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Files from the Advanced Concepts of Deep Learning Nanocourse

Created by Cooper Mellema in Dr. Montillo's Deep Learning Lab University of Texas Southwestern Medical Center Lyda Hill Dept. of Bioinformatics Project centered around using different causal measures to infer Effective connectivity and DL properties

Three variants of regression models based on deep neural networks, random forest, and gradient boosting to predict chromatin interaction strength.

PyTorch code and models for the DINOv2 self-supervised learning method.

Generate flow-rate gradients with Fluigent microfluidic pumps.

Course material for machine learning course offered to UTSW community.

Anything that you think could be useful for the general processing of MR spectroscopy data. Intended to serve as a library for MRS processing. Currently basic utilities for MRS preprocessing and LCModel fitting have been uploaded

Anything that you think could be useful for the general processing of MR spectroscopy data. Intended to serve as a library for MRS processing. Currently basic utilities for MRS preprocessing and LCModel fitting have been uploaded. Copy of old version. any new changes will be made here

Cooper Mellema - MSTP student Parkinson's MRI & MEG machine learning identification and classification project

Notebooks and scripts provided here can be used for analyzing data files generated by Tecan and Biotek plate readers.

This is a set of python and R routines for converting imaging and mask information to radiomic features and perform simple classification task.