Predictive, Agent-Based, and Causal Machine Learning Models of U.S. Congressional Elections

2018 ◽  
Author(s):  
Parker Quinn
Keyword(s):  
Machine Learning ◽  
Congressional Elections ◽  
Learning Models ◽  
Agent Based ◽  
Machine Learning Models

scholarly journals Multi-fidelity Sequential Learning for Accelerated Materials Discovery

2021 ◽  
Author(s):  
Aini Palizhati ◽  
Muratahan Aykol ◽  
Santosh Suram ◽  
Jens Strabo Hummelshøj ◽  
Joseph H. Montoya
Keyword(s):  
Machine Learning ◽  
A Priori ◽  
Sequential Learning ◽  
A Priori Knowledge ◽  
Learning Models ◽  
Agent Based ◽  
Acquisition Strategies ◽  
Acquisition Strategy ◽  
Gap Data ◽  
Machine Learning Models

We introduce a new agent-based framework for materials discovery that combines multi-fidelity modeling and sequential learning to lower the number of expensive data acquisitions while maximizing discovery. We demonstrate the framework's capability by simulating a materials discovery campaign using experimental and DFT band gap data. Using these simulations, we determine how different machine learning models and acquisition strategies influence the overall rate of discovery of materials per experiment. The framework demonstrates that including lower fidelity (DFT) data, whether as a-priori knowledge or using in-tandem acquisition, increases the discovery rate of materials suitable for solar photoabsorption. We also show that the performance of a given agent depends on data size, model selection, and acquisition strategy. As such, our framework provides a tool that enables materials scientists to test various acquisition and model hyperparameters to maximize the discovery rate of their own multi-fidelity sequential learning campaigns for materials discovery.

scholarly journals Multi-fidelity Sequential Learning for Accelerated Materials Discovery

2021 ◽  
Author(s):  
Aini Palizhati ◽  
Muratahan Aykol ◽  
Santosh Suram ◽  
Jens Strabo Hummelshøj ◽  
Joseph H. Montoya
Keyword(s):  
Machine Learning ◽  
A Priori ◽  
Sequential Learning ◽  
A Priori Knowledge ◽  
Learning Models ◽  
Agent Based ◽  
Acquisition Strategies ◽  
Acquisition Strategy ◽  
Gap Data ◽  
Machine Learning Models

We introduce a new agent-based framework for materials discovery that combines multi-fidelity modeling and sequential learning to lower the number of expensive data acquisitions while maximizing discovery. We demonstrate the framework's capability by simulating a materials discovery campaign using experimental and DFT band gap data. Using these simulations, we determine how different machine learning models and acquisition strategies influence the overall rate of discovery of materials per experiment. The framework demonstrates that including lower fidelity (DFT) data, whether as a-priori knowledge or using in-tandem acquisition, increases the discovery rate of materials suitable for solar photoabsorption. We also show that the performance of a given agent depends on data size, model selection, and acquisition strategy. As such, our framework provides a tool that enables materials scientists to test various acquisition and model hyperparameters to maximize the discovery rate of their own multi-fidelity sequential learning campaigns for materials discovery.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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