Abstract
Convolutional neural networks extract high-level abstraction features using minimum preprocessing steps. In this research, we propose a new approach in classifying Down Syndrome with Acute Lymphoblastic Leukemia using a convolutional neural network. Sequences are represented using a one hot vector depending on point mutation as input to the CNN model. Therefore, it conserves the necessary position data of each nucleotide in the sequence. Using two different genomic datasets, our proposed model has achieved significant improvements over classical classification techniques, with an increased accuracy of 98%, and 98.5%, respectively.
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References
Andrews, T.D., Jeelall, Y., Talaulikar, D., Goodnow, C.C., Field, M.A.: DeepSNVMiner: a sequence analysis tool to detect emergent, rare mutations in subsets of cell populations. PeerJ 4, 1–13 (2016)
Blekas, K., Fotiadis, D.I., Likas, A.: Motif-based protein sequence classification using neural networks. J. Comput. Biol. 12(1), 64–82 (2005)
Buitenkamp, T.D., et al.: Acute lymphoblastic leukemia in children with down syndrome: a retrospective analysis from the ponte di legno study group. Blood 123(1), 70–77 (2014)
Choong, A.C.H., Lee, N.K.: Evaluation of convolutionary neural networks modeling of DNA sequences using ordinal versus one-hot encoding method. bioRxiv, pp. 60–65 (2017)
Hannun, A., et al.: Deep speech: scaling up end-to-end speech recognition. arXiv preprint arXiv:1412.5567 (2014)
Johnson, R., Zhang, T.: Effective use of word order for text categorization with convolutional neural networks. arXiv preprint arXiv:1412.1058 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Leung, M.K.K., Delong, A., Alipanahi, B., Frey, B.J.: Machine learning in genomic medicine: a review of computational problems and data sets. Proc. IEEE 104(1), 176–197 (2016)
Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325–5334 (2015)
Li, H.: A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27(21), 2987–2993 (2011)
Li, H., et al.: The sequence alignment/map format and samtools. Bioinformatics 25(16), 2078–2079 (2009)
Loudin, M.G., et al.: Genomic profiling in down syndrome acute lymphoblastic leukemia identifies histone gene deletions associated with altered methylation profiles. Leukemia 25(10), 1555 (2011)
Mahdieh, N., Rabbani, B.: An overview of mutation detection methods in genetic disorders. Iran. J. Pediatr. 23(4), 375–388 (2013)
Maloney, K.W.: Acute lymphoblastic leukaemia in children with down syndrome: an updated review. Br. J. Haematol. 155(4), 420–425 (2011)
McCarthy, M.I., MacArthur, D.G.: Human disease genomics: from variants to biology. Genome Biol. 18(1), 1–3 (2017)
Mikolov, T.: Statistical language models based on neural networks. Presentation at Google, Mountain View (2012)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Min, S., Lee, B., Yoon, S.: Deep learning in bioinformatics. Brief. Bioinform. 18(5), 851–869 (2017)
Ng, P.: dna2vec: Consistent vector representations of variable-length k-mers. arXiv preprint arXiv:1701.06279 (2017)
Nguyen, N.G., et al.: DNA sequence classification by convolutional neural network. J. Biomed. Sci. Eng. 9(05), 280–286 (2016)
Pan, X., Shen, H.-B.: Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks. Bioinformatics p. bty364 (2018)
Ramirez-Gonzalez, R.H., Bonnal, R., Caccamo, M., MacLean, D.: Bio-samtools: ruby bindings for samtools, a library for accessing bam files containing high-throughput sequence alignments. Source Code Biol. Med. 7(1), 1–6 (2012)
Srinivas, S., et al.: A taxonomy of deep convolutional neural nets for computer vision. arXiv preprint arXiv:1601.06615 (2016)
Venter, J.C., et al.: The sequence of the human genome. Science 291(5507), 1304–1351 (2001)
Wang, Q., et al.: Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers. Genome Med. 5(10), 1–8 (2013)
Wu, R., Yan, S., Shan, Y., Dang, Q., Sun, G.: Deep image: scaling up image recognition, 7(8). arXiv preprint arXiv:1501.02876 (2015)
Yan, S., Xia, Y., Smith, J.S., Lu, W., Zhang, B.: Multiscale convolutional neural networks for hand detection. Appl. Comput. Intell. Soft Comput. 2017 (2017)
Yue, T., Wang, H.: Deep learning for genomics: a concise overview. arXiv preprint arXiv:1802.00810 (2018)
Zeng, H., Edwards, M.D., Liu, G., Gifford, D.K.: Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics 32(12), i121–i127 (2016)
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Shouman, M., Belal, N., El Sonbaty, Y. (2018). Detecting Acute Lymphoblastic Leukemia in down Syndrome Patients Using Convolutional Neural Networks on Preprocessed Mutated Datasets. In: Alves, R. (eds) Advances in Bioinformatics and Computational Biology. BSB 2018. Lecture Notes in Computer Science(), vol 11228. Springer, Cham. https://doi.org/10.1007/978-3-030-01722-4_9
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