AIMC Topic: Machine Learning

Clear Filters Showing 24291 to 24300 of 34417 articles

Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites.

Genomics, proteomics & bioinformatics Jan 11, 2019
As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms ...
Amino Acids Animals Amino Acid Sequence Deep Learning Machine Learning Lysine Malonates Protein Processing, Post-Translational Forecasting
View on PubMed DOI

Evolving Gaussian Process Autoregression Based Learning of Human Motion Intent Using Improved Energy Kernel Method of EMG.

IEEE transactions on bio-medical engineering Jan 10, 2019
Continuous human motion intent learning may be modeled using a Gaussian process (GP) autoregression based evolving system to cope with the unspecified and time-varying motion patterns. Electromyography (EMG) signals are the primary input. GP is used ...
Young Adult Adult Biomechanical Phenomena Machine Learning Algorithms Intention Humans Normal Distribution Exoskeleton Device Male Signal Processing, Computer-Assisted Electromyography
View on PubMed DOI

DeepNEU: cellular reprogramming comes of age - a machine learning platform with application to rare diseases research.

Orphanet journal of rare diseases Jan 10, 2019
BACKGROUND: Conversion of human somatic cells into induced pluripotent stem cells (iPSCs) is often an inefficient, time consuming and expensive process. Also, the tendency of iPSCs to revert to their original somatic cell type over time continues to ...
Humans Rett Syndrome Machine Learning Cellular Reprogramming Myocytes, Cardiac Fibroblasts Rare Diseases Cell Differentiation Induced Pluripotent Stem Cells Neurons
View on PubMed DOI

A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records.

BMC medical informatics and decision making Jan 10, 2019
BACKGROUND: Adverse drug events (ADEs) as well as other preventable adverse events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in the United States alone. Therefore, it is of paramount importance to reduce the ...
Medical Informatics Applications Drug-Related Side Effects and Adverse Reactions Data Mining Humans Electronic Health Records Machine Learning Hospitals Models, Statistical
View on PubMed DOI

EEG correlation at a distance: A re-analysis of two studies using a machine learning approach.

F1000Research Jan 10, 2019
In this paper, data from two studies relative to the relationship between the electroencephalogram (EEG) activities of two isolated and physically separated subjects were re-analyzed using machine-learning algorithms. The first dataset comprises the...
Electroencephalography Acoustic Stimulation Machine Learning Algorithms
View on PubMed DOI

Prediction of Recurrence after Transsphenoidal Surgery for Cushing's Disease: The Use of Machine Learning Algorithms.

Neuroendocrinology Jan 10, 2019
BACKGROUND: There are no reliable predictive models for recurrence after transsphenoidal surgery (TSS) for Cushing's disease (CD).
Data Mining Recurrence Pituitary ACTH Hypersecretion Male Models, Statistical Algorithms Retrospective Studies Female Risk Factors Young Adult Adult Humans Machine Learning
View on PubMed DOI

Computational prediction of diagnosis and feature selection on mesothelioma patient health records.

PloS one Jan 10, 2019
BACKGROUND: Mesothelioma is a lung cancer that kills thousands of people worldwide annually, especially those with exposure to asbestos. Diagnosis of mesothelioma in patients often requires time-consuming imaging techniques and biopsies. Machine lear...
Male Computational Biology Regression Analysis Humans Machine Learning Female Health Records, Personal Mesothelioma
View on PubMed DOI

A review on machine learning methods for in silico toxicity prediction.

Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews Jan 10, 2019
In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have bee...
Environmental Pollutants Quantitative Structure-Activity Relationship Machine Learning Computer Simulation Toxicity Tests Algorithms Support Vector Machine
View on PubMed DOI

Machine learning models for predicting endocrine disruption potential of environmental chemicals.

Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews Jan 10, 2019
We introduce here ML4Tox, a framework offering Deep Learning and Support Vector Machine models to predict agonist, antagonist, and binding activities of chemical compounds, in this case for the estrogen receptor ligand-binding domain. The ML4Tox mode...
Receptors, Estrogen Support Vector Machine Environmental Pollutants Toxicity Tests Quantitative Structure-Activity Relationship Endocrine Disruptors Machine Learning Computer Simulation Protein Binding
View on PubMed DOI

Computational Prediction of Human Disease- Associated circRNAs Based on Manifold Regularization Learning Framework.

IEEE journal of biomedical and health informatics Jan 9, 2019
The accumulating evidences regarding circular RNAs (circRNAs) indicate that they play crucial roles in a wide range of biological processes and participate in tumorigenesis and progression. The number of newly discovered circRNAs have increased drama...
Neoplasms Computational Biology Area Under Curve Coronary Artery Disease RNA, Circular Humans Machine Learning Algorithms
View on PubMed DOI
Popular Topics
Recent Journals