AIMC Topic: Machine Learning

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Machine Learning and Integrative Analysis of Biomedical Big Data.

Genes Jan 28, 2019
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., gen...
Computational Biology Animals Big Data Machine Learning Humans
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In silico prediction of chemical reproductive toxicity using machine learning.

Journal of applied toxicology : JAT Jan 27, 2019
Reproductive toxicity is an important regulatory endpoint in health hazard assessment. Because the in vivo tests are expensive, time consuming and require a large number of animals, which must be killed, in silico approaches as the alternative strate...
Machine Learning Computer Simulation Reproduction Animals Datasets as Topic Structure-Activity Relationship
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A fast kernel extreme learning machine based on conjugate gradient.

Network (Bristol, England) Jan 27, 2019
Kernel extreme learning machine (KELM) introduces kernel leaning into extreme learning machine (ELM) in order to improve the generalization ability and stability. But the Penalty parameter in KELM is randomly set and it has a strong impact on the per...
Generalization, Psychological Machine Learning Humans Neural Networks, Computer Algorithms Models, Theoretical
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Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters.

Journal of chemical information and modeling Jan 25, 2019
Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially "badly behaving compounds...
Protein Binding Proteins Models, Molecular Machine Learning ROC Curve Binding Sites Pharmaceutical Preparations Small Molecule Libraries High-Throughput Screening Assays Databases, Pharmaceutical
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Prediction of skin dose in low-kV intraoperative radiotherapy using machine learning models trained on results of in vivo dosimetry.

Medical physics Jan 25, 2019
PURPOSE: The purpose of this study was to implement a machine learning model to predict skin dose from targeted intraoperative (TARGIT) treatment resulting in timely adoption of strategies to limit excessive skin dose.
Breast Neoplasms Middle Aged Models, Statistical Aged, 80 and over Adult Intraoperative Care Mastectomy, Segmental In Vivo Dosimetry Organs at Risk Radiotherapy Dosage Skin Female Aged Machine Learning Humans Neural Networks, Computer
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Detection of Extended-Spectrum β-Lactamase-Producing Escherichia coli Using Infrared Microscopy and Machine-Learning Algorithms.

Analytical chemistry Jan 25, 2019
The spread of multidrug resistant bacteria has become a global concern. One of the most important and emergent classes of multidrug-resistant bacteria is extended-spectrum β-lactamase-producing bacteria (ESBL-positive = ESBL). Due to widespread and c...
Uropathogenic Escherichia coli Spectroscopy, Fourier Transform Infrared beta-Lactamases Microscopy Machine Learning Infrared Rays
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Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework.

International journal of environmental research and public health Jan 25, 2019
The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing r...
Automobile Driving Risk-Taking Machine Learning Models, Theoretical Forecasting Decision Trees Risk Assessment Humans Risk Factors Accidents, Traffic China
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A Semi-Automatic Annotation Approach for Human Activity Recognition.

Sensors (Basel, Switzerland) Jan 25, 2019
Modern smartphones and wearables often contain multiple embedded sensors which generate significant amounts of data. This information can be used for body monitoring-based areas such as healthcare, indoor location, user-adaptive recommendations and t...
Supervised Machine Learning Activities of Daily Living Human Activities Humans Machine Learning Algorithms
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Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis.

Genetics in medicine : official journal of the American College of Medical Genetics Jan 24, 2019
PURPOSE: Despite the successful progress next-generation sequencing technologies has achieved in diagnosing the genetic cause of rare Mendelian diseases, the current diagnostic rate is still far from satisfactory because of heterogeneity, imprecision...
Genetic Variation High-Throughput Nucleotide Sequencing Rare Diseases Mutation Genetic Testing Exome Software Phenotype Genotype Computational Biology Genomics Humans Machine Learning
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NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules.

Biomolecules Jan 24, 2019
Natural products (NPs) remain the most prolific resource for the development of smallmolecule drugs. Here we report a new machine learning approach that allows the identification of natural products with high accuracy. The method also generates simil...
Molecular Structure Small Molecule Libraries Biological Products Machine Learning
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