AIMC Topic: Linear Models

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Artificial intelligence automates and augments baseline impedance measurements from pH-impedance studies in gastroesophageal reflux disease.

Journal of gastroenterology Nov 5, 2020
BACKGROUND: Artificial intelligence (AI) has potential to streamline interpretation of pH-impedance studies. In this exploratory observational cohort study, we determined feasibility of automated AI extraction of baseline impedance (AIBI) and evaluat...
Esophageal pH Monitoring Proof of Concept Study Gastroesophageal Reflux Proton Pump Inhibitors Middle Aged Case-Control Studies ROC Curve Electric Impedance Linear Models Treatment Outcome Female Feasibility Studies Aged Adult Male Humans Artificial Intelligence Prospective Studies
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Prediction of cancer dependencies from expression data using deep learning.

Molecular omics Nov 2, 2020
Detecting cancer dependencies is key to disease treatment. Recent efforts have mapped gene dependencies and drug sensitivities in hundreds of cancer cell lines. These data allow us to learn for the first time models of tumor vulnerabilities and apply...
Computational Biology Gene Expression Profiling Drug Resistance, Neoplasm Humans Transcriptome Gene Expression Regulation, Neoplastic Linear Models Neural Networks, Computer Deep Learning Neoplasms
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Discrimination of alcohol dependence based on the convolutional neural network.

PloS one Oct 27, 2020
In this paper, a total of 20 sites of single nucleotide polymorphisms (SNPs) on the serotonin 3 receptor A gene (HTR3A) and B gene (HTR3B) are used for feature fusion with age, education and marital status information, and the grid search-support vec...
Male Alcoholism Humans Educational Status Linear Models Middle Aged Support Vector Machine Neural Networks, Computer Young Adult Age Factors Female Adult Marital Status Genotype
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A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues.

Molecules (Basel, Switzerland) Oct 14, 2020
Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. Demersal fish species, such as turbot (), are acce...
Environmental Monitoring Water Pollutants, Chemical Liver Europe Machine Learning Animals Metals, Heavy Flatfishes Bioaccumulation Environmental Biomarkers Muscle, Skeletal Nonlinear Dynamics Linear Models
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Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent.

BMC bioinformatics Sep 15, 2020
BACKGROUND: Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalization approaches are often the methods of choice. They are especially useful...
Linear Models Machine Learning Algorithms Humans
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Does including machine learning predictions in ALS clinical trial analysis improve statistical power?

Annals of clinical and translational neurology Aug 30, 2020
OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease which leads to progressive muscle weakness and eventually death. The increasing availability of large ALS clinical trial datasets have generated much interest in developing...
Databases, Factual Disease Progression Severity of Illness Index Amyotrophic Lateral Sclerosis Neurodegenerative Diseases Humans Linear Models Machine Learning
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Improving depth-of-interaction resolution in pixellated PET detectors using neural networks.

Physics in medicine and biology Aug 27, 2020
Parallax error is a common issue in high-resolution preclinical positron emission tomography (PET) scanners as well as in clinical scanners that have a long axial field of view (FOV), which increases estimation uncertainty of the annihilation positio...
Positron-Emission Tomography Linear Models Calibration Uncertainty Humans Neural Networks, Computer Image Processing, Computer-Assisted
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Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation.

Neural computation Aug 14, 2020
In this letter, we study an active learning problem for maximizing an unknown linear function with high-dimensional binary features. This problem is notoriously complex but arises in many important contexts. When the sampling budget, that is, the num...
Bayes Theorem Supervised Machine Learning Humans Confidence Intervals Linear Models
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A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data.

Genes Aug 12, 2020
DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylate...
DNA Methylation Epigenesis, Genetic Genetic Variation Gene Expression Regulation, Neoplastic Gene Ontology Deep Learning Databases, Genetic Linear Models ROC Curve Computational Biology Humans Neoplasms Algorithms Gene Expression Profiling Biomarkers, Tumor
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Accurate detection of spontaneous seizures using a generalized linear model with external validation.

Epilepsia Aug 6, 2020
OBJECTIVE: Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizur...
Electrocorticography Kainic Acid Excitatory Amino Acid Agonists Disease Models, Animal Reproducibility of Results Area Under Curve Linear Models Animals Seizures Electroencephalography Epilepsies, Partial Signal Processing, Computer-Assisted ROC Curve Machine Learning Rats
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