113,116 results for machine learning
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Machine learning-based prediction of glioma grading
…by subjectivity and poor reproducibility. This study aimed to develop a machine learning–based prediction model that integrates clinical and molecular characteristics to improve early glioma grading, thereby enhancing diagnostic accuracy and supporting individualized treatment strategies. METHODS…
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Koina: Democratizing machine learning for proteomics research
Recent developments in machine learning (ML) and deep learning have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML models are regularly published, the rate at which…
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Machine Learning for Protein Science and Engineering
Recent years have seen significant breakthroughs at the intersection of machine learning and protein science. Tools such as AlphaFold have revolutionized protein structure prediction. They are also enabling variant effect prediction and functional annotation of proteins, as well as opening up new…
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Machine learning-based penetrance of genetic variants
Accurate variant penetrance estimation is crucial for precision medicine. We constructed machine learning (ML) models for 10 diseases using 1,347,298 participants with electronic health records (EHRs), then applied them to an independent cohort with linked exome data. Resulting probabilities were…
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Machine Learning Phase Classification of Thermoelectric Materials
In this study, we employ a Support Vector Machine (SVM) model to efficiently classify the phases of thermoelectric (TE) alloys. While ab initio calculations and experiments have explored the phases of functional TE materials, the large variety of alloys makes these explorations time-consuming and…
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Machine learning predicts meter-scale laboratory earthquakes
In recent years, there has been a growing interest in utilizing machine learning (ML) to investigate the predictability of shear-slip failures, known as laboratory quakes, in centimeter-scale rock-friction experiments. However, the applicability of ML to larger-scale laboratory quakes and natural…
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Machine Learning-Based Transcriptomic Diagnosis of Periodontitis
…by subjectivity and low sensitivity. This study aimed to develop a machine learning (ML)-based diagnostic framework using transcriptomic data to enhance diagnostic accuracy and efficiency. METHODS: Transcriptomic datasets from 616 samples (452 periodontitis, 164 healthy controls) were retrieved…
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Prediction of stillbirth using machine learning methods
This study developed a machine learning model to predict stillbirth using retrospective data from 32,953 singleton pregnancies at multi-centers in South Korea. Variables were collected at baseline, E1 (before 13 weeks of pregnancy), and T0 (before 28 weeks of pregnancy). Each separate cohort (all…
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Incisional hernia prediction using machine learning models
…low or high Risk. There is no currently full peer-reviewed study in which Machine learning (ML) is applied for the prediction of IH, so the objective of this study was to develop a predictive model of IH based on different ML techniques. METHODS: Retrospective cohort study type of diagnostic tests…
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Unraveling sperm kinematic heterogeneity with machine learning
…for analysis using conventional statistical methods. Advances in machine learning and deep learning have addressed these limitations by leveraging motility parameters and trajectory representations for automated classification and clustering of motility patterns. These methods, including supervised…
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Machine Learning-Assisted Quantification of Organelle Abundance
…potential bias and variability. To address this issue, we present a machine learning–assisted algorithm that allows for the quantification of organelle density using the open-source Fiji platform and WEKA segmentation. Our method enables the automated quantification of organelle number, area, and…
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Advances in Machine Learning for Wearable Sensors
Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and…
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Automation of Rietveld refinement through machine learning
Rietveld refinement is a widely used technique for determining crystal structures from powder X-ray diffraction data. Despite its broad applicability, the refinement process often requires substantial manual effort and expert knowledge, which limits the pace of materials research. Here, we…
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Using Machine Learning to Predict Early Preparation of Pharmacy Prescriptions at PSMMC - a Comparison of Four Machine Learning Algorithms
…OBJECTIVE: A healthcare framework has been constructed utilizing a machine learning approach to construct an early predicting preparation model of pharmacy prescriptions and the worthiness of changing the outpatient pharmacy workflow. METHODS: Data sets were retrieved between Januarys and June 2019…
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The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering(*)
Objective. Machine learning’s (MLs) ability to capture intricate patterns makes it vital in neural engineering research. With its increasing use, ensuring the validity and reproducibility of ML methods is critical. Unfortunately, this has not always been the case in practice, as there have been…
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A Machine Learning Approach to Predicting Mortality Risk in Chemotherapy-Treated Lung Cancer: Machine Learning Model Development and Validation
…management of this population, this study developed a multivariate machine learning (ML) model to assess all-cause mortality risk in chemotherapy-treated patients with lung cancer. METHODS: This study retrospectively recruited 1278 postchemotherapy patients with lung cancer from Guangzhou Chest…
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Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development
…To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms…
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Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach
BACKGROUND: Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical…
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