Analysis of Biological Data: A Soft Computing Approach by Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T L Wang

By Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T L Wang

Bioinformatics, a box dedicated to the translation and research of organic facts utilizing computational concepts, has developed enormously lately as a result of the explosive progress of organic info generated via the medical neighborhood. smooth computing is a consortium of methodologies that paintings synergistically and offers, in a single shape or one other, versatile details processing features for dealing with real-life ambiguous occasions. a number of examine articles facing the appliance of soppy computing instruments to bioinformatics were released within the fresh previous; notwithstanding, they're scattered in numerous journals, convention complaints and technical stories, hence inflicting inconvenience to readers, scholars and researchers. This e-book, designated in its nature, is geared toward supplying a treatise in a unified framework, with either theoretical and experimental effects, describing the elemental ideas of soppy computing and demonstrating a few of the ways that they are often used for examining organic facts in a good demeanour. attention-grabbing examine articles from eminent scientists around the globe are introduced jointly in a scientific manner such that the reader should be capable of comprehend the problems and demanding situations during this area, the present methods of tackling them, fresh developments, and destiny instructions. This e-book is the 1st of its variety to compile vital examine components, smooth computing and bioinformatics, in an effort to exhibit how the instruments and strategies within the former can be utilized for successfully fixing a number of difficulties within the latter.

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Extra resources for Analysis of Biological Data: A Soft Computing Approach

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A new type of protein structure prediction methods, referred to as protein comparative modeling, were developed based on the same concept that proteins with similar sequences often share structures. 68 The accuracy of comparative protein modeling programs depends on two factors: the identification of an appropriate (known) structure as template, and the alignment between the template and the protein to be modeled (target). When no close homolog exists for modeling, sophisticated methods to address these problems are needed to improve the quality of comparative modeling.

Thus ungrouping an information system into components by some strategy and regrouping them into clusters by some other strategy can give rise to a new kind of information sub-systems. Determining the strategies for ungrouping and grouping, