214.423 hasil untuk machine learning
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Machine Learning & Molecular Radiation Tumor Biomarkers
…advances in molecular profiling and computational biology including machine learning. However, the increasingly complex nature of the data generated from high-throughput and “omics” assays require careful selection of analytical strategies. Furthermore, the power of modern machine learning…
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Machine learning predictions from unpredictable chaos
…attractors. In this work, we introduce, for the first time, chaotic learning, a novel multiscale topological paradigm that enables accurate predictions from chaotic systems. We show that seemingly random and unpredictable chaotic dynamics counterintuitively offer unprecedented quantitative…
Juga tersedia di: PubMed Central
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Predicting abatacept retention using machine learning
BACKGROUND: The incorporation of machine learning is becoming more prevalent in the clinical setting. By predicting clinical outcomes, machine learning can provide clinicians with a valuable tool for refining precision medicine approaches and improving treatment outcomes. METHODS: This was a post…
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Machine learning vehicle fuel efficiency prediction
…decisions. Therefore, we propose a comprehensive framework that uses machine learning to predict fuel efficiency by integrating various vehicle information. The proposed method comprises a predictive model and analysis framework utilizing key vehicle attributes, such as fuel type, engine…
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Mapping Biomaterial Complexity by Machine Learning
…biomaterials. More recently, high-throughput experimentation coupled with machine learning methods has matured beyond expert users allowing scientists and engineers from diverse backgrounds to access these powerful data science tools. As a result, we now have the opportunity to strategically…
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Machine‐Learning‐Aided Advanced Electrochemical Biosensors
…biosensors by improving sensitivity, response, and portability. Machine learning (ML) integration with electrochemical biosensors is also gaining traction, being particularly promising for addressing challenges such as electrode fouling, interference from non‐target analytes, variability in testing…
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Machine learning in Alzheimer’s disease genetics
Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer’s disease (AD) to…
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Autophagy and Machine Learning: Unanswered Questions
…years, there has been a surge of studies utilizing state of the art machine learning (ML) tools to analyze and understand the roles of autophagy in various biological processes. We taxonomize ML techniques that are applicable in autophagy context, comprehensively review existing efforts in this…
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Machine learning applied to biocatalysis research
Machine learning (ML) is rapidly influencing the development of many research areas, including biocatalysis, the use of enzymes and living systems to mediate chemical reactions, often of pharmaceutical and industrial importance. To find out how can machine learning be applied to biocatalysis…
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Pediatric diabetes prediction using machine learning
…researchers are increasingly turning to big data analytics and machine learning (ML) methodologies. This study presents the development of an ML-based system aimed at predicting the likelihood of diabetes and classifying its various types. A novel dataset, termed Diabetes Types Dataset, was…
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Machine learning models predicting inpatient falls
…between January 2018 and July 2022, including 4245 fallers. Four dynamic machine learning models were developed using HD variables alone and in combination with socio-demographics, comorbidities, physiological measures, medications, and timeseries data updated at 8- and 24 h intervals. Among the…
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Task-Agnostic Machine-Learning-Assisted Inference
Machine learning (ML) is playing an increasingly important role in scientific research. In conjunction with classical statistical approaches, ML-assisted analytical strategies have shown great promise in accelerating research findings. This has also opened a whole field of methodological research…
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Interpretable Machine Learning for Survival Analysis
With the spread and rapid advancement of black box machine learning (ML) models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade. This is particularly relevant for survival analysis, where the…
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Mitigating Racial Bias in Machine Learning
When applied in the health sector, AI-based applications raise not only ethical but legal and safety concerns, where algorithms trained on data from majority populations can generate less accurate or reliable results for minorities and other disadvantaged groups.
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Trimodal machine learning based biometrics system
Biometrics-based authentication systems have recently been considered as one of the safest methods to secure our data or possessions. In the literature, the solutions based on one or two measurable traits are broadly described. There was also claim that more features than two can make the samples…
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SmartEM: machine-learning guided electron microscopy
…generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational…
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Interpretable machine learning for shoreline forecasting
Machine learning has revolutionized scientific modeling, providing breakthroughs in fields ranging from weather prediction to protein folding. However, its adoption in physics-based domains remains limited due to the lack of interpretability in traditional black-box models. In environmental…
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