Abstract
Histone modifications provide an important layer of gene regulation in eukaryotes. In this paper, we propose an approach that identifies the histone modification patterns most relevant for specific biological functions, such as flowering in plants. We first propose a new pattern scoring method, which evaluates the importance of each combinatorial pattern of histone modifications; this is used along with logistic regression, Support Vector Machines, and naive Bayesian classifier algorithms to predict gene functions. This approach is shown to be successful in inferring significant patterns verified by independent gene function data, outperforming other pattern scores used in current histone modification analysis research.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017 |
| Publisher | The International Society for Computers and Their Applications (ISCA) |
| Pages | 217-222 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781943436071 |
| State | Published - 2017 |
| Event | 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017 - Honolulu, United States Duration: Mar 20 2017 → Mar 22 2017 |
Other
| Other | 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017 |
|---|---|
| Country | United States |
| City | Honolulu |
| Period | 3/20/17 → 3/22/17 |
Keywords
- Gene expression
- Histone modification
- Predicting biological function
ASJC Scopus subject areas
- Biomedical Engineering
- Health Information Management
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Cite this
Xiao, Y., Mehrotra, K. G., Mohan, C. K., Choudhary, P., & Raina, R. (2017). Prediction of biological functions by histone modification patterns profiling. In Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017 (pp. 217-222). The International Society for Computers and Their Applications (ISCA).