Dissertation/ Thesis
One-class SVM and supervised machine learning models for uncovering associations of non-coding RNA with diseases
| Title: | One-class SVM and supervised machine learning models for uncovering associations of non-coding RNA with diseases |
|---|---|
| Authors: | Gutiérrez Cárdenas, Juan Manuel |
| Contributors: | Wang, Zenghui |
| Publication Year: | 2022 |
| Collection: | University of South Africa: UNISA Institutional Repository |
| Subject Terms: | mRNAs, lncRNAs, K-mers, Sequence features, Breast neoplasms, Breast cancer, SARS-CoV-2, One-class models, Supervised learning, Unsupervised learning, 615.8950285631, Non-coding RNA -- Data processing, Breast -- Cancer -- Gene therapy -- Data processing, COVID-19 (Disease) -- Gene therapy -- Data processing, Support vector machines, Machine learning |
| Description: | The study of MicroRNAs (miRNAs), long non-coding RNAs (lncRNAs) and gene interactions may be expected to provide new technologies to serve as valuable biomarkers for personalized treatments of diseases and to aid in the prognosis of certain conditions. These molecules act at the genome level by regulating or suppressing their protein expression functions. The primary challenge in the study of these non-coding molecules involves the necessity of finding labeled data indicating positive and negative interactions when predicting interactions using machine-learning or deep-learning techniques. However, usually we end up with a scenario of unbalanced data or unstable scenarios for using these models. An additional problem involves the extraction of features derived from the binding of these non-coding RNAs and genes. This binding process usually occurs fully or partially in animal genetics, which leads to considerable complexity in studying the process. Therefore, the main objective of the present work is to demonstrate that it is possible to use features extracted for miRNAs sequences in the development of diseases such as breast cancer, breast neoplasms, or if there is any influence with immune genes related to the SARS-COV-2. We performed experiments focusing on the erb-b2 receptor tyrosine kinase 2 (ERBB2) gene involved in breast cancer. For this purpose, we gathered miRNA-mRNA information from the binding between these two genetic molecules. In this part of our research, we applied a One-Class SVM and an Isolation Forest to discriminate between weak interactions, outliers given by the one-class model, and strong interactions that could occur between miRNA and mRNA (messenger RNA). Additionally, this study aimed to differentiate between breast cancer cases and breast neoplasm conditions. In this section we used the information encoded in lncRNAs. The additional feature used in this part was the frequency of k-mers, i.e., small portions of nucleotides, along with the data from the energy released in miRNA folding. ... |
| Document Type: | thesis |
| File Description: | 1 online resource (xiii, 104 leaves) : illustrations (chiefly color), color graphs; application/pdf |
| Language: | English |
| Relation: | https://hdl.handle.net/10500/28539 |
| Availability: | https://hdl.handle.net/10500/28539 |
| Accession Number: | edsbas.7A6C8038 |
| Database: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hdl.handle.net/10500/28539# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.7A6C8038 RelevancyScore: 772 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 772.276306152344 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: One-class SVM and supervised machine learning models for uncovering associations of non-coding RNA with diseases – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gutiérrez+Cárdenas%2C+Juan+Manuel%22">Gutiérrez Cárdenas, Juan Manuel</searchLink> – Name: Author Label: Contributors Group: Au Data: Wang, Zenghui – Name: DatePubCY Label: Publication Year Group: Date Data: 2022 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of South Africa: UNISA Institutional Repository – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22mRNAs%22">mRNAs</searchLink><br /><searchLink fieldCode="DE" term="%22lncRNAs%22">lncRNAs</searchLink><br /><searchLink fieldCode="DE" term="%22K-mers%22">K-mers</searchLink><br /><searchLink fieldCode="DE" term="%22Sequence+features%22">Sequence features</searchLink><br /><searchLink fieldCode="DE" term="%22Breast+neoplasms%22">Breast neoplasms</searchLink><br /><searchLink fieldCode="DE" term="%22Breast+cancer%22">Breast cancer</searchLink><br /><searchLink