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deep learning: new computational modelling techniques for genomics

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Course materials and notes for MIT class 6.802 / 6.874 / 20.390 / 20.490 / HST.506 Computational Systems Biology: Deep Learning in the Life Sciences Nat. Preprint at bioRxiv https://doi.org/10.1101/262501 (2018). Biotechnol. This site needs JavaScript to work properly. Predicting splicing from primary sequence with deep learning. Pharmacogenomics 19, 629–650 (2018). Machine learning models that embed the entire data-processing pipeline to transform raw input data into predictions without requiring a preprocessing step. Golub, T. R. et al. Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. Boža, V., Brejová, B. Bioinformatics 33, 2539–2546 (2017). Problems in the analysis of survey data, and a proposal. & Shen, H.-B. Park, S., Min, S., Choi, H. & Yoon, S. deepMiRGene: deep neural network based precursor microRNA prediction. Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. In this paper, a deep CNN was trained to predict more than 4,000 genomic measurements including gene expression as measured by cap analysis of gene expression (CAGE) for every 150 bp in the genome using a receptive field of 32 kb. Online ahead of print. Fleming, N. How artificial intelligence is changing drug discovery. Killoran, N., Lee, L. J., Delong, A., Duvenaud, D. & Frey, B. J. Curr. MMSplice: modular modeling improves the predictions of genetic variant effects on splicing. In the simplest case, it measures the discrepancy between predictions and observations. Am. Preprint at bioRxiv https://doi.org/10.1101/085118 (2016). Sundararajan, M., Taly, A. Methods 15, 30 (2018). Clipboard, Search History, and several other advanced features are temporarily unavailable. & Le, Q. V. Do better ImageNet models transfer better? Use of the ‘Perceptron’ algorithm to distinguish translational initiation sites in E. coli. Pattern Anal. Luo, R., Sedlazeck, F. J., Lam, T.-W. & Schatz, M. Clairvoyante: a multi-task convolutional deep neural network for variant calling in single molecule sequencing. Genomics Proteomics Bioinformatics 16, 320–331 (2018). It is evident that deep learning models can provide higher accuracies in specific tasks of genomics … Quang, D. & Xie, X. FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data. Activation functions are usually nonlinear yet very simple, such as the rectified-linear unit or the sigmoid function. Sung, K. & Poggio, T. Example-based learning for view-based human face detection. 2017 Aug;18(3):273-284. doi: 10.1007/s10339-017-0796-7. 32, 1627–1645 (2010). Quang, D. & Xie, X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. PubMed  Genet. Feature importance scores defined as the gradient absolute values of the model output with respect to the model input. Comput. 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