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1day-notebooks

2021_qsarcovid

Небольшой проект с попыткой найти что-то нормальное среди молекул

acute-toxicity-qsar

Predictive Models for Acute Oral Systemic Toxicity.

adversarial-learning-robustness

Contains materials for workshops pertaining to adversarial robustness in deep learning.

code

Compilation of R and Python programming codes on the Data Professor YouTube channel.

computationalbio-informal

2-1 Informal SOP under Aduri Sir. Based on Computational Biology and Machine Learning

convolutional_neural_networks_essentials

Contains presentation deck and notebooks showcasing fundamental concepts and hands-on examples for Convolutional Neural Networks

data

kumpulan data

data_science_for_all

Code and resources for my blog and articles to share Data Science and AI knowledge and learnings with everyone

deep-rules

Ten Simple Rules for Deep Learning in Biology

deep_transfer_learning_nlp_dhs2019

Contains the code and deck for the presentation on Applying Deep Transfer Learning for NLP in Analytics Vidhya's DataHack Summit 2019

deeplearning-models

A collection of various deep learning architectures, models, and tips

deepscreen

DEEPScreen: Virtual Screening with Deep Convolutional Neural Networks Using Compound Images

diffusiondrawer

Diffusion models for 2D rendering of compounds

drug_discovery

QSAR

dva

Computational methods for drug re-positioning identify potential anti-virals treatments against COVID-19

explainable_artificial_intelligence

Slides, code and resources for model interpretation methods in machine learning and deep learning

feature_engineering_session_dhs18

Contains code and presentation for my interactive hack session, 'Effective Feature Engineering: A Structured Approach to Building Better Machine Learning Models' where we look at two interesting case studies on how to effectively leverage feature engineering and use a structured approach to build good machine learning models. This is created for th

generation-of-novel-drug-molecules-with-specific-protein-targets-through-a-graph-network-and-custom-

ABSTRACT Creation of novel drug molecules is a time consuming and expensive process. Current methods require manually synthesizing thousands of molecules to develop a single viable lead candidate. In silico prediction of drug–target interactions (DTI) is necessary for the development of new drugs. In this research, I developed a novel artificial

herg-qsar-model

hignn

Code for "HiGNN: A Hierarchical Informative Graph Neural Network for Molecular Property Prediction Equipped with Feature-Wise Attention"

image

leaf

img

img2mol

lc50-qsar-estimator

ml_model_deployment_example

A simple example to showcase machine learning model deployment with an API

ml_qsar

This repo holds all the codes on all the machine learning models and the fuzzy logic model that has been created to solve this problem regarding the QSAR dataset.

ml_qsar-1

Applying machine learning models to QSAR problems

molecule-solubility

Molecule water solubility prediction using DeepChem.

notebooks