FOREFRONT BIOINFORMATICS & STATISTICS RESEARCH GROUP

AD_RNA-Seq

Supplementary material towards the manuscript: Modelling early changes in Alzheimer’s disease by RNA Sequencing of brain regions differentially affected by pathology.

awesome-gan-for-medical-imaging

Awesome GAN for Medical Imaging

awesome-public-datasets

A topic-centric list of HQ open datasets.

CDR-Classification

Towards the development of robust models for clinical dementia rating classification with improved interpretability

MinimumBoundingBox

Finds the minimum bounding box from a point cloud.

ML-for-Parkinson-a-Microarray

Jupyter Notebook files to classify Microarray data into PD/HC

Non-image-data-classification-with-CNN

Non-image data classification with CNN: 3 algorithms

PPMI_classification

Algorithms and pipeline to classify PD

SRA-RNAseq

ukb-IDEARS

UKBiobank dementia, AD and PD classification and SHAP

binfnstats/SRA-RNAseq

binfnstats/ukb-IDEARS

UKBiobank dementia, AD and PD classification and SHAP

binfnstats/Augmentor

Image augmentation library in Python for machine learning.

binfnstats/bias-and-LIME

binfnstats/boruta_py

Python implementations of the Boruta all-relevant feature selection method.

binfnstats/cuml

cuML - RAPIDS Machine Learning Library

binfnstats/deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

binfnstats/DeepBrain

Deep learning framework for Alzheimer's disease severity index (SI) from brain transcriptomic data

binfnstats/deepgene

Source code of the paper "DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations"

binfnstats/DeepInsight3D_pkg

DeepInsight3D package to deal with multi-omics or multi-layered data

binfnstats/DeepInsightTab2Image

DeepInsight Tab2Image coder: a simple and easy way of converting tabular data to images for convolutional neural networks (CNNs). Improvements and new packages are added. One line function for data conversion from tabular to image samples.

binfnstats/iDash2020

binfnstats/idears_orig

binfnstats/image-gpt

binfnstats/interpret

Fit interpretable models. Explain blackbox machine learning.

binfnstats/kaggle_moa_winner_hungry_for_gold

Winning Solution of Kaggle Mechanisms of Action (MoA) Prediction.

binfnstats/lime

Lime: Explaining the predictions of any machine learning classifier

binfnstats/matconvnet

MatConvNet: CNNs for MATLAB

binfnstats/mccorkindale_thesis

Code and Supplementary data pertaining to thesis

binfnstats/mClass---Multiple-cancer-classification

binfnstats/ML_cognition

Machine learning code used in our paper to find genes and pathology linked to cognition in the ROSMAP cohort

binfnstats/omicsGAT

Graph Attention Network for Cancer Subtype Analyses

binfnstats/PiML-Toolbox

PiML (Python Interpretable Machine Learning) toolbox for model development and validation

binfnstats/pyDeepInsight

A python implementation of the DeepInsight methodology.

binfnstats/pytorch-grad-cam

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

binfnstats/RNA-seq_SNP-calling

binfnstats/ROSMAP_RNAseq

Code for machine learning and diffex on ROSMAP RNAseq data

binfnstats/shap

A game theoretic approach to explain the output of any machine learning model.

binfnstats/spinn

Sparse-input neural networks

binfnstats/torch-cam

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)