8.473 hasil untuk machine learning
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Using machine learning and big data to explore the drug resistance landscape in HIV
…to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs. We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a…
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Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches
…types is also untested for consumer-prey interactions. Here, we used two machine-learning techniques (random forest and k-nearest neighbour; KNN) to test whether we could accurately predict empirically-observed quantitative host-parasitoid networks using trait and phylogenetic information. Then, we…
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Understanding importance of clinical biomarkers for diagnosis of anxiety disorders using machine learning models
…that can diagnose the types of anxiety disorders. In this study, we apply machine learning (ML) techniques to understand the importance of a set of biomarkers with four types of anxiety disorders—Generalized Anxiety Disorder (GAD), Agoraphobia (AP), Social Anxiety Disorder (SAD) and Panic Disorder…
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A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis
…we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the…
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Analysis of COVID-19 vaccine adverse event using language model and unsupervised machine learning
…their dimensionality. We also clustered symptoms by using unsupervised machine learning and analyzed the characteristics of each symptom cluster. Lastly, to discover any association rules among adverse events, we used a data mining approach. The frequency of adverse events was higher for women than…
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Use of machine learning to examine disparities in completion of substance use disorder treatment
The objective of this work is to examine disparities in the completion of substance use disorder treatment in the U.S. Our data is from the Treatment Episode Dataset Discharge (TEDS-D) datasets from the U.S. Substance Abuse and Mental Health Services Administration (SAMHSA) for 2017–2019. We apply…
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Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation
…target histone marks or transcription factors. Results: Imputations using machine learning models trained for each single cell, each ChIP protein target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene identification on real human data. Results on…
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Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India
…their own limitations in terms of assumptions. Methods: In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their…
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Machine learning based skin lesion segmentation method with novel borders and hair removal techniques
…image enhancement method and segments lesion from it using machine learning technique namely, GrabCut method. The proposed method was tested on PH2 and ISIC 2018 datasets containing 200 images each and its accuracy was measured with two evaluation metrics, i.e., Jaccard index, and Dice index. The…
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Application of machine learning for multi-community COVID-19 outbreak predictions with wastewater surveillance
…of wastewater from different sewersheds. We proposed exploiting machine learning and deep learning techniques, which are supported by the growing WBE data. In this article, we reviewed the existing predictive models, among which the emerging machine learning/deep learning models showed great…
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Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning
…with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality. Materials and methods: We utilized electronic health record data from 34,575 patients with a diagnosis of…
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Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning
…miss out on information which is not obvious to the human eye. The machine learning (ML) community has recently proposed new data analysis methods which by uncovering subtle features in images obtain excellent predictive accuracy. Scientific credibility demands however that results are…
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Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19
…medical resources worldwide. Objectives: To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. Methods: Two cohorts were used for the two…
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Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods
…with rifampin-resistant TB. Methods and findings: We assessed three machine learning-based models (logistic regression, neural network, and random forest) using information from 540 patients with rifampicin-resistant TB, diagnosed using Xpert MTB/RIF and notified in the Republic of Moldova between…
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Artificial intelligence and machine learning in mobile apps for mental health: A scoping review
…published in English since 2014 that evaluate artificial intelligence- or machine learning-enabled mobile apps for mental health support. Two reviewers collaboratively screened references (MMI and EM), selected studies for inclusion based on the eligibility criteria and extracted the data (MMI and…
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Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods
…not necessarily optimized for target conditions. We investigated whether machine learning methods can supplement researchers’ knowledge of target conditions in building CBAs. Retrospective cohort study using a claims database combined with annual health check-up results of employees’ health…
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Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks
…can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed…
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Developmental gene regulatory network connections predicted by machine learning from gene expression data alone
Gene regulatory network (GRN) inference can now take advantage of powerful machine learning algorithms to complement traditional experimental methods in building gene networks. However, the dynamical nature of embryonic development–representing the time-dependent interactions between thousands of…
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Crop loss identification at field parcel scale using satellite remote sensing and machine learning
…study investigates the feasibility of using satellite images to train machine learning (ML) models to classify agricultural field parcels into those with and without crop loss. The reference data for this study was provided by Finnish Food Authority (FFA) containing crop loss information of…
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App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning
…in "Dados do Bem," a Brazilian app-based symptom tracker. We applied machine learning techniques and provided a SARS-CoV-2 infection risk map of Rio de Janeiro city. Results: From April 28 to July 16, 2020, 337,435 individuals registered their symptoms through the app. Of these, 49,721 participants…
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