Computational intelligence approaches for pattern discovery in biological systems.
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TLDR
This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.Abstract:
Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.read more
Citations
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Machine-learning approaches in drug discovery: methods and applications.
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Machine learning techniques and drug design.
Jadson Castro Gertrudes,Vinícius Gonçalves Maltarollo,Ricardo Silva,Patrícia R. Oliveira,Káthia Maria Honório,A. B. F. da Silva +5 more
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John H. Phan,Richard A. Moffitt,Todd H. Stokes,Jian Liu,Andrew N. Young,Shuming Nie,Shuming Nie,May D. Wang +7 more
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Artificial Neural Networks Trained to Detect Viral and Phage Structural Proteins
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TL;DR: A method of predicting phage structural protein sequences that uses Artificial Neural Networks, which are new tools for identifying phage and potential prophage structural proteins that are difficult or impossible to detect by other bioinformatic analysis.
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Fragment informatics and computational fragment-based drug design: an overview and update.
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References
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