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Machine learning

Predicting drug sensitivity of cancer cells based on DNA methylation levels

Sofia P. Miranda, Fernanda A. Baião, Julia L. Fleck, Stephen R. Piccolo

Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2—ACE2 interface

Monika Rola, Jakub Krassowski, [ ... ], Piotr Paneth

Multivariate random forest prediction of poverty and malnutrition prevalence

Chris Browne, David S. Matteson, [ ... ], Christopher B. Barrett

Fishing trip cost modeling using generalized linear model and machine learning methods – A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis

Hing Ling Chan, Minling Pan

HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation

Xiaolong Hu, Liejun Wang, Shuli Cheng, Yongming Li

Random forest-integrated analysis in AD and LATE brain transcriptome-wide data to identify disease-specific gene expression

Xinxing Wu, Chong Peng, Peter T. Nelson, Qiang Cheng

Pneumonia detection in chest X-ray images using an ensemble of deep learning models

Rohit Kundu, Ritacheta Das, [ ... ], Ram Sarkar

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

Katharina Löffler, Tim Scherr, Ralf Mikut

Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance

Shuang Liu, Haiye Yu, [ ... ], Lei Zhang

Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study

Jae-Geum Shim, Kyoung-Ho Ryu, [ ... ], Jin Hee Ahn

Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping

Rubi Quiñones, Francisco Munoz-Arriola, Sruti Das Choudhury, Ashok Samal

Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research

Noah Grodzinski, Ben Grodzinski, Benjamin M. Davies

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Joe G. Greener, David T. Jones

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