Pinned Repositories
BackgroundGenrator
CHOP-THT
CHQA Analyst take-home task
coding_challenge-29
Escape From Jurassic
Customer-Churn-Potential
gifity
HybridTox2D
In recent times, toxicological classification of chemical compounds is considered to be a grand challenge for pharma-ceutical and environment regulators. Advancement in machine learning techniques enabled efficient toxicity predic-tion pipelines. Random forests (RF), support vector machines (SVM) and deep neural networks (DNN) are often ap-plied to model the toxic effects of chemical compounds. However, complexity-accuracy tradeoff still needs to be ac-counted in order to improve the efficiency and commercial deployment of these methods. In this study, we implement a hybrid framework consists of a shallow neural network and a decision classifier for toxicity prediction of chemicals that interrupt nuclear receptor (NR) and stress response (SR) signaling pathways. A model based on proposed hybrid framework is trained on Tox21 data using 2D chemical descriptors that are less multifarious in nature and easy to calcu-late. Our method achieved the highest accuracy of 0.847 AUC (area under the curve) using a shallow neural network with only one hidden layer consisted of 10 neurons. Furthermore, our hybrid model enabled us to elucidate the inter-pretation of most important descriptors responsible for NR and SR toxicity.
ideal-octo-train
JackDaw
machine-learning-roadmap
A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
Multi-Class-Output-Conv2D
Haymaekr's Repositories
Haymaekr/CHOP-THT
CHQA Analyst take-home task
Haymaekr/psu-IE575
IE575 Team1 term project
Haymaekr/Multi-Class-Output-Conv2D
Haymaekr/Customer-Churn-Potential
Haymaekr/ideal-octo-train
Haymaekr/Port
Haymaekr/coding_challenge-29
Escape From Jurassic
Haymaekr/machine-learning-roadmap
A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
Haymaekr/zero-to-mastery-ml
All course materials for ZTM ML on Udemy
Haymaekr/BackgroundGenrator
Haymaekr/startup
Haymaekr/gifity
Haymaekr/PythonProjects
Haymaekr/JackDaw
Haymaekr/python-art
A ZTM Challenge for Hacktoberfest 2019
Haymaekr/HybridTox2D
In recent times, toxicological classification of chemical compounds is considered to be a grand challenge for pharma-ceutical and environment regulators. Advancement in machine learning techniques enabled efficient toxicity predic-tion pipelines. Random forests (RF), support vector machines (SVM) and deep neural networks (DNN) are often ap-plied to model the toxic effects of chemical compounds. However, complexity-accuracy tradeoff still needs to be ac-counted in order to improve the efficiency and commercial deployment of these methods. In this study, we implement a hybrid framework consists of a shallow neural network and a decision classifier for toxicity prediction of chemicals that interrupt nuclear receptor (NR) and stress response (SR) signaling pathways. A model based on proposed hybrid framework is trained on Tox21 data using 2D chemical descriptors that are less multifarious in nature and easy to calcu-late. Our method achieved the highest accuracy of 0.847 AUC (area under the curve) using a shallow neural network with only one hidden layer consisted of 10 neurons. Furthermore, our hybrid model enabled us to elucidate the inter-pretation of most important descriptors responsible for NR and SR toxicity.