Prediction of Deafness Gene Promoters Based on a Two-Level Cascade Model
Authors: 
Yangfan Huang, Jing Sun, Li Teng, Xiaohua Luo, Xiao LiuAuthors Info & Claims
Yangfan Huang, Jing Sun, Li Teng, Xiaohua Luo, Xiao LiuAuthors Info & ClaimsPages 38 - 46
Abstract
Hearing loss is one of the most common disabilities. Aberrant regulation of promoters can lead to disease. Therefore, it is essential to explore the mechanisms of gene expression regulation and their association with hearing loss. We applied machine learning to identify and predict deafness gene-specific promoters. Six typical machine learning models were cross-combined to construct a two-stage prediction network by calculating information-theoretic features. The first stage was used for predicting promoters, and the second stage was used for predicting deafness gene promoters. For sequences with lengths of 200 bp and 300 bp, the overall accuracy and area under curve can reach 0.802/0.854 and 0.738/0.785, respectively, on the independent test set. The experimental results show that the method of combining the information-theoretic features and the two-stage cascade model to predict deafness promoters can obtain better prediction results, and it can provide a reference method for the analysis of disease promoters.
References
[1]
GBD 2019 Hearing Loss Collaborators, “Hearing loss prevalence and years lived with disability, 1990-2019: findings from the Global Burden of Disease Study 2019,” Lancet, vol. 397, no. 10278, pp. 996–1009, Mar. 2021.
[2]
S. Huang et al., “Gene4HL: An Integrated Genetic Database for Hearing Loss,” Front Genet, vol. 12, p. 773009, 2021.
[3]
M. T. Maurano et al., “Systematic localization of common disease-associated variation in regulatory DNA,” Science, vol. 337, no. 6099, pp. 1190–1195, Sep. 2012.
[4]
M. Levine and R. Tjian, “Transcription regulation and animal diversity,” Nature, vol. 424, no. 6945, pp. 147–151, Jul. 2003.
[5]
S. Kojima et al., “Mobile element variation contributes to population-specific genome diversification, gene regulation and disease risk,” Nat Genet, vol. 55, no. 6, pp. 939–951, Jun. 2023.
[6]
Q. Liu, M. Liu, and W. Wu, “Strong/Weak Feature Recognition of Promoters Based on Position Weight Matrix and Ensemble Set-Valued Models,” J Comput Biol, vol. 25, no. 10, pp. 1152–1160, Oct. 2018.
[7]
P. J. Park, A. J. Butte, and I. S. Kohane, “Comparing expression profiles of genes with similar promoter regions,” Bioinformatics, vol. 18, no. 12, pp. 1576–1584, Dec. 2002.
[8]
S. Döhr, A. Klingenhoff, H. Maier, M. Hrabé de Angelis, T. Werner, and R. Schneider, “Linking disease-associated genes to regulatory networks via promoter organization,” Nucleic Acids Res, vol. 33, no. 3, pp. 864–872, 2005.
[9]
R. Coles, R. Caswell, and D. C. Rubinsztein, “Functional analysis of the Huntington's disease (HD) gene promoter,” Hum Mol Genet, vol. 7, no. 5, pp. 791–800, May 1998.
[10]
R. V. Davuluri, Y. Suzuki, S. Sugano, C. Plass, and T. H.-M. Huang, “The functional consequences of alternative promoter use in mammalian genomes,” Trends Genet, vol. 24, no. 4, pp. 167–177, Apr. 2008.
[11]
B. Maloney and D. K. Lahiri, “Structural and functional characterization of H2 haplotype MAPT promoter: unique neurospecific domains and a hypoxia-inducible element would enhance rationally targeted tauopathy research for Alzheimer's disease,” Gene, vol. 501, no. 1, pp. 63–78, Jun. 2012.
[12]
Y.-S. Long, Y.-W. Shi, and W.-P. Liao, “Conservation-based prediction of the transcription regulatory region of the SCN1A gene,” Progress in Natural Science, vol. 19, no. 12, pp. 1675–1681, Dec. 2009.
[13]
S. Liu, “Increasing alternative promoter repertories is positively associated with differential expression and disease susceptibility,” PLoS One, vol. 5, no. 3, p. e9482, Mar. 2010.
[14]
Y. Dong et al., “A Genome-Wide Investigation of Effects of Aberrant DNA Methylation on the Usage of Alternative Promoters in Hepatocellular Carcinoma,” Front Oncol, vol. 11, p. 780266, 2021.
