R package for microbiome biomarker discovery
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Updated
Nov 4, 2023 - R
R package for microbiome biomarker discovery
Curated List of Biomarkers, Blood Tests, and Blood Tracking
🧪 🖥 Transparent exploration of machine learning for biomarker discovery from proteomics and omics data
Learn interpretable computational phenotyping models from k-merized genomic data
TIGS (Tumor Immunogenicity Score) project https://doi.org/10.7554/eLife.49020
👀 An all-purpose eye tracking web application and API for Alzheimer's disease research (3 tasks, <3 mins). 1st place in the 2021 CNT hackathon https://www.cnthackathon.org/
🧪 🖥 Transparent exploration of machine learning for biomarker discovery from proteomics and omics data
A user-friendly R pipeline for biomarker discovery in single-cell transcriptomics
Python implementation of the feature relevance interval (FRI) algorithm
OmicSelector - Environment, docker-based application and R package for biomarker signiture selection (feature selection) & deep learning diagnostic tool development from high-throughput high-throughput omics experiments and other multidimensional datasets. Initially developed for miRNA-seq, RNA-seq and qPCR.
FS-Select identifies the best feature selection (FS) method for a given dataset from a pool of FS methods.
Histomic Prognostic Signature (HiPS): A population-level computational histologic signature for invasive breast cancer prognosis
Investigating the reproducibility of federated GNN models
netNorm (network normalization) framework for multi-view network integration (or fusion), recoded up in Python by Ahmed Nebli.
📦 🔬 R/biotmle: Targeted Learning with Moderated Statistics for Biomarker Discovery
Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data (Nature Communications, 2023)
NAGFS (Network Atlas-Guided Feature Selection) for a fast and accurate graph data classification code, recoded by Dogu Can ELCI.
This repository is the author implementation of the paper "Biomarker Identification by Reversing the Learning Mechanism of Autoencoder and Recursive Feature Elimination"
Spatial profiling toolbox for spatial characterization of tumor immune microenvironment in multiplex images
Objective of this project is to compare different machine learning models and deep learning neural networks. It also focusses on hyperparameter tuning and performance of deep learning neural network over machine learning. Dataset Used: Diabetes prediction
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