AIMC Topic: Regression Analysis

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Regression-based machine-learning approaches to predict task activation using resting-state fMRI.

Human brain mapping
Resting-state fMRI has shown the ability to predict task activation on an individual basis by using a general linear model (GLM) to map resting-state network features to activation z-scores. The question remains whether the relatively simplistic GLM ...

Using satellite-measured relative humidity for prediction of Metisa plana's population in oil palm plantations: A comparative assessment of regression and artificial neural network models.

PloS one
Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. T...

Bayesian additive regression trees and the General BART model.

Statistics in medicine
Bayesian additive regression trees (BART) is a flexible prediction model/machine learning approach that has gained widespread popularity in recent years. As BART becomes more mainstream, there is an increased need for a paper that walks readers throu...

FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou's Five-Step Rule.

International journal of molecular sciences
DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these met...

Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis.

IEEE transactions on medical imaging
Lung cancer is the leading cause of cancer deaths worldwide and early diagnosis of lung nodule is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accurate lung nodule benign-malignant class...

Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unpreceden...

A deep convolutional neural network for the estimation of gas chromatographic retention indices.

Journal of chromatography. A
A deep convolutional neural network was used for the estimation of gas chromatographic retention indices on non-polar (polydimethylsiloxane and polydimethyl(5%-phenyl) siloxane) stationary phases. The neural network can be used for candidate ranking ...

Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.

BMC bioinformatics
BACKGROUND: Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic...