AIMC Topic: Machine Learning

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A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

Journal of proteome research Apr 6, 2018
Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary b...
Algorithms Time Factors Proteomics Support Vector Machine Machine Learning Software Databases, Protein
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Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings.

IEEE journal of biomedical and health informatics Apr 5, 2018
Complexity, costs, and waiting list issues demand a simplified alternative for sleep apnea-hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO) carries useful information about SAHS and can be easily acquired from overnight ox...
Oximetry Humans Home Care Services Machine Learning Severity of Illness Index Neural Networks, Computer Signal Processing, Computer-Assisted Sleep Apnea Syndromes Adult
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Personalized response generation by Dual-learning based domain adaptation.

Neural networks : the official journal of the International Neural Network Society Apr 5, 2018
Open-domain conversation is one of the most challenging artificial intelligence problems, which involves language understanding, reasoning, and the utilization of common sense knowledge. The goal of this paper is to further improve the response gener...
Artificial Intelligence Machine Learning Humans Databases, Factual Multilingualism Language Learning
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Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping.

Journal of environmental management Apr 5, 2018
In this research, eight individual machine learning and statistical models are implemented and compared, and based on their results, seven ensemble models for flood susceptibility assessment are introduced. The individual models included artificial n...
ROC Curve Models, Statistical Iran Floods Machine Learning Forecasting
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MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae.

Metabolic engineering Apr 5, 2018
Facing boosting ability to construct combinatorial metabolic pathways, how to search the metabolic sweet spot has become the rate-limiting step. We here reported an efficient Machine-learning workflow in conjunction with YeastFab Assembly strategy (M...
Neural Networks, Computer Saccharomyces cerevisiae Machine Learning Metabolic Engineering Models, Biological Indoles
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Transcriptomics and machine learning predict diagnosis and severity of growth hormone deficiency.

JCI insight Apr 5, 2018
BACKGROUND: The effect of gene expression data on diagnosis remains limited. Here, we show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and rando...
Gene Regulatory Networks Polymorphism, Single Nucleotide Predictive Value of Tests Datasets as Topic Principal Component Analysis Healthy Volunteers Child, Preschool Child ROC Curve Adolescent Human Growth Hormone Growth Disorders Biomarkers Female Male Transcriptome Case-Control Studies Humans Severity of Illness Index Machine Learning Gene Expression Profiling
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Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer's Disease Tests: A Multivariate and Machine Learning Study.

Dementia and geriatric cognitive disorders Apr 4, 2018
BACKGROUND: The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patients with Alzheimer's dementia. Mild cognitive impairment (MCI) has received attention as a pre-dementi...
Translations Mental Recall Verbal Behavior Support Vector Machine Alzheimer Disease Neuropsychological Tests Aged, 80 and over Cross-Sectional Studies Male Female Cognitive Dysfunction Humans Middle Aged Reproducibility of Results Socioeconomic Factors Neural Networks, Computer Thailand Aged Machine Learning
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Word embeddings quantify 100 years of gender and ethnic stereotypes.

Proceedings of the National Academy of Sciences of the United States of America Apr 3, 2018
Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we de...
Machine Learning Language Female Male Humans Sexism History, 20th Century Religion Minority Groups Occupations Stereotyping United States History, 21st Century Racism Newspapers as Topic Culture Social Change Internet Ethnicity
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Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder.

Scientific reports Apr 3, 2018
Electroconvulsive therapy (ECT) is one of the most effective treatments for major depression disorder (MDD). ECT can induce neurogenesis and synaptogenesis in hippocampus, which contains distinct subfields, e.g., the cornu ammonis (CA) subfields, a g...
Male Depressive Disorder, Major Treatment Outcome Organ Size Machine Learning Female Adult Hippocampus Electroconvulsive Therapy Humans
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Statistics versus machine learning.

Nature methods Apr 3, 2018
Machine Learning Models, Genetic Data Interpretation, Statistical Gene Expression Regulation
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