In addition to rigorous machine learning analysis, we demonstrate the proposed classifier's superiority by comparing it with nine state-of-the-art peers on six heterogeneous profiles generated from different profiling technologies and processed by different preprocessing algorithms.
We propose a machine vision approach and we demonstrate that this technique is applicable in an industrial setting.
In this study, we demonstrate that machine learning can be used to classify students who had backgrounds in positive-sciences (including engineering, science and math disciplines) vs. social-sciences (including arts and humanities disciplines) by the help of musical hearing and perception using genetic neural networks.
We demonstrate that machine learning can be used for identifying these characteristic signatures and for subsequently classifying genes as to whether they are primary Ahr-dependent targets or indirectly affected (BPDE-dependent) genes.
By using a two-machine-quasi-infinite-bus system, we demonstrate that the proposing control effectively damps low-frequency oscillation.
We demonstrate a machine-learning-based approach to the construction of a similarity metric, which uses this contextual information to project the calculated features into an intermediate space where a music similarity function that incorporates some of the cultural information may be calculated.
Results: We demonstrate that a machine-learning approach using only network topological features can successfully predict the validity of enzyme annotations.
Taken together, we demonstrate that SOLiD machines, which employ sequencing-by-ligation mechanism, are unable to read through the palindromic region.
Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles.
Using a support vector machine classification algorithm, we demonstrated that genome-wide methylation profiles predicted tumor mutation status with estimated error rates of 19% (BRCA1), 31% (BRCA2) and 36% (BRCAx), but did not accurately predict the intrinsic subtypes defined by gene expression with error rates of 43% (basal) and 54% (luminal A).
