Dimensional Reduction in the Protein Secondary Structure Prediction — Nonlinear Method Improvements

Simas, Gisele M.; Botelho, Silvia Silva da Costa; Grando, Neusa; Colares, Rafael

doi:10.1007/978-3-540-74972-1_55

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…SVM is superior in predicting the location of turns [160]; in ubiquitin protein structure prediction, SVM is superior to both ANN and HMM [161]; SVM requires a relatively small training set to avoid overfitting of the data [162]; ANN have much better accuracy and take much less training and computation time [163]; SVM require much larger memory and powerful processor [163]; SVM outperformed ANN with an overall accuracy of 89.3% in identification of lipid-binding proteins (LPBs) from non-LBPs [164] Other -Nearest-neighbour method had an overall three-state accuracy of 72%, higher than neural network [165]; nonlinear dimensional reduction in protein secondary structure prediction yielded similar results compared to ANN [166] methods for highlighting components across networks [181]. Additionally, for multivariate data visualization, Kuntal and Mande [182] offered a web-based platform (Web-Igloo) which is useful for visual DM.…”

Section: Bayesian Network (Bn)mentioning

confidence: 99%

Intelligent mining of large-scale bio-data: Bioinformatics applications

Hashemi

Ismail

Rafii

et al. 2017

Biotechnology & Biotechnological Equipment

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Today, there is a collection of a tremendous amount of bio-data because of the computerized applications worldwide. Therefore, scholars have been encouraged to develop effective methods to extract the hidden knowledge in these data. Consequently, a challenging and valuable area for research in artificial intelligence has been created. Bioinformatics creates heuristic approaches and complex algorithms using artificial intelligence and information technology in order to solve biological problems. Intelligent implication of the data can accelerate biological knowledge discovery. Data mining, as biology intelligence, attempts to find reliable, new, useful and meaningful patterns in huge amounts of data. Hence, there is a high potential to raise the interaction between artificial intelligence and bio-data mining. The present paper argues how artificial intelligence can assist bio-data analysis and gives an up-to-date review of different applications of bio-data mining. It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. Important and new techniques are critically discussed for intelligent knowledge discovery of different types of row datasets with applicable examples in human, plant and animal sciences. Finally, a broad perception of this hot topic in data science is given.

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