8.473 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning
…introduced. Here we introduce a new method, S/HIC, which uses supervised machine learning to precisely infer the location of both hard and soft selective sweeps. We show that S/HIC has unrivaled accuracy for detecting sweeps under demographic histories that are relevant to human populations, and…
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Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming
…(http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree…
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Effective injury forecasting in soccer with GPS training data and machine learning
…forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both…
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An improved machine learning pipeline for urinary volatiles disease detection: Diagnosing diabetes
…pipeline to diagnose diabetes on the basis of a simple urine sample using machine learning classifiers. We present results for data generated from a case-control study of 115 urine samples, collected from 72 type II diabetic patients, with 43 healthy volunteers as negative controls. The resulting…
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Predicting residential structures from open source remotely enumerated data using machine learning
…these into two classes, “sprayable” and “not-sprayable”. Ensemble machine learning, using building characteristics such as size, shape and proximity to neighbouring features, was then used to form a model to predict which of these 2 classes every building in these two countries fell into. Results…
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Artikel Jurnal Teks lengkap
Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles
The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs…
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Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets
…affect the outcome (e.g. abundance of the studied organisms). Ensemble machine learning methods such as random forests offer powerful alternatives. We propose that analysis of small radioecological data sets by GLMs and/or machine learning can be made more informative by using the following…
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Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach
…anxiety disorders, in 2 to 5 year old children. In this paper, we apply machine-learning tools to already collected PAPA data from two large community studies to identify sub-sets of PAPA items that could be developed into an efficient, reliable, and valid screening tool to assess a young child’s…
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A Generalizable Brain-Computer Interface (BCI) Using Machine Learning for Feature Discovery
This work describes a generalized method for classifying motor-related neural signals for a brain-computer interface (BCI), based on a stochastic machine learning method. The method differs from the various feature extraction and selection techniques employed in many other BCI systems. The…
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Distillation of crop models to learn plant physiology theories using machine learning
…paper, crop models are distilled by CNN to evaluate the ability of deep learning to identify the plant physiology knowledge behind such crop models simply by learning. Due to difficulty in collecting big data on crop growth, a crop model was used to generate datasets. The generated datasets were…
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Artikel Jurnal Teks lengkap
Predicting inadequate postoperative pain management in depressed patients: A machine learning approach
…and therefore may reduce the effectiveness of prodrug opioids. We used a machine learning approach to identify patients prescribed a combination of SSRIs and prodrug opioids postoperatively and to examine the effect of this combination on postoperative pain control. Using EHR data from an academic…
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Non-invasive assessment of NAFLD as systemic disease—A machine learning perspective
…included in clinical routine. In the current study, we developed a novel machine learning approach to overcome the caveats of existing approaches. Methods: Non-invasive parameters were selected by an ensemble feature selection (EFS) from a retrospectively collected training cohort of 164 obese…
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Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation
…a large, prospective cohort study. Soo-Jin Kang and colleagues present a machine learning–based model for estimating the risk of ischemia resulting from a coronary stenosis. If prospectively validated, the tool may reduce the invasive nature of this diagnosis. Why was this study done?: What did the…
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Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning
…on a visual analogue scale. Following statistical group comparisons, machine learning methods including feature extraction and classification were applied to the fNIRS features to classify patients with tinnitus and controls and differentiate tinnitus at different severity levels. Resting state…
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Artikel Jurnal Teks lengkap
A novel riboswitch classification based on imbalanced sequences achieved by machine learning
…available at https://github.com/Seasonsling/riboswitch. Author summary: Machine learning application has been used in many ways in bioinformatics and computational biology. Its use in riboswitch classification is still limited. Existing attempts showed challenges due to imbalanced sequences…
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Artikel Jurnal Teks lengkap
Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients
Opioids play a critical role in acute postoperative pain management. Our objective was to develop machine learning models to predict postoperative opioid requirements in patients undergoing ambulatory surgery. To develop the models, we used a perioperative dataset of 13,700 patients (≥ 18 years)…
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Artikel Jurnal Teks lengkap
Evolution and impact of bias in human and machine learning algorithm interaction
Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been receiving data from the general population in the form of labeling, annotations, etc. The result is that algorithms are subject to bias that is born…
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Artikel Jurnal Teks lengkap
The proximity of ideas: An analysis of patent text using machine learning
This paper introduces a measure of the proximity in ideas using unsupervised machine learning. Knowledge transfers are considered a key driving force of innovation and regional economic growth. I explore knowledge relationships by deriving vector space representations of a patent’s abstract text…
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Artikel Jurnal Teks lengkap
Comparing machine learning with case-control models to identify confirmed dengue cases
…prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have…
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Artikel Jurnal Teks lengkap
A machine learning approach to predict pancreatic islet grafts rejection versus tolerance
The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid…
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