Advances Technology of Databases by Computational  Biology/Bioinformatics

Sudha Giri

Advances Technology of Databases by Computational Biology/Bioinformatics

Sudha Giri

Abstract

Advances in genomics and imaging technology have resulted in an explosion of molecular and cellular profiling data from vast numbers of samples. This significant rise in biological data size and collection velocity is putting traditional analytic methodologies to the test. Modern machine learning approaches, such as deep learning, promise to use very huge data sets to uncover underlying structure and make accurate predictions. In addition to offering applications and practical recommendations, we also discuss potential problems and limits to help computational biologists decide when and how to employ this new tool.

Keywords

Databases, Ageing, Genomics, Computational Biology, Gene expression, Data Interpretation.

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References

Angermueller, C., Pärnamaa, T., Parts, L. and Stegle, O., 2016. Deep learning for computational biology. Molecular systems biology, 12(7), p.878.

Battle, A., Khan, Z., Wang, S.H., Mitrano, A., Ford, M.J., Pritchard, J.K. and Gilad, Y., 2015. Impact of regulatory variation from RNA to protein. Science, 347(6222), pp.664-667.

Cireşan, D.C., Giusti, A., Gambardella, L.M. and Schmidhuber, J., 2013, September. Mitosis detection in breast cancer histology images with deep neural networks. In International conference on medical image computing and computer-assisted intervention (pp. 411-418). Springer, Berlin, Heidelberg.

Eickholt, J. and Cheng, J., 2012. Predicting protein residue–residue contacts using deep networks and boosting. Bioinformatics, 28(23), pp.3066-3072.

Fakoor, R., Ladhak, F., Nazi, A. and Huber, M., 2013, June. Using deep learning to enhance cancer diagnosis and classification. In Proceedings of the international conference on machine learning (Vol. 28, pp. 3937-3949). ACM, New York, USA.

Glorot, X. and Bengio, Y., 2010, March. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 249-256). JMLR Workshop and Conference Proceedings.

He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

Kabir, M.N., 2019. Technologies of the future. In Knowledge-Based Social Entrepreneurship (pp. 91-133). Palgrave Macmillan, New York.

Nesterov, Y., 2003. Introductory lectures on convex optimization: A basic course (Vol. 87). Springer Science & Business Media.

Ning, F., Delhomme, D., LeCun, Y., Piano, F., Bottou, L. and Barbano, P.E., 2005. Toward automatic phenotyping of developing embryos from videos. IEEE Transactions on Image Processing, 14(9), pp.1360-1371.

Stegle, O., Parts, L., Durbin, R. and Winn, J., 2010. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS computational biology, 6(5), p.e1000770.

Stormo, G.D., Schneider, T.D., Gold, L. and Ehrenfeucht, A., 1982. Use of the ‘Perceptron’algorithm to distinguish translational initiation sites in E. coli. Nucleic acids research, 10(9), pp.2997-3011.

Yosinski, J., Clune, J., Bengio, Y. and Lipson, H., 2014. How transferable are features in deep neural networks? Advances in neural information processing systems, 27.

Zeiler, M.D. and Fergus, R., 2014, September. Visualizing and understanding convolutional networks. In European conference on computer vision (pp. 818-833). Springer, Cham.

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