision-making in breast cancer management.

cancer biology↗

bioRxiv2018

SpeciesMLP: Sequence based Multi-layer Perceptron for Amplicon Read Classification Using Real-time Data Augmentation

Taxonomic assignment is the core of targeted metagenomics approaches that aims to assign sequencing reads to their corresponding taxonomy. Sequence similarity searching and machine learning (ML) are two commonly used approaches for taxonomic assignment based on the 16S rRNA. Similarity based approaches require high computation resources, while ML approaches dont need these resources in prediction. The majority of these ML approaches depend on k-mer frequency rather than direct sequence, which leads to low accuracy on short reads as k-mer frequency doesnt consider k-mer position. Moreover training ML taxonomic classifiers depend on a specific read length which may reduce the prediction performance by decreasing read length. In this study, we built a neural network classifier for 16S rRNA reads based on SILVA database (version 132). Modeling was performed on direct sequences using Convolutional neural network (CNN) and other neural network architectures such as Multi-layer Perceptron and Recurrent Neural Network. In order to reduce modeling time of the direct sequences, In-silico PCR was applied on SILVA database. Total number of 14 subset databases were generated by universal primers for each single or paired high variable region (HVR). Moreover, in this study, we illustrate the results for the V2 database model on 8443 classes on the species level and 1552 on the genus level. In order to simulate sequencing fragmentation, we trained variable length subsequences from 50 bases till the full length of the HVR that are randomly changing in each training iteration. Simple MLP model with global max pooling gives 0.71 & 0.93 test accuracy for the species and genus levels respectively (for reads of 100 base sub-sequences) and 0.75 & 0.96 accuracy for the species and genus levels respectively (on the full length V2 HVR). In this study, we present a novel method (SpeciesMLP https://github.com/ali-kishk/SpeciesMLP) to model the direct amplicon sequence using MLP over a sequence of k-mers faster 20 times than CNN in training and 10 times in prediction.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=131 HEIGHT=200 SRC=\"FIGDIR/small/419846_fig1.gif\" ALT=\"Figure 1\">\nView larger version (25K):\norg.highwire.dtl.DTLVardef@16a239aorg.highwire.dtl.DTLVardef@14773fcorg.highwire.dtl.DTLVardef@fcf14org.highwire.dtl.DTLVardef@1e43f9e_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Workflow diagram.The diagram begins with Insilico PCR to SILVA taxa. The rest pipeline is applied only to the V2 HVR database only from splitting to modeling.\n\nC_FIG

bioinformatics↗

bioRxiv2018

Human salivary amylase gene copy number impacts oral and gut microbiomes

Host genetic variation influences the composition of the human microbiome. While studies have focused on associations between the microbiome and single nucleotide polymorphisms in genes, their copy number (CN) can also vary. Here, in a study of human subjects including a 2-week standard diet, we relate oral and gut microbiome to CN at the AMY1 locus, which encodes the gene for salivary amylase, active in starch degradation. We show that although diet standardization drove gut microbiome convergence, AMY1-CN influenced oral and gut microbiome composition and function. The gut microbiomes of low-AMY1-CN subjects had an enhanced capacity for breakdown of complex carbohydrates. Those of high-AMY1 subjects were enriched in microbiota linked to resistant starch fermentation, had higher fecal SCFAs, and drove higher adiposity when transferred to germfree mice. Gut microbiota results were validated in a larger separate population. This study establishes AMY1-CN as a genetic factor patterning microbiome composition and function.

genetics↗