Academic Journal
Gene Teams are on the Field: Evaluation of Variants in Gene-Networks Using High Dimensional Modelling
| العنوان: | Gene Teams are on the Field: Evaluation of Variants in Gene-Networks Using High Dimensional Modelling |
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| المؤلفون: | Tuna, S., Gulec, C., Yucesan, E., Cirakoglu, A., Arguden, Y.T. |
| بيانات النشر: | Institute of Electrical and Electronics Engineers Inc. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | chaos game representation, enhanced multivariance products representation, Gene network analysis, high dimensional modelling, support vector machines, Antibiotics, Bioinformatics, Complex networks, Genes, Tensors, Vectors, Computational modelling, Enhanced multivariance product representation, Game, Gene network analyse, Gene networks, Genomics, High-dimensional models, Product representation, Support vectors machine, Genome |
| الوصف: | In medical genetics, each genetic variant is evaluated as an independent entity regarding its clinical importance. However, in most complex diseases, variant combinations in specific gene networks, rather than the presence of a particular single variant, predominates. In the case of complex diseases, disease status can be evaluated by considering the success level of a team of specific variants. We propose a high dimensional modelling based method to analyse all the variants in a gene network together, which we name 'Computational Gene Network Analysis' (CoGNA). To evaluate our method, we selected two gene networks, mTOR and TGF-??. For each pathway, we generated 400 control and 400 patient group samples. mTOR and TGF-?? pathways contain 31 and 93 genes of varying sizes, respectively. We produced Chaos Game Representation images for each gene sequence to obtain 2-D binary patterns. These patterns were arranged in succession, and a 3-D tensor structure was achieved for each gene network. Features for each data sample were acquired by exploiting Enhanced Multivariance Products Representation to 3-D data. Features were split as training and testing vectors. Training vectors were employed to train a Support Vector Machines classification model. We achieved more than 96% and 99% classification accuracies for mTOR and TGF-?? networks, respectively, using a limited amount of training samples. © 2004-2012 IEEE. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 15455963 |
| Relation: | IEEE/ACM Transactions on Computational Biology and Bioinformatics; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; https://doi.org/10.1109/TCBB.2023.3292245; https://hdl.handle.net/20.500.12831/17805; 20; 2959; 2969; 2-s2.0-85164411260 |
| DOI: | 10.1109/TCBB.2023.3292245 |
| الاتاحة: | https://doi.org/10.1109/TCBB.2023.3292245 https://hdl.handle.net/20.500.12831/17805 |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.D655B198 |
| قاعدة البيانات: | BASE |
| تدمد: | 15455963 |
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| DOI: | 10.1109/TCBB.2023.3292245 |