8.473 hasil untuk machine learning
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Machine learning approach to single nucleotide polymorphism-based asthma prediction
Machine learning (ML) is poised as a transformational approach uniquely positioned to discover the hidden biological interactions for better prediction and diagnosis of complex diseases. In this work, we integrated ML-based models for feature selection and classification to quantify the risk of…
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Predicting Meridian in Chinese traditional medicine using machine learning approaches
…classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution…
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Machine learning prediction of emesis and gastrointestinal state in ferrets
…in recording myoelectric signals. It also examines the potential for machine learning algorithms to predict functional states, such as retching and emesis, from GI signal features. Studies were performed using ferrets, a gold standard model for emesis testing. Our results include simultaneous…
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Validation pipeline for machine learning algorithm assessment for multiple vendors
A standardized objective evaluation method is needed to compare machine learning (ML) algorithms as these tools become available for clinical use. Therefore, we designed, built, and tested an evaluation pipeline with the goal of normalizing performance measurement of independently developed…
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Identification of high-risk COVID-19 patients using machine learning
…of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data…
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Interpretable machine learning for high-dimensional trajectories of aging health
…and medical background information. We combine techniques of modern machine learning with an interpretable interaction network, where health variables are coupled by explicit pair-wise interactions within a stochastic dynamical system. Our dynamic joint interpretable network (DJIN) model is…
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Machine learning modeling of family wide enzyme-substrate specificity screens
…their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes…
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Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9
…remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical…
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Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia
…unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s…
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Machine learning-based prediction of intraoperative hypoxemia for pediatric patients
…is considered crucial by clinicians. We aimed to develop and validate a machine learning model that can predict intraoperative hypoxemia events 1 min ahead in children undergoing general anesthesia. Methods: This retrospective study used prospectively collected intraoperative vital signs and…
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Review of machine learning methods for RNA secondary structure prediction
…the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a…
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Crash severity analysis of vulnerable road users using machine learning
…are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users—pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with…
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Use of machine learning to identify risk factors for insomnia
…of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. Design, setting, and participants: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted…
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Graph-based machine learning improves just-in-time defect prediction
…changes are introduced has proven challenging, and using traditional machine learning (ML) methods to make these determinations seems to have reached a plateau. In this work, we build contribution graphs consisting of developers and source files to capture the nuanced complexity of changes required…
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Computational method for aromatase-related proteins using machine learning approach
…need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like…
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Machine learning-based investigation of the cancer protein secretory pathway
…a dual approach involving differential expression analysis and machine learning to identify PSP genes whose expression was associated with key tumor characteristics: mutation of p53, cancer status, and tumor stage. Eight different machine learning algorithms were included in the analysis to enable…
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Machine learning-based predictive modeling of depression in hypertensive populations
We aimed to develop prediction models for depression among U.S. adults with hypertension using various machine learning (ML) approaches. Moreover, we analyzed the mechanisms of the developed models. This cross-sectional study included 8,628 adults with hypertension (11.3% with depression) from the…
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An evolutionary machine learning algorithm for cardiovascular disease risk prediction
…a well-designed GA, and finally, the results were compared with classic machine learning (ML) and statistical methods. Results: A number of risk scores such as the WHO, and PARS models were utilized as the baseline for comparison due to their similar chart-based models. The Framingham and PROCAM…
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A machine learning based exploration of COVID-19 mortality risk
…planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients’ day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive…
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PHOTONAI—A Python API for rapid machine learning model development
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support…
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