Author: Cyrille Njume
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Dynamic Shiny Dashboard for the Visualization of DESeq2 Results
The project developed an interactive Dynamic Shiny Dashboard to visualize DESeq2 differential expression results, allowing users to explore data without R programming skills. It features various visualizations such as volcano plots and heatmaps, facilitating effective communication of results while supporting collaborative efforts in bioinformatics. The project lasted two weeks.
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Gene Set Enrichment and KEGG Pathway Analysis Using ClusterProfiler
The project utilized KEGG-based gene set enrichment analysis from DESeq2 results to visualize biological pathway alterations in Alzheimer’s disease. Using R and ClusterProfiler, enriched pathways were identified and visualized, revealing significant immune and neurodegenerative responses. The findings could inform future research and biomarker discovery in Alzheimer’s.
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Differential Gene Expression Analysis in Alzheimer’s Disease
This project conducts differential gene expression analysis on Alzheimer’s disease using RNA-Seq data from the GSE53697 dataset, identifying differentially expressed genes (DEGs) via DESeq2 in R. It emphasizes preprocessing, outlier removal, and visualization through a Shiny app, facilitating interactive exploration of results, enhancing understanding of gene expression changes.
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Wrapping Bioinformatics Tools into a User-Friendly Web Platform
The project aimed to simplify access to complex bioinformatics tools by creating a user-friendly web platform for Bioinfopipe Ltd. Over four months, over 300 tools were wrapped into intuitive interfaces, enhancing usability for non-technical users. The project resulted in comprehensive documentation and a scalable solution on AWS, democratizing bioinformatics.
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BIOPRED: A Machine Learning-Based Web Application for Accurate Bioactivity Prediction, Drug Repurposing, and Molecular Docking
BIOPRED is a machine learning-driven web application developed for predicting drug-target interactions and supporting molecular docking. Utilizing ChEMBL data, it employs various algorithms for both regression and classification tasks with high accuracy. The user-friendly platform enables researchers to input SMILES strings and get bioactivity predictions, facilitating drug repurposing efforts.
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