AIMC Topic:
Bayes Theorem

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Detection of Left Ventricular Hypertrophy Using Bayesian Additive Regression Trees: The MESA.

Journal of the American Heart Association Mar 5, 2019
Background We developed a new left ventricular hypertrophy ( LVH ) criterion using a machine-learning technique called Bayesian Additive Regression Trees ( BART ). Methods and Results This analysis included 4714 participants from MESA (Multi-Ethnic S...
Bayes Theorem Ventricular Remodeling Hypertrophy, Left Ventricular Magnetic Resonance Imaging Decision Trees Predictive Value of Tests Aged, 80 and over Male Ventricular Function, Left Middle Aged Diagnosis, Computer-Assisted United States Electrocardiography Humans Machine Learning Female Reproducibility of Results Aged
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An account of in silico identification tools of secreted effector proteins in bacteria and future challenges.

Briefings in bioinformatics Jan 18, 2019
Bacterial pathogens secrete numerous effector proteins via six secretion systems, type I to type VI secretion systems, to adapt to new environments or to promote virulence by bacterium-host interactions. Many computational approaches have been used i...
Machine Learning Algorithms Databases, Factual Computer Simulation Humans Neural Networks, Computer Bacterial Proteins Models, Biological Virulence Markov Chains Computational Biology Support Vector Machine Bayes Theorem Bacterial Secretion Systems Host Microbial Interactions
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Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies.

Current medical imaging reviews Jan 1, 2019
BACKGROUND: Brain tumor is the leading cause of death worldwide. It is obvious that the chances of survival can be increased if the tumor is identified and properly classified at an initial stage. MRI (Magnetic Resonance Imaging) is one source of bra...
Magnetic Resonance Imaging Databases, Factual Humans Decision Trees Retrospective Studies Machine Learning Bayes Theorem Sensitivity and Specificity Algorithms Brain Neoplasms
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Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches.

Current pharmaceutical design Jan 1, 2019
BACKGROUND: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess...
beta-Globins K562 Cells Algorithms Humans Support Vector Machine Machine Learning Bayes Theorem beta-Thalassemia
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Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components.

Current topics in medicinal chemistry Jan 1, 2019
In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The i...
Bayes Theorem Molecular Targeted Therapy Machine Learning Animals Neoplasms Humans Support Vector Machine
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Ensemble Based Approach for Time Series Classification in Metabolomics.

Studies in health technology and informatics Jan 1, 2019
BACKGROUND: Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare.
Medical Informatics Humans Machine Learning Algorithms Metabolomics Bayes Theorem
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Personalized Pancreatic Cancer Management: A Systematic Review of How Machine Learning Is Supporting Decision-making.

Pancreas Jan 1, 2019
This review critically analyzes how machine learning is being used to support clinical decision-making in the management of potentially resectable pancreatic cancer. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses...
Humans Decision Making Pancreatic Neoplasms Bayes Theorem Markov Chains Machine Learning Decision Support Techniques Precision Medicine Reproducibility of Results
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Bayesian deep learning for single-cell analysis.

Nature methods Dec 1, 2018
Single-Cell Analysis Bayes Theorem Machine Learning Deep Learning
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Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to i...
Bayes Theorem Humans Models, Biological Normal Distribution Machine Learning Insulin Data Mining Diabetes Mellitus, Type 2 Phenotype Blood Glucose Self-Monitoring Regression Analysis Blood Glucose
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Can Machine-learning Techniques Be Used for 5-year Survival Prediction of Patients With Chondrosarcoma?

Clinical orthopaedics and related research Oct 1, 2018
BACKGROUND: Several studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artific...
Diagnosis, Computer-Assisted Adolescent Time Factors Child United States Support Vector Machine Bone Neoplasms Decision Support Techniques Young Adult Prognosis SEER Program Adult Reproducibility of Results Retrospective Studies Male Chondrosarcoma Humans Female Aged Decision Trees Middle Aged Neural Networks, Computer Aged, 80 and over Predictive Value of Tests Bayes Theorem
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