214.423 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Swarm Learning for decentralized and confidential clinical machine learning
Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine(1,2). Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes(3). However, there is an increasing divide between what is…
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Applications of continual learning machine learning in clinical practice
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Introduction to Machine Learning, Neural Networks, and Deep Learning
PURPOSE: To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. METHODS: A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial…
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Research on learning achievement classification based on machine learning
Academic achievement is an important index to measure the quality of education and students’ learning outcomes. Reasonable and accurate prediction of academic achievement can help improve teachers’ educational methods. And it also provides corresponding data support for the formulation of education…
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Identifying protein complexes with fuzzy machine learning model
BACKGROUND: Many computational approaches have been developed to detect protein complexes from protein-protein interaction (PPI) networks. However, these PPI networks are always built from high-throughput experiments. The presence of unreliable interactions in PPI network makes this task very…
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Machine Learning Estimates of Natural Product Conformational Energies
Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor…
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GeneRIF indexing: sentence selection based on machine learning
…sentences describing a novel function. RESULTS: We have compared several learning algorithms and several features extracted or derived from MEDLINE sentences to determine if a sentence should be selected for GeneRIF indexing. Features are derived from the sentences or using mechanisms to augment…
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Fall Classification by Machine Learning Using Mobile Phones
…to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99%…
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Dana-Farber Repository for Machine Learning in Immunology
The immune system is characterized by high combinatorial complexity that necessitates the use of specialized computational tools for analysis of immunological data. Machine learning (ML) algorithms are used in combination with classical experimentation for the selection of vaccine targets and in…
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Early Prediction of Reading Disability using Machine Learning
This paper presents application of machine learning methods on a 356 sample dataset for early prediction of reading disability among first graders. A wide array of classifiers consisting of Support Vector Machines, Decision Trees (CART and C4.5), Linear Discriminant Analysis, k Nearest Neighbor and…
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Machine Learning: A Crucial Tool for Sensor Design
…and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides…
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Machine Learning Methods for Prediction of CDK-Inhibitors
…similarity search methods. In this work we have evaluated and developed machine learning methods for identification of CDKIs. We used different compositional features and evolutionary information in the form of PSSMs, from CDKIs and non-CDKIs for generating SVM and ANN classifiers. In the first…
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Image-based crystal detection: a machine-learning approach
…on the likelihood of containing crystalline material as perceived by a machine-learning algorithm. The system can be incorporated into existing crystallization-analysis pipelines, whereby specialists examine images as they normally would with the exception that the images appear in rank order…
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A Protein Classification Benchmark collection for machine learning
Protein classification by machine learning algorithms is now widely used in structural and functional annotation of proteins. The Protein Classification Benchmark collection () was created in order to provide standard datasets on which the performance of machine learning methods can be compared. It…
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APPLYING MACHINE LEARNING TECHNIQUES IN DETECTING BACTERIAL VAGINOSIS
There are several diseases which arise because of changes in the microbial communities in the body. Scientists continue to conduct research in a quest to find the catalysts that provoke these changes in the naturally occurring microbiota. Bacterial Vaginosis (BV) is a disease that fits the above…
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