Dr.Nilofer

EasyDockVina. EasyDockVina is a free tool to perform for receptor with multiple (batch) ligand docking with AutoDockVina.

GEO-query-and-DEG-analysis-using-Python. GEO query and DEG analysis using Python

Deep_learning_Model_for_Drug_Discovery. Deep learning Model for Drug Discovery

Gene-Expression-analysis-with-DEGs-Significant-DEGs-and-Functional-Enrichment-Analysis. End to End Gene Expression analysis with DEGs, Significant DEGs and Functional Enrichment Analysis

BioMedical-and-Bioinformatics-Research-Paper-Search-with-AI-Summaries. BioMedical and Bioinformatics Research Paper Search with AI Summaries_By 🤖 Future Omics · 🤖Bioinformatics made easy ❤️ using Streamlit

GEOexplorer-gene-expression-analysis-and-visualisation. GEOexplorer a webserver for gene expression analysis and visualisation by launch the App in RStudio

RDKit-and-Py3Dmol. RDKit and Py3Dmol

py3Dmol. Py3Dmol is a convenient tool for interactive visualization of molecular structures in Python

Biomedical-and-Bioinformatics-Research-Paper-Search-Streamlit-app. Future Omics Bioinformatics Research Paper Search_an open source for scientific metadata to search research papers

Gene-Expression-Analysis-with-Data-Normalization-in-R_Part-1. 🚀 Gene Expression Analysis with Data Normalization in R🧬

AI-in-Drug-Discovery. AI in drug discovery, particularly utilizing machine learning (ML) models with drug solubility data, is a promising area that offers significant potential for accelerating the drug development process.

IRIS-Data-Machine-learning-Modelling. IRIS Data Machine learning Modelling and EDA analysis

Drug-Bioactive-Properties-Calculator_App. Drug Bioactive Properties Calculator using streamlit app

Gene-Expression-Analysis-Heatmap_top_50_genes-in-R_Part-2. 🚀 Gene Expression Analysis with Heatmap_Top_50_Genes in R_Part 2🧬

Pathway-Enrichment-Analysis_Part-4. Pathway Enrichment Analysis_Part 4

PaDEL-Descriptor. PaDELPy is a Python wrapper for the Java-based PaDEL-Descriptor software, streamlining molecular descriptor calculations

Machine-learning-in-drug-discovery_. Machine learning in drug discovery

Breast-Cancer-EDA-Data-Analysis. Breast Cancer Wisconsin EDA Data Analysis

Mining-Drug-Data-From-ChEMBL-Database-For-Drug-Discovery-. Mining Drug Data From ChEMBL Database For Drug Discovery

Gene-Expression-Omnibus-GEO-analysis-using-Python. Gene Expression Omnibus (GEO) analysis using Python

Streamlit-faster-way-to-build-app. Streamlit faster way to build app

Molecular-fingerprints-using-Python-and-RDKit. Molecular fingerprints using Python and RDKit

Pandas-Profiling-to-automate-Exploratory-Data-Analysis-EDA-. Pandas Profiling to automate Exploratory Data Analysis (EDA)

COVID-19-Drug-Discovery-using-Machine-Learning. COVID 19 Drug Discovery using Machine Learning

Differential-expression-analysis-using-R. Differential expression analysis using R

Matplotlib_Beginner_Examples. Matplotlib For Beginner Examples

Plotly-EDA-analysis. Plotly EDA analysis and visualization

RDKit-for-Cheminformatics. RDKit for Cheminformatics for drug discovery and molecular modeling

Computer-Aided-Drug-Discovery-CADD. Computer-Aided Drug Discovery (CADD)_ChEMBL Bioactivity data

Machine-Learning-Modeling-for-Differential-Gene-Expression. Machine Learning (ML) modeling for Differential Gene Expression_Significant genes

Download-the-Reference-Genome-FASTA-GTF-via-NCBI-Ensembl. Download the Reference Genome FASTA & GTF via NCBI & Ensembl using Python

Differential-Expression-Analysis-in-R_Part-3. Differential Expression Analysis and identify significant genes in R_Part 3

Download-GEO-Sample-id. Download GEO Sample id of Microarray Gene Expression Dataset

Download-GEO-query-perform-analysis. Download GEO query of gene expression dataset of microarray experiment

Differential-Gene-Expression-Analysis-using-DESeq2. This repository contains R scripts and guidance for performing Differential Gene Expression (DGE) analysis using the [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) package. It is designed to help researchers identify significantly differentially expressed genes from RNA-Seq data.

Exploratory-Data-Analysis-with-Skrub. Cancer Gene Expression and Exploratory Data Analysis (EDA) with Skrub is a powerful and modern approach to analyzing tabular data, particularly when that data is messy, incomplete, or contains categorical columns.

ADMET-ML-Modeling. This repository contains a Python script for performing Exploratory Data Analysis (EDA) and Machine Learning modeling on molecular datasets to predict aqueous solubility (LogS), an important ADMET property.

Breast-Cancer-Wisconsin-Data-Machine-Learning. Breast Cancer Wisconsin Data Machine Learning

ML-in-Drug-Discovery-using-SWISSADME-properties. ML in Drug Discovery using SWISSADME properties

Breast-cancer-EDA. Breast cancer data analysis and EDA

Gene-Expression-Analysis-of-GEO-. Gene Expression Analysis of GEO analysis of differentially expressed genes (DEGs), enriched GO terms and clusters of co-expressed genes

GEO-query-package. To access GEO expression data set using Geo query package in R

Python-for-Beginner-Examples. Python for Beginner tutorials

RDKit-drug-and-protein-3D-visualization. RDKit drug and protein 3D visualization

Breast-Cancer-Dataset-EDA. Breast Cancer Dataset EDA Exploratory Data Analysis (EDA) on the Breast Cancer dataset using Python libraries such as Matplotlib, Seaborn, and Plotly

Clinical-Data-Analysis. Clinical Data Analysis using matplotlib

Coding-for-biology-Iris-dataset-EDA. A Comprehensive Exploratory Data Analysis (EDA) of the Iris dataset using Python, we can leverage three visualization libraries: Matplotlib, Seaborn, and Plotly. Each library offers different strengths.

Fingerprints-PaDEL-Descriptors. Fingerprints PaDEL use for calculating molecular descriptors and fingerprints

Matplotlib-gene-expression. Matplotlib library in Python to visualize biological gene expression data to visualize the expression levels of different genes

Survival-analysis-in-R. Survival analysis in R for Lung cancer

Machine-Learning-Regression-Modeling-for-Gene-Expression-Predicting-logFC-or-B-. Regression model for predicting a binary class (significant vs not), and predict a continuous value either logFC (fold change in gene expression) or B (log-odds of differential expression). Multiple regression models to predict these values from gene expression statistics.

GEO-Differential-Gene-Expression-Analysis. **Differential Gene Expression (DEG) analysis** using publicly available datasets from the **Gene Expression Omnibus (GEO)**.