Distributed Machine Learning for Bio-marker Prediction from Big Data Stream collected from Multi-modal Wearable Sensor Data
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Updated
Mar 25, 2017 - Python
Distributed Machine Learning for Bio-marker Prediction from Big Data Stream collected from Multi-modal Wearable Sensor Data
📦 Bioconductor data package associated with the biotmle R package
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
Different methods for optimizing state-of-the-art feature selection methods namely SVMRFE, HSICLASSO, and mRMR.
NAG-FS (Network Atlas-Guided Feature Selection) for a fast and accurate graph data classification.
We provide both Matlab and Python versions of netNorm. In this folder you find the Maltab version of the code.
NAGFS (Network Atlas-Guided Feature Selection) for a fast and accurate graph data classification code, recoded by Dogu Can ELCI.
FS-Select identifies the best feature selection (FS) method for a given dataset from a pool of FS methods.
netNorm (network normalization) framework for multi-view network integration (or fusion), recoded up in Python by Ahmed Nebli.
👀 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/
Working towards deliverable 5.3
5 gene pairs for predicting cervical LN metastasis in patients with early-stages oral squamous cell carcinoma
Curated List of Biomarkers, Blood Tests, and Blood Tracking
TIGS (Tumor Immunogenicity Score) project https://doi.org/10.7554/eLife.49020
📦 🔬 R/biotmle: Targeted Learning with Moderated Statistics for Biomarker Discovery
Fall 2020 - Computational Medicine - course project
Here, we studied the conservation of carP sequence and its occurrence in diverse phylogenetic groups of bacteria. In silico analysis revealed that carP and its two paralogues PA2017 and PA0319 are primarily present in P. aeruginosa and belong to the core genome of the species.
This repository is the author implementation of the paper "Biomarker Identification by Reversing the Learning Mechanism of Autoencoder and Recursive Feature Elimination"
Learn interpretable computational phenotyping models from k-merized genomic data
Python implementation of the feature relevance interval (FRI) algorithm
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