[15]
D. Demircioğlu et al., “A Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters,” Cell, vol. 178, no. 6, pp. 1465-1477.e17, Sep. 2019.
[16]
B. Liu, F. Yang, D.-S. Huang, and K.-C. Chou, “iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC,” Bioinformatics, vol. 34, no. 1, pp. 33–40, Jan. 2018.
[17]
X. Liu, L. Teng, W. Zuo, S. Zhong, Y. Xu, and J. Sun, “Deafness gene screening based on a multilevel cascaded BPNN model,” BMC Bioinformatics, vol. 24, no. 1, p. 56, Feb. 2023.
[18]
M. J. Landrum et al., “ClinVar: improvements to accessing data,” Nucleic Acids Res, vol. 48, no. D1, pp. D835–D844, Jan. 2020.
[19]
H. Azaiez et al., “Genomic Landscape and Mutational Signatures of Deafness-Associated Genes,” Am J Hum Genet, vol. 103, no. 4, pp. 484–497, Oct. 2018.
[20]
J. S. Amberger, C. A. Bocchini, A. F. Scott, and A. Hamosh, “OMIM.org: leveraging knowledge across phenotype-gene relationships,” Nucleic Acids Res, vol. 47, no. D1, pp. D1038–D1043, Jan. 2019.
[21]
T. Davoli et al., “Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome,” Cell, vol. 155, no. 4, pp. 948–962, Nov. 2013.
[22]
A. P. Cazier and J. Blazeck, “Advances in promoter engineering: Novel applications and predefined transcriptional control,” Biotechnol J, vol. 16, no. 10, p. e2100239, Oct. 2021.
[23]
C. Chen et al., “TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data,” Mol Plant, vol. 13, no. 8, pp. 1194–1202, Aug. 2020.
[24]
J. Li et al., “Integrated entropy-based approach for analyzing exons and introns in DNA sequences,” BMC Bioinformatics, vol. 20, no. Suppl 8, p. 283, Jun. 2019.
[25]
X. Liu, L. Teng, Y. Luo, and Y. Xu, “Prediction of prokaryotic and eukaryotic promoters based on information-theoretic features,” Biosystems, vol. 231, p. 104979, Sep. 2023.
[26]
C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, “A comparative analysis of gradient boosting algorithms,” Artif Intell Rev, vol. 54, no. 3, pp. 1937–1967, Mar. 2021.
[27]
H.-Y. Lai et al., “iProEP: A Computational Predictor for Predicting Promoter,” Molecular Therapy - Nucleic Acids, vol. 17, pp. 337–346, Sep. 2019.
[28]
C. C. Morton and W. E. Nance, “Newborn hearing screening–a silent revolution,” N Engl J Med, vol. 354, no. 20, pp. 2151–2164, May 2006.
[29]
B. Pang, J. H. van Weerd, F. L. Hamoen, and M. P. Snyder, “Identification of non-coding silencer elements and their regulation of gene expression,” Nat Rev Mol Cell Biol, vol. 24, no. 6, pp. 383–395, Jun. 2023.
Index Terms
- Prediction of Deafness Gene Promoters Based on a Two-Level Cascade Model
Recommendations
Predicting vertebrate promoters using heterogeneous clusters
This paper proposes a system, named the Vertebrate Promoter Prediction System (VePPS), which employs a new statistics-based approach to predict vertebrate promoters, and analyses a putative promoter sequence by investigating the presence of short ...
Identification and characterization of promoters and cis-regulatory elements of genes involved in secondary metabolites production in hop (Humulus lupulus. L)
Display Omitted Single or combination of different cis-acting regulatory elements involved in the transcriptional regulation metabolic genes involved in biosynthesis of prenylflavanoid (PF) and bitter acid (BA) production in Humulus lupulus.The presence ...
Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles
Display Omitted An integrated pathway-oriented method is proposed for analyzing transcription regulation networks from gene expression data.The method currently is based on KEGG pathway definitions but is flexible to other pathway models.This improves ...
Comments
Information & Contributors
Information
Published In
May 2024
279 pages
ISBN:9798400717666
DOI:10.1145/3674658
Copyright © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 18 November 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
ICBBT 2024
ICBBT 2024: 2024 16th International Conference on Bioinformatics and Biomedical Technology
May 24 - 26, 2024
Chongqing, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 9Total Downloads
- Downloads (Last 12 months)9
- Downloads (Last 6 weeks)4
Reflects downloads up to 21 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderFull Text
View this article in Full Text.
Full TextHTML Format
View this article in HTML Format.
HTML Format