دورية أكاديمية
AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU
العنوان: | AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU |
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المؤلفون: | Chih-Hao Fang, Nawanol Theera-Ampornpunt, Michael A. Roth, Ananth Grama, Somali Chaterji |
المصدر: | BMC Bioinformatics, Vol 20, Iss 1, Pp 1-17 (2019) |
بيانات النشر: | BMC, 2019. |
سنة النشر: | 2019 |
المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Biology (General) |
مصطلحات موضوعية: | Support vector machines (SVM), Random forest (RF), Deep neural networks (DNN), ConvNets (CNN), Image processing algorithms, Distal regulatory elements, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5 |
الوصف: | Abstract Background The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model’s complexity. In our system Aikyatan, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications’ combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes). Results We develop a suite of ML models, under the banner Aikyatan, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-‘a-vis the competition. Specifically, Aikyatan-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS. Conclusions Our exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our Aikyatan suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 1471-2105 |
Relation: | http://link.springer.com/article/10.1186/s12859-019-3049-1; https://doaj.org/toc/1471-2105 |
DOI: | 10.1186/s12859-019-3049-1 |
URL الوصول: | https://doaj.org/article/02aa026752414ec69617c939aee7594d |
رقم الأكسشن: | edsdoj.02aa026752414ec69617c939aee7594d |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 14712105 |
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DOI: | 10.1186/s12859-019-3049-1 |