Deep learning for cancer type classification and driver gene identification
| Autor: | Seema A. Khan, Chengsheng Mao, Xiaoyu Li, Janna Ore Nugent, Susan E. Clare, Yuan Luo, Andy H. Vo, Zexian Zeng |
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| Rok vydání: | 2021 |
| Předmět: |
QH301-705.5
Computer applications to medicine. Medical informatics R858-859.7 Convolutional neural network Germline variants Computational biology Biology Biochemistry DNA sequencing Germline Deep Learning Breast cancer Germline mutation Structural Biology Neoplasms Exome Sequencing medicine Humans Biology (General) Molecular Biology Exome sequencing Cancer business.industry Research Somatic mutation Applied Mathematics Deep learning Oncogenes Sequence Analysis DNA Classification medicine.disease Computer Science Applications Whole-exome sequencing Artificial intelligence DNA microarray business |
| Zdroj: | BMC Bioinformatics, Vol 22, Iss S4, Pp 1-13 (2021) BMC Bioinformatics |
| ISSN: | 1471-2105 |
| Popis: | Background Genetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. We aim to develop a novel method to effectively explore the landscape of genetic variants, including germline variants, and small insertions and deletions for cancer type prediction. Results We proposed DeepCues, a deep learning model that utilizes convolutional neural networks to unbiasedly derive features from raw cancer DNA sequencing data for disease classification and relevant gene discovery. Using raw whole-exome sequencing as features, germline variants and somatic mutations, including insertions and deletions, were interactively amalgamated for feature generation and cancer prediction. We applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (p Conclusion Our results support DeepCues as a novel method to improve the representational resolution of DNA sequencings and its power in deriving features from raw sequences for cancer type prediction, as well as discovering new cancer relevant genes. |
| Databáze: | OpenAIRE |
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