Data Analytics in Bioinformatics. Группа авторов
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very less as a result the network becomes too simple and may not be able to model complex data. Similarly, if the model is trained using too many neurons, it may take excessively long time for training the input data and there is a high chance of overfitting of data. In such case the network may begin to model some random noise in the output, as a result the model generalizes the training data extremely well, but fails to generalize new or future data. Integration of ANN algorithm with different optimization algorithms minimizes the error rate produced by the classification model which in result improves the model performance.
3.6 Conclusion
In this paper we have discussed the different applications of ANN related to different fields of bioinformatics. We have also made a comparative study between various machine learning algorithms and ANN algorithm to get some useful variants about how ANN works and what affects the performance of an ANN classification model and how the performance of the model can be improved to get more accurate result. The problems associated with the traditional approach to solve classification problem can be overcome with the concept of deep learning, which will allow faster learning by reducing the computational cost of the classification model even for the large dataset with inbuilt feature engineering that reduces the requirement of domain expertise. The observation from the study shows that the ANN and its variations can be used to solve complex problem of disease diagnostics or prognosis related with bioinformatics, resulting in the improved lifestyle and environment.
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