I am a human geneticist whose academic training and thesis research have centered on NGS‑based diagnostic approaches and RNA sequencing technologies, providing a strong foundation in human genetics and the analysis of high‑throughput omics data.
I am particularly interested in research environments that combine wet‑lab experimentation with computational biology, where experimental molecular approaches and in‑silico analysis work synergistically to generate biologically meaningful and clinically interpretable insights. I am especially motivated by interdisciplinary projects that maintain a balance between laboratory investigation and computational data analysis.
My research interests are strongly aligned with translational medicine, with the aim of applying genomic and transcriptomic technologies to improve the molecular diagnosis, biological understanding, and therapeutic strategies for rare diseases, cancer, and other complex human disorders.
To pursue these goals, I work with and actively develop expertise in next‑generation sequencing (NGS), RNA sequencing, and advanced transcriptomic technologies such as single‑cell RNA sequencing (scRNA‑seq). I am also highly interested in expanding this framework toward proteomics and integrative multi‑omics approaches to better understand disease mechanisms across multiple molecular layers.
Ultimately, my goal is to contribute to research that bridges human genetics, omics technologies, computational biology, and clinical translation, enabling data‑driven discoveries that support precision medicine and translational biomedical research.
DESeq2
limma
edgeR
NOISeq
EBSeq
voom
tximport
tximeta
ballgown
sleuth
WGCNA
flashClust
dynamicTreeCut
igraph
tidygraph
ggraph
networkD3
clusterProfiler
ReactomePA
DOSE
fgsea
enrichR
pathview
GSEABase
GSVA
CIBERSORT
xCell
MCPcounter
ESTIMATE
immunedeconv
EPIC
quanTIseq
survival
survminer
rms
pec
timeROC
riskRegression
caret
glmnet
randomForest
ranger
xgboost
gbm
e1071
nnet
kernlab
MLmetrics
ggplot2
ComplexHeatmap
pheatmap
EnhancedVolcano
ggpubr
ggrepel
cowplot
patchwork
RColorBrewer
viridis
plotly
Machine Learning Identification of Prognostic Biomarkers
Repository
https://github.com/heidarzadehroozbeh-cmyk/ML-Prognostic_Biomarkers-for-Serous-OvarianCancer
Full Project Abstract
This project investigates prognostic biomarkers in high‑grade serous ovarian carcinoma using a comprehensive multi‑cohort transcriptomic framework.
Gene expression datasets from public repositories were integrated and systematically analyzed using differential expression analysis, weighted gene co‑expression network analysis (WGCNA), and functional enrichment strategies.
Machine learning models were subsequently applied to identify and validate prognostic biomarkers associated with patient survival outcomes. The resulting framework provides a reproducible computational strategy for biomarker discovery in complex cancer transcriptomic datasets.
Repository
https://github.com/heidarzadehroozbeh-cmyk/crpc-wgcna-metastasis-2025
Full Project Abstract
This project explores transcriptional regulatory networks involved in metastatic castration‑resistant prostate cancer (CRPC).
Using weighted gene co‑expression network analysis, metastasis‑associated modules were identified and integrated with functional enrichment analysis and pathway interpretation.
The objective was to uncover key regulatory hubs and biological processes driving metastatic progression in CRPC.
Repository
https://github.com/heidarzadehroozbeh-cmyk/pcos-wgcna-biomedicines-2023
Full Project Abstract
This study applies systems transcriptomics to investigate molecular mechanisms underlying polycystic ovary syndrome.
Network‑based approaches were used to identify co‑expression modules and hub genes potentially involved in disease pathophysiology.
Functional enrichment analysis provided insight into biological pathways and regulatory processes contributing to PCOS development.
Repository
https://github.com/heidarzadehroozbeh-cmyk/EOC_WNT_TGFb_EMT_Transcriptomics
Full Project Abstract
This project investigates transcriptional signatures associated with epithelial‑mesenchymal transition in epithelial ovarian cancer.
Transcriptomic datasets were analyzed to characterize signaling pathways involving WNT and TGF‑β signaling networks.
The analysis highlights regulatory mechanisms potentially contributing to tumor invasion, metastasis, and disease progression.
My current research interests center on omics‑driven collaborative projects, particularly in transcriptomics, genomics, and integrative computational biology.
I am especially interested in research environments that maintain a productive balance between wet‑lab experimentation and bioinformatics analysis, allowing computational findings to remain closely connected to biological mechanisms and experimental validation.
I am highly motivated by opportunities to contribute to and lead new scientifically challenging projects, especially those involving complex biological questions, multi‑step analytical design, and translational impact.
Computational Oncology • Systems Biology • Translational Bioinformatics