AIMC Topic:
Supervised Machine Learning

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Semi-supervised learning of a nonnative phonetic contrast: How much feedback is enough?

Attention, perception & psychophysics May 1, 2019
Semi-supervised learning refers to learning that occurs when feedback about performance is provided on only a subset of training trials. Algorithms for semi-supervised learning are popular in machine learning because of their minimal reliance on labe...
Male Supervised Machine Learning Task Performance and Analysis Phonetics Female Young Adult Feedback Humans Algorithms Knowledge of Results, Psychological
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Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study.

The Journal of antimicrobial chemotherapy Apr 1, 2019
BACKGROUND: Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood par...
Humans Algorithms Biomarkers Aged Aged, 80 and over Young Adult Adult Prognosis Clinical Decision-Making Female Disease Management Diagnostic Tests, Routine Infections Patient Admission Hematologic Tests ROC Curve Male Follow-Up Studies Decision Support Systems, Clinical Middle Aged Supervised Machine Learning Cohort Studies
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Machine learning in quantum spaces.

Nature Mar 1, 2019
Spatial Learning Machine Learning Supervised Machine Learning Physics
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Machine Learning to Predict Binding Affinity.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic ...
Proteins Cyclin-Dependent Kinase 2 Databases, Protein Molecular Dynamics Simulation HIV Protease Web Browser Supervised Machine Learning Humans Algorithms Ligands Machine Learning Molecular Docking Simulation Software Models, Statistical
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Development of a Supervised Learning Algorithm for Detection of Potential Disease Reemergence: A Proof of Concept.

Health security Jan 1, 2019
Infectious disease reemergence is an important yet ambiguous concept that lacks a quantitative definition. Currently, reemergence is identified without specific criteria describing what constitutes a reemergent event. This practice affects reproducib...
Disease Outbreaks Supervised Machine Learning Humans Algorithms Medical Informatics Communicable Diseases, Emerging
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Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
CITRUS is a supervised machine learning algorithm designed to analyze single cell data, identify cell populations, and identify changes in the frequencies or functional marker expression patterns of those populations that are significantly associated...
Biomarkers Single-Cell Analysis Mass Spectrometry Flow Cytometry Supervised Machine Learning Humans Algorithms
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PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely availa...
Pharmacogenomic Variants Supervised Machine Learning Drug Resistance, Neoplasm Information Storage and Retrieval Patient-Specific Modeling Antineoplastic Agents Software Cell Line, Tumor Databases, Factual Precision Medicine Humans Computational Biology Neoplasms Machine Learning
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A Drug-Target Network-Based Supervised Machine Learning Repurposing Method Allowing the Use of Multiple Heterogeneous Information Sources.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
Drug-target networks have an important role in pharmaceutical innovation, drug lead discovery, and recent drug repositioning tasks. Many different in silico approaches for the identification of new drug-target interactions have been proposed, many of...
Software Algorithms Drug Repositioning Supervised Machine Learning Drug Discovery Computational Biology Databases, Pharmaceutical
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Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
The cost of new drug development has been increasing, and repurposing known medications for new indications serves as an important way to hasten drug discovery. One promising approach to drug repositioning is to take advantage of machine learning (ML...
Gene Expression Regulation Transcriptome Databases, Pharmaceutical Deep Learning Genomics Machine Learning Workflow Computational Biology Algorithms Supervised Machine Learning Drug Repositioning Humans Reproducibility of Results Neural Networks, Computer Gene Expression Profiling
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Semisupervised category learning facilitates the development of automaticity.

Attention, perception & psychophysics Jan 1, 2019
In the human category of learning, learning is studied in a supervised, an unsupervised, or a semisupervised way. The rare human semisupervised category of learning studies all focus on early learning. However, the impact of the semisupervised catego...
Adult Male Adolescent Photic Stimulation Feedback, Psychological Reaction Time Supervised Machine Learning Learning Female Young Adult Humans
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