AIMC Topic: Markov Chains

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An Application of Random Walk Resampling to Phylogenetic HMM Inference and Learning.

IEEE transactions on nanobioscience May 8, 2020
Statistical resampling methods are widely used for confidence interval placement and as a data perturbation technique for statistical inference and learning. An important assumption of popular resampling methods such as the standard bootstrap is that...
Computational Biology Markov Chains Sequence Alignment Machine Learning Computer Simulation Phylogeny
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GOMCL: a toolkit to cluster, evaluate, and extract non-redundant associations of Gene Ontology-based functions.

BMC bioinformatics Apr 10, 2020
BACKGROUND: Functional enrichment of genes and pathways based on Gene Ontology (GO) has been widely used to describe the results of various -omics analyses. GO terms statistically overrepresented within a set of a large number of genes are typically ...
Markov Chains User-Computer Interface Gene Ontology High-Throughput Screening Assays Plant Roots Arabidopsis Multigene Family
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HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection.

Computational and mathematical methods in medicine Mar 28, 2020
Prediction of DNA-binding proteins (DBPs) has become a popular research topic in protein science due to its crucial role in all aspects of biological activities. Even though considerable efforts have been devoted to developing powerful computational ...
ROC Curve Amino Acid Sequence Databases, Protein Humans Markov Chains Computational Biology Amino Acids Machine Learning Support Vector Machine DNA-Binding Proteins Algorithms
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The Role and Promise of Artificial Intelligence in Medical Toxicology.

Journal of medical toxicology : official journal of the American College of Medical Toxicology Mar 25, 2020
Artificial intelligence (AI) refers to machines or software that process information and interact with the world as understanding beings. Examples of AI in medicine include the automated reading of chest X-rays and the detection of heart dysrhythmias...
Deep Learning Big Data Knowledge Bases Humans Neural Networks, Computer Artificial Intelligence Vocabulary, Controlled Psychotropic Drugs Data Mining Markov Chains Toxicology
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Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Nature communications Mar 3, 2020
Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biolog...
Humans Neural Networks, Computer Gene Expression Markov Chains Epigenome K562 Cells Genomics Computer Graphics Data Analysis Cluster Analysis Chromatin Unsupervised Machine Learning Algorithms Reproducibility of Results
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Resilient fault-tolerant anti-synchronization for stochastic delayed reaction-diffusion neural networks with semi-Markov jump parameters.

Neural networks : the official journal of the International Neural Network Society Feb 28, 2020
This paper deals with the anti-synchronization issue for stochastic delayed reaction-diffusion neural networks subject to semi-Markov jump parameters. A resilient fault-tolerant controller is utilized to ensure the anti-synchronization in the presenc...
Humans Neural Networks, Computer Diffusion Markov Chains Stochastic Processes
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Patient selection for proton therapy: a radiobiological fuzzy Markov model incorporating robust plan analysis.

Physical and engineering sciences in medicine Feb 10, 2020
While proton therapy can offer increased sparing of healthy tissue compared with X-ray therapy, it can be difficult to predict whether a benefit can be expected for an individual patient. Predictive modelling may aid in this respect. However, the pre...
Uncertainty Radiotherapy, Intensity-Modulated Life Expectancy Proton Therapy Probability Markov Chains Humans Quality-Adjusted Life Years Female Quality of Life Fuzzy Logic Patient Selection Child, Preschool Models, Theoretical
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Deep Learning of Markov Model-Based Machines for Determination of Better Treatment Option Decisions for Infertile Women.

Reproductive sciences (Thousand Oaks, Calif.) Jan 14, 2020
In this technical article, we are proposing ideas, that we have been developing on how machine learning and deep learning techniques can potentially assist obstetricians/gynecologists in better clinical decision-making, using infertile women in their...
Diagnosis, Computer-Assisted Deep Learning Infertility, Female Models, Biological Clinical Decision-Making Humans Markov Chains Pregnancy Female
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A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants.

Journal of neural engineering Jan 14, 2020
OBJECTIVE: To classify sleep states using electroencephalogram (EEG) that reliably works over a wide range of preterm ages, as well as term age.
Humans Neural Networks, Computer Algorithms Databases, Factual Brain Infant, Premature Normal Distribution Sleep Stages Infant, Newborn Electroencephalography Markov Chains
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Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm.

Journal of medical economics Jan 10, 2020
As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-prediction algorithms may help to efficiently identify people with undiagnosed AF. However, the cost-effec...
Undiagnosed Diseases Markov Chains Quality-Adjusted Life Years United Kingdom Decision Trees Atrial Fibrillation Machine Learning Cost-Benefit Analysis Humans Algorithms Risk Assessment Mass Screening
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