Inferring Causation in Yeast Gene Association Networks With Kernel Logistic Regression
| Autor: | Andrzej Kudlicki, Maher Maalouf, Amira Al-Aamri, Dirar Homouz, Kamal Taha |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
Kernel logistic regression Association (object-oriented programming) lcsh:Evolution 02 engineering and technology Computational biology Biology Computer Science Applications Yeast gene 03 medical and health sciences 0202 electrical engineering electronic engineering information engineering Genetics lcsh:QH359-425 Gene co-expression network 020201 artificial intelligence & image processing Causation Gene Transcription factor Ecology Evolution Behavior and Systematics 030304 developmental biology |
| Zdroj: | Evolutionary Bioinformatics, Vol 16 (2020) |
| ISSN: | 1176-9343 |
| Popis: | Computational prediction of gene-gene associations is one of the productive directions in the study of bioinformatics. Many tools are developed to infer the relation between genes using different biological data sources. The association of a pair of genes deduced from the analysis of biological data becomes meaningful when it reflects the directionality and the type of reaction between genes. In this work, we follow another method to construct a causal gene co-expression network while identifying transcription factors in each pair of genes using microarray expression data. We adopt a machine learning technique based on a logistic regression model to tackle the sparsity of the network and to improve the quality of the prediction accuracy. The proposed system classifies each pair of genes into either connected or nonconnected class using the data of the correlation between these genes in the whole Saccharomyces cerevisiae genome. The accuracy of the classification model in predicting related genes was evaluated using several data sets for the yeast regulatory network. Our system achieves high performance in terms of several statistical measures. |
| Databáze: | OpenAIRE |
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