10.4231/E80W-7941
Aihua Huang
,
John W. Sutherland
,
Sidi Deng
,
Xiaoyu Zhou
,
Yuehwern Yih
09/03/2020
This repository contains the supporting information for the manuscript regarding Sherwood principle and Machine learning. All critical underlying data files, along with a flow chart that describes the methodologies applied in the paper are enclosed.
Circular Economy Empirical Models Environmental and Ecological Engineering Machine Learning Sherwood Principle
10.4231/50R5-EM83
Karen Hudson
,
Kranthi K Varala
,
Ying Li
01/04/2024
Organ-specific gene expression datasets that include hundreds to thousands of experiments allow reconstruction of gene regulatory networks and discovery of transcriptional regulators various pathways and processes.
Arabidopsis thaliana Gene regulatory networks k-nearest neighbor (kNN) linear support vector machines (SVM) Machine Learning Systems biology
10.4231/SCF2-QJ02
Koushiki Basu
,
Nicholas Huls
,
Shan Lu
,
Tonglei Li
11/06/2024
We implemented the Manifold Embedding of Molecular Surface approach, which retains the quantum mechanical characteristics of molecules, to predict a drug's likelihood of binding to cytochrome P450 enzymes by deep learning.
deep learning Informatics Machine Learning Molecular Pharmacology
10.4231/1TZX-BR43
David R Johnson
,
Ed Delp
,
Fu-Chen Chen
,
Mohammad Jahanshahi
07/28/2020
Structural attributes relevant to flood risk (foundation height/type, square footage, number of stories, building type), produced using machine learning for automated image analysis of GSV images.
flood risk Industrial Engineering Machine Learning structure attributes
10.4231/0A0F-7A84
10/14/2019
This project works on understanding the different statistical models that are available to analyze and predict mean sea level changes in Guam.
Climate Change Data Education Earth and Atmospheric Sciences FAIR Data Machine Learning Sea level rise Statistical analysis Statistical Methods
10.4231/R7N58J9Z
Baichuan Zhang , Bartlomiej P. Rajwa , Murat Dundar , Qiang Kou , Yicheng He
10/01/2015
A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity
bacterial colonies BARDOT Biomedical Engineering Computer Science deep learning Elastic light scattering Interdisciplinary Research K-Means Clustering Life Sciences Machine Learning representation learning
10.4231/3YX4-EY30
01/21/2020
Source code of an ANN model, site level data, input data, output data and visualization results, which are presented in the manuscript "Inventorying Global Wetland Methane Emissions Based on In Situ Data and an Artificial Neural Network...
Artificial Neural Network (ANN) Atmospheric Chemistry Modeling Biogeochemistry Earth and Atmospheric Sciences Interactive Data Language (IDL) Machine Learning Matlab Methane Dynamics Model (MDM) Methane Emission
10.4231/R7ST7MVC
Kelsie Larson
,
Mireille Boutin
05/16/2017
The TARP method uses random projections, followed by threshold classifications, to construct receiver-operating characteristic curves and uncover underlying structure in the given data.
Electrical and Computer Engineering Machine Learning Receiver Operating Characteristics ROC curve Signal Processing Target Detection
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