Fazlul Huq

Network-based identification of genetic factors in Ageing, lifestyle and Type 2 Diabetes that Influence in the progression of Alzheimer’s disease

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.

A system biology approach reveals cellular pathway differences between follicular thyroid carcinoma and follicular thyroid adenoma

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

Detection of Parkinson’s disease using blood and brain cells transcript profiles

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

Genetic Effects of Welding Fumes on the Development of Respiratory System Diseases

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

Identification of biomarkers and pathways to identify novel therapeutic targets in Alzheimers disease: Insights from a systems biomedicine perspective

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

Genetic Effects of Welding Fumes on the Progression ofNeurodegenerative Diseases

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

Bioinformatics Approach to Identify Diseasome and Comorbidities Effect of Mitochondrial Dysfunctions on the Progression of Neurological Disorders

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

Identification of Ovarian Cancer Gene Expression Patterns Associated with Disease Progression and Mortality

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