113.116 hasil untuk machine learning
Menampilkan 721–740 · 0,42 detik · RSS
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Artikel Jurnal Teks lengkap
Energy-free machine learning force field for aluminum
We used the machine learning technique of Li et al. (PRL 114, 2015) for molecular dynamics simulations. Atomic configurations were described by feature matrix based on internal vectors, and linear regression was used as a learning technique. We implemented this approach in the LAMMPS code. The…
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MLACP: machine-learning-based prediction of anticancer peptides
…to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and…
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Bypassing the Kohn-Sham equations with machine learning
Last year, at least 30,000 scientific papers used the Kohn–Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the…
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Classifying kinase conformations using a machine learning approach
…active site geometry. RESULTS: We utilize an unbiased informatics based machine learning approach to classify all eukaryotic protein kinase conformations deposited in the PDB. We show that the orientation of the activation segment, measured by φ, ψ, χ1, and pseudo-dihedral angles more accurately…
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Machine learning for epigenetics and future medical applications
Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and…
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Modeling Music Emotion Judgments Using Machine Learning Methods
Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML) methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of…
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An application of machine learning to haematological diagnosis
Quick and accurate medical diagnoses are crucial for the successful treatment of diseases. Using machine learning algorithms and based on laboratory blood test results, we have built two models to predict a haematologic disease. One predictive model used all the available blood test parameters and…
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Machine-Learning Techniques Applied to Antibacterial Drug Discovery
…to pre-clinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field.
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Machine Learning Techniques for Arterial Pressure Waveform Analysis
…overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the…
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PCP-ML: Protein characterization package for machine learning
BACKGROUND: Machine Learning (ML) has a number of demonstrated applications in protein prediction tasks such as protein structure prediction. To speed further development of machine learning based tools and their release to the community, we have developed a package which characterizes several…
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Machine learning applications in cancer prognosis and prediction
…the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex…
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Machine learning approach for pooled DNA sample calibration
…and 1.0, and address a limited set of error types. We propose a novel machine learning method to address the limitations identified. RESULTS: The approach is tested on SNPs genotyped with the Sequenom iPLEX platform and compared to existing state of the art calibration methods. The new method is…
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Investigating Predictors of Cognitive Decline Using Machine Learning
OBJECTIVES: Genetic risks for cognitive decline are not modifiable; however their relative importance compared to modifiable factors is unclear. We used machine learning to evaluate modifiable and genetic risk factors for Alzheimer’s disease (AD), to predict cognitive decline. METHODS: Health and…
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Machine learning methods in psychiatry: a brief introduction
Machine learning (ML) techniques have been widely used to address mental health questions. We discuss two main aspects of ML in psychiatry in this paper, that is, supervised learning and unsupervised learning. Examples are used to illustrate how ML has been implemented in recent mental health…
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Machine learning–based identification of hip arthroplasty designs
BACKGROUND: The purposes of this study were to develop a machine learning–based implant recognition program and to verify its accuracy. METHODS: Postoperative anteroposterior (AP) X-rays (≥300 dpi) were collected of patients who underwent total hip arthroplasty. X-rays with a wire or plate added…
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Analyzing collective motion with machine learning and topology
We use topological data analysis and machine learning to study a seminal model of collective motion in biology [M. R. D’Orsogna et al., Phys. Rev. Lett. 96, 104302 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective…
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A machine-learning approach to predict postprandial hypoglycemia
…is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. METHODS: We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day…
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