Mohammad Ali Moni

This study identifies key signalling pathways and biomarkers associated with dasatinib resistance in pancreatic cancer using transcriptomics and network analysis.

This study identifies shared genetic variants and causal relationships between coronary artery disease and stroke using GWAS and Mendelian randomization.

This study identifies shared genetic variants and causal genes between type 2 diabetes and stroke using GWAS data, revealing novel molecular targets.

This paper develops a machine learning model for diabetes prediction using data preprocessing and random oversampling, achieving high accuracy across multiple datasets.

This study identifies ten blood biomarker genes for stroke risk by integrating Mendelian randomisation and systems biology models with GWAS and transcriptomics data.

This study identifies 14 traits with potential causal genetic effects on stroke risk using GWAS and causal inference methods, highlighting shared genetic architectures and biological pathways.

The paper identifies and characterizes RNAi genes and their regulatory elements in sweet orange, finding 4 CsDCL, 8 CsAGO, and 4 CsRDR genes with functional annotations and expression patterns.

This study designs 8 siRNA molecules targeting SARS-CoV-2's nucleocapsid phosphoprotein and surface glycoprotein genes using sequence conservation and thermodynamic properties.

The paper identifies genes and molecular pathways associated with Alzheimer’s disease by analyzing gene expression data from multiple related conditions and using network-based methods.

The paper identifies differentially expressed genes, pathways, transcription factors, and microRNAs between follicular thyroid carcinoma and follicular thyroid adenoma using system biology approaches.

The paper identifies 12 dysregulated genes shared between brain and blood cells in Parkinson's disease using transcriptomic and eQTL data, validated with dbGaP.

The paper identifies gene expression changes in respiratory tissues exposed to welding fumes and links them to respiratory system diseases through network analysis.

This study identifies blood-based biomarker signatures and candidate drugs for Alzheimer's disease through integrative transcriptomics and network analyses.

The paper identifies common molecular biomarker signatures in blood and brain tissues of Alzheimer's disease patients, revealing shared gene expression patterns and regulatory elements.

The paper identifies differentially expressed genes between welding fumes and five neurodegenerative diseases using network analysis and gene expression data.

This study investigates the apoptotic effects of Basella alba leaf extracts on Ehrlich's Ascites Carcinoma cells using various bioassays and finds that the extracts induce apoptosis via both extrinsic and intrinsic pathways.

This study uses decision tree models to predict diabetes severity and identify significant features from a Bangladeshi dataset, finding plasma glucose, glucose 2hr after glucose, and HDL-cholesterol as key predictors.

The paper identifies dysregulated genes and shared genetic factors in Parkinson's disease using network-based analysis of gene expression data from multiple conditions.

The paper investigates how type 2 diabetes affects the risk and severity of neurological diseases by analyzing gene expression and developing genetic associations and networks.

The paper develops a framework to analyze how mitochondrial dysfunction affects neurological disorders by examining gene-disease networks, KEGG pathways, and protein interactions.

The paper identifies gene expression patterns in ovarian cancer associated with disease progression and mortality using clinical and transcriptomic data from TCGA.