Abstract
Exploring the effects of genetic information in causing potential brain disorders like Alzheimer’s disease (AD) and Parkinson’s disease (PD) is a relatively unexplored field. The aim of this investigation was to employ computational techniques at predicting anomalies that cause neuro-degenerative brain disorders with improved accuracy at an enhanced pace by analysis of gene and protein sequence data. The proposed methodology employed deep learning techniques to determine anomaly causing genes that played a significant role in causing potential brain disorders. The results revealed that deep learning models exhibit improved performance compared to conventional machine learning models, in identifying the optimal genes that cause neuro-degenerations.
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Acknowledgements
This research work is a part of the Science and Engineering Research Board (SERB), Department of Science and Technology (DST) funded project under Young Scientist Scheme—Early Start-up Research Grant- titled “Investigation on the effect of Gene and Protein Mutants in the onset of Neuro-Degenerative Brain Disorders (Alzheimer’s and Parkinson’s disease): A Computational Study” with Reference No- SERB—YSS/2015/000737.
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Athilakshmi, R., Jacob, S.G., Rajavel, R. (2019). Protein Sequence Based Anomaly Detection for Neuro-Degenerative Disorders Through Deep Learning Techniques. In: Peter, J., Alavi, A., Javadi, B. (eds) Advances in Big Data and Cloud Computing. Advances in Intelligent Systems and Computing, vol 750. Springer, Singapore. https://doi.org/10.1007/978-981-13-1882-5_48
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DOI: https://doi.org/10.1007/978-981-13-1882-5_48
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