113.116 hasil untuk machine learning
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Machine learning applications in RNA modification sites prediction
…This availability of transcriptomic datasets has led to increased use of machine learning based approaches in epitranscriptomics, particularly in identifying RNA modifications. In this review, we comprehensively explore machine learning based approaches used for the prediction of 11 RNA…
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Preventing dataset shift from breaking machine-learning biomarkers
Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population…
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Detection of child depression using machine learning methods
…severe consequences in the future. There has been no research employing machine learning (ML) approaches for depression detection among children and adolescents aged 4–17 years in a precisely constructed high prediction dataset, such as Young Minds Matter (YMM). As a result, our objective is to 1)…
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Recent Advances in Drug Repurposing Using Machine Learning
…validation. One particular area of computational repurposing is using machine learning. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. In addition, we will highlight several applications of…
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Machine Learning Technology-Based Heart Disease Detection Models
…concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain…
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Novel Cocrystals of Vonoprazan: Machine Learning-Assisted Discovery
…cocrystals of VPZ, we adopted an artificial neural network (ANN)-based machine learning model as a virtual screening tool that can guide selection of the most promising coformers for VPZ cocrystals. Experimental screening by liquid-assisted grinding (LAG) confirmed that 8 of 19 coformers selected…
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Voice in Parkinson's Disease: A Machine Learning Study
INTRODUCTION: Parkinson's disease (PD) is characterized by specific voice disorders collectively termed hypokinetic dysarthria. We here investigated voice changes by using machine learning algorithms, in a large cohort of patients with PD in different stages of the disease, OFF and ON therapy…
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Confidential machine learning on untrusted platforms: a survey
With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and…
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Creeping Bentgrass Yield Prediction With Machine Learning Models
…temperature into account, limiting its accuracy. This study developed machine-learning-based turf growth models using the random forest (RF) algorithm to estimate short-term turfgrass clipping yield. To build the RF model, a large set of variables were extracted as predictors including the 7-day…
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Facial Emotion Recognition Predicts Alexithymia Using Machine Learning
…of the present study was to propose a new screening tool that utilizes machine learning models based on the scores of facial emotion recognition task. METHOD: In a cross-sectional study, 55 students of the University of Tabriz were selected based on the inclusion and exclusion criteria and their…
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ECG-based machine-learning algorithms for heartbeat classification
…different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People’s Hospital (SPH) database, which consists of more than 10,000…
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Application of Machine Learning in Rheumatic Immune Diseases
…auxiliary diagnostic method for rheumatic immune disorders is critical. Machine learning overcomes the inefficiencies and volatility of human data processing, ushering in a revolution in artificial intelligence research. With the use of big data, machine learning-based application research on…
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Identifying essential genes across eukaryotes by machine learning
…from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers…
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A machine learning model for nowcasting epidemic incidence
Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data. This process is known in economics as nowcasting. We describe in this paper a simple random forest statistical model for nowcasting the COVID-19…
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Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning
Purpose: To predict central serous chorioretinopathy (CSC) recurrence 3 and 6 months after laser treatment by using machine learning. Methods: Clinical and imaging features of 461 patients (480 eyes) with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The ZOC…
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DermIA: Machine Learning to Improve Skin Cancer Screening
…of up to 98.2% on their premium version. As artificial intelligence and machine learning become a more integral part of our society, this mobile application has the potential to revolutionize skin cancer surveillance. Application Specs Application name: DermIA Analyze Skin Cancer with your camera…
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Drug delivery: Experiments, mathematical modelling and machine learning
…we propose to combine real experiments, numerical simulation, and Machine Learning (ML) based on Artificial Neural Networks (ANN), aiming at a reliable identification of the physical model factors, e.g. the killing action of the drug. To this purpose, we exploit the employed mathematical-numerical…
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Machine learning-aided protein identification from multidimensional signatures
…molecular biology, and in medical diagnostics. Here, we present a macine learning aided microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a…
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Machine Learning Strategies When Transitioning between Biological Assays
[Image: see text] Machine learning is widely used in drug development to predict activity in biological assays based on chemical structure. However, the process of transitioning from one experimental setup to another for the same biological endpoint has not been extensively studied. In a…
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Machine learning in the prediction of cancer therapy
…overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response…
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