zeromtmu,github

machine-learning-energetic-molecules-notebooks

Python Jupyter Notebooks to reproduce the results in our paper, "Applying machine learning techniques to predict the properties of energetic materials"

machine-learning-for-thai-lottery

ใช้เพื่อการศึกษาเท่านั้น

metamap

MetaMap - This repository describes the code and analyses accompanying the "MetaMap: An atlas of metatranscriptomic reads in human disease-related RNA-seq data" manuscript.

ml-for-beginners

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

ml_cheat_sheet

My notes and superstitions about common machine learning algorithms

mlcp

Machine learning pipeline for CellProfiler 3.0 output

mmci-practical-datascience

Course Materials for the MMCi Practical Data Science Course

mmoi-calc

Experimental molecule moments of inertia calculator.

modifiednb

Popular cheminformatics Naïve Bayes model implemented in scikit-learn

moses

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

multiple_regression_gene_expression_predictions

The goal of this coding project is to generate a simple regression model (that takes a single input to make a prediction) and compare its performance to a multiple regression model (that takes more than one input to make a prediction.) The models are built in the context of the input, gene expression of a gene familiy, and how it affects the output, effector gene expression. I expect that a regression model built with multiple gene expression inputs would more precisely predict the gene expression of an effector gene. The goal is test this code with existing gene expression repositories such as the NIH Gene Expression Omnibus (GEO) repository.