fieldCode="DE" term="%22SARS-CoV-2%22">SARS-CoV-2</searchLink><br /><searchLink fieldCode="DE" term="%22One-class+models%22">One-class models</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Unsupervised+learning%22">Unsupervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22615%2E8950285631%22">615.8950285631</searchLink><br /><searchLink fieldCode="DE" term="%22Non-coding+RNA+--+Data+processing%22">Non-coding RNA -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Breast+--+Cancer+--+Gene+therapy+--+Data+processing%22">Breast -- Cancer -- Gene therapy -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19+%28Disease%29+--+Gene+therapy+--+Data+processing%22">COVID-19 (Disease) -- Gene therapy -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Description Group: Ab Data: The study of MicroRNAs (miRNAs), long non-coding RNAs (lncRNAs) and gene interactions may be expected to provide new technologies to serve as valuable biomarkers for personalized treatments of diseases and to aid in the prognosis of certain conditions. These molecules act at the genome level by regulating or suppressing their protein expression functions. The primary challenge in the study of these non-coding molecules involves the necessity of finding labeled data indicating positive and negative interactions when predicting interactions using machine-learning or deep-learning techniques. However, usually we end up with a scenario of unbalanced data or unstable scenarios for using these models. An additional problem involves the extraction of features derived from the binding of these non-coding RNAs and genes. This binding process usually occurs fully or partially in animal genetics, which leads to considerable complexity in studying the process. Therefore, the main objective of the present work is to demonstrate that it is possible to use features extracted for miRNAs sequences in the development of diseases such as breast cancer, breast neoplasms, or if there is any influence with immune genes related to the SARS-COV-2. We performed experiments focusing on the erb-b2 receptor tyrosine kinase 2 (ERBB2) gene involved in breast cancer. For this purpose, we gathered miRNA-mRNA information from the binding between these two genetic molecules. In this part of our research, we applied a One-Class SVM and an Isolation Forest to discriminate between weak interactions, outliers given by the one-class model, and strong interactions that could occur between miRNA and mRNA (messenger RNA). Additionally, this study aimed to differentiate between breast cancer cases and breast neoplasm conditions. In this section we used the information encoded in lncRNAs. The additional feature used in this part was the frequency of k-mers, i.e., small portions of nucleotides, along with the data from the energy released in miRNA folding. ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: thesis – Name: Format Label: File Description Group: SrcInfo Data: 1 online resource (xiii, 104 leaves) : illustrations (chiefly color), color graphs; application/pdf – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://hdl.handle.net/10500/28539 – Name: URL Label: Availability Group: URL Data: https://hdl.handle.net/10500/28539 – Name: AN Label: Accession Number Group: ID Data: edsbas.7A6C8038 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.7A6C8038 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: mRNAs Type: general – SubjectFull: lncRNAs Type: general – SubjectFull: K-mers Type: general – SubjectFull: Sequence features Type: general – SubjectFull: Breast neoplasms Type: general – SubjectFull: Breast cancer Type: general – SubjectFull: SARS-CoV-2 Type: general – SubjectFull: One-class models Type: general – SubjectFull: Supervised learning Type: general – SubjectFull: Unsupervised learning Type: general – SubjectFull: 615.8950285631 Type: general – SubjectFull: Non-coding RNA -- Data processing Type: general – SubjectFull: Breast -- Cancer -- Gene therapy -- Data processing Type: general – SubjectFull: COVID-19 (Disease) -- Gene therapy -- Data processing Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: One-class SVM and supervised machine learning models for uncovering associations of non-coding RNA with diseases Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gutiérrez Cárdenas, Juan Manuel – PersonEntity: Name: NameFull: Wang, Zenghui IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2022 Identifiers: – Type: issn-locals Value: edsbas |
| ResultId | 1 |