This study proposes a computational learning approach in addition to parametric statistical methods along with a filtering mechanism, to build glaucoma genetic risk assessment model.
Here, we discuss how computational machine learning approaches can utilize hidden interactions among panels of the genetic and other risk factors, predictive of the individual disease risk by means of implementing genetic feature selection procedures and network-guided predictive models.
This covers an introduction to the more advanced and computational data mining and machine learning approaches to extract insights from data, as well as create data products such as recommendation engines and other predictive models.
She is interested in how sensory information is encoded in the brain and uses machine learning approaches to fit computational models to large-scale brain data acquired using functional magnetic resonance imaging (fMRI).
18– 21 These methods use a variety of statistical and machine learning approaches to assist computational prescreening of immunogenic epitopes for vaccine design.
To alleviate the computational complexity, researchers have previously applied heuristic search and machine learning approaches, including neural networks, to solve similar problems.
The running time on the human dataset indicated that although SLAN was slower than some network learning approaches, such as the ones proposed in [ 9, 11], its computational complexity is acceptable, even for gene function prediction in large mammalian genomes.
In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application.
Technically, data mining as a key technology for computational social science, is to discover the interesting and useful patterns from massive data by using machine learning approaches.
Machine learning approaches for accelerated materials discovery by identifying structure-property relationships and computable descriptors of complex materials properties, and by enhancing accuracy and efficiency of computational methods.
In this paper, the dynamic control approaches for spectrum sensing are proposed, based on the theory that prediction is synonymous with data compression in computational learning.
