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A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis.

Abstract：

:Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining such splicing components, however, is not a straightforward task mainly due to the heterogeneity of tumors, the variability across samples, and the fat-short characteristic of genomic datasets. In this work, a supervised machine learning-based methodology is proposed, allowing the determination of subsets of relevant splicing components that best discriminate samples. The methodology comprises three main phases: first, a ranking of features is determined by means of applying feature weighting algorithms that compute the importance of each splicing component; second, the best subset of features that allows the induction of an accurate classifier is determined by means of conducting an effective heuristic search; then the confidence over the induced classifier is assessed by means of explaining the individual predictions and its global behavior. At the end, an extensive experimental study was conducted on a large collection of transcript-based datasets, illustrating the utility and benefit of the proposed methodology for analyzing dysregulation in splicing machinery.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Reyes O,Pérez E,Luque RM,Castaño J,Ventura S

doi

10.1016/j.artmed.2020.101950

subject

Has Abstract

pub_date

2020-08-01 00:00:00

pages

101950

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(19)31218-7

journal_volume

108

pub_type

杂志文章

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