8.473 hasil untuk machine learning
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Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning
…IC50 values, we resort to a combination of atomistic modeling and machine learning. For each antibody/virus complex, an all-atoms model is built using the amino acid sequence and a known structure of a related complex. Then a series of descriptors are derived from the atomistic models, and these…
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Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma
Objective: Machine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to assess risk and dynamically identify…
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Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning
…is monitored using a commercially available wearable sensor. We show that machine learning can be used to differentiate children with an internalizing diagnosis from controls with 81% accuracy (67% sensitivity, 88% specificity). We provide a detailed description of the modeling methodology used to…
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A machine learning approach of predicting high potential archers by means of physical fitness indicators
k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers…
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Supervised machine learning for automated classification of human Wharton’s Jelly cells and mechanosensory hair cells
…those which were pertinent to classification were identified. Different machine learning models were then developed, some using all extracted data and some using only certain features. To evaluate model performance, the area under the curve (AUC) of the receiver operating characteristic curve was…
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Accurate spatiotemporal mapping of drug overdose deaths by machine learning of drug-related web-searches
…the reported ODD rates and drug-related search terms for 2004–2017. A machine learning model (Extremely Random Forest) was developed to produce yearly ODD estimates at state and county levels, as well as monthly estimates at state level. Regarding the total number of ODDs per year, DOVE’s error was…
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Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning
…human ventrolateral prefrontal cortex (VLPFC) and analyse the data with machine learning. We investigated effects over an adjacent region, the dorsolateral prefrontal cortex (DLPFC). In total, we analyzed data from 97 participants after exclusions. We found weak or absent effects over the VLPFC and…
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Essential gene prediction using limited gene essentiality information–An integrative semi-supervised machine learning strategy
Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However, currently available ML pipelines perform poorly for organisms with limited experimental data. The…
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A machine learning approach to predict ethnicity using personal name and census location in Canada
…research and interventions. Automated ethnicity classification using machine learning has shown potential to address this data gap but its performance in Canada is largely unknown. This study conducted a large-scale machine learning framework to predict ethnicity using a novel set of name and…
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A systematic review of machine learning models for predicting outcomes of stroke with structured data
Background and purpose: Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing…
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Machine learning utilising spectral derivative data improves cellular health classification through hyperspectral infra-red spectroscopy
…between 2500–3500 nm were collected using a portable prototype device. Machine learning algorithms, in the form of clustering, have been performed on a variety of pre-processing data types including ‘raw’ unprocessed, smoothed resampling, background subtracted and spectral derivative. The resulting…
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A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus
…of full-length HCV genomes, and applied these variants to various machine-learning algorithms to evaluate a preliminary predictive model. HCV genomic RNA was extracted from serum from 173 patients (109 with subsequent sustained virological response [SVR] and 64 without) before DAA treatment. HCV…
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Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys
…is to determine the efficiency of some of these alternatives, involving Machine Learning (ML) classification algorithms. PSA is applied in two simulation scenarios, representing situations commonly found in online surveys, using logistic regression and ML models for propensity estimation. The…
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LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes
…computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including information about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver…
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Pragmatics, Theory of Mind and executive functions in schizophrenia: Disentangling the puzzle using machine learning
…the results are inconclusive and this relationship is still unclear. Machine learning (ML) provides novel opportunities for studying complex relationships among phenomena and representing causality among multiple variables. The present research explored the potential of applying ML, specifically…
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Myosoft: An automated muscle histology analysis tool using machine learning algorithm utilizing FIJI/ImageJ software
Methods: Muscle sections were stained for cell boundary (laminin) and myofiber type (myosin heavy chain isoforms). Myosoft, running in the open access software platform FIJI (ImageJ), was used to analyze myofiber size and type in transverse sections of entire gastrocnemius/soleus muscles. Results…
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The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis
…is high, detailing donor maintenance-related (DMR) variables and using machine learning (ML) methods beyond the traditional regression-based models. Methods: A total of 443 brain dead deceased donor kidney transplants (KT) from two Brazilian centers were retrospectively analyzed and the following…
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Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification
…pig snout and tongue. The recordings were used to train a support vector machine to identify and classify the different skin and tissue types. Results: The resulting EWDRS curves for each skin and tissue type had a unique profile. The support vector machine was able to classify each skin and tissue…
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Technology opportunity discovery by structuring user needs based on natural language processing and machine learning
…values of needs are calculated through graph-based semi-supervised learning. Finally, the needs of the user are classified in resolving the problem of vacant technology to discover technology opportunity. Then, an R&D strategy of each opportunity is suggested based on opinion triggers, patent…
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A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer’s disease
…(LM) I and II, and the Boston Naming Test. Furthermore, we trained a machine-learning algorithm that validates the clinical relevance of the independent linguistic battery for differentiating between patients with MCI-AD and cognitive healthy control individuals. Our EFA identified ten linguistic…
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