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Supervised Learning Approach for Knowledge Extraction & Deci-sion-Making Process using Genome Sequence in Bioinformatics


May Abdullah Almutairi and Abdul Rauf Baig


Vol. 20  No. 12  pp. 149-158


Biomedicine, health care, and life sciences have recently played a significant role in data and information-intensive science. Particularly in the area of bioinformatics and compu-tational biology, there is tremendous growth in data that could be noisy data, multidimensional, unstructured data or struc-tured data, and the diversity of highly complex data. Therefore, a specific modelling and integrative analysis system is required. The present study focuses on developing a conceptual frame-work using deep learning approach to predictive modelling of diseases in bioinformatics using data from genome sequences. Initially, the data is pre-processed using Min-Max Standardiza-tion approach where it cross verifies the missing value and data scaling has been performed. Second, the significant fea-tures are selected using random forest method and it gets ex-tracted using the deep learning-based auto-encoder method. Third, the data classification has been done with the help of XG-boost classifier technique. At last, the performance of suggested model has been tested using TCGA-PANCAN da-taset then compared the performance with traditional method in terms of precision, recall, f-measure, accuracy, success rate, F-score and error rate.


Genome Sequence, Machine Learning, Bioinformatics, Dis-ease Prediction.