359.866 hasil untuk machine learning
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Machine learning and pre-medical education
Machine learning and artificial intelligence (AI)-driven technologies are contributing significantly to various facets of medicine and care management. It is likely that the next generation of healthcare professionals will be confronted with a series of innovations that are powered by AI, and they…
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Machine learning boosts three-dimensional bioprinting
…in improving the printing accuracy in the printing process. Data- driven machine learning algorithms with powerful predictive capabilities have natural advantages in behavior prediction and new model exploration. Combining machine learning algorithms with 3D bioprinting helps to find more efficient…
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Discovery of senolytics using machine learning
…Here, we report the discovery of three senolytics using cost-effective machine learning algorithms trained solely on published data. We computationally screened various chemical libraries and validated the senolytic action of ginkgetin, periplocin and oleandrin in human cell lines under various…
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Predicting mutational function using machine learning
…made to solve this problem using computational approaches, especially machine learning (ML). Here, we review the progress and achievements made in recent years in this field of research. We classify the computational models in two ways: one according to their prediction goals including protein…
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Electrostatic Embedding of Machine Learning Potentials
[Image: see text] This work presents a variant of an electrostatic embedding scheme that allows the embedding of arbitrary machine learned potentials trained on molecular systems in vacuo. The scheme is based on physically motivated models of electronic density and polarizability, resulting in a…
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Primer on Machine Learning in Electrophysiology
Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary…
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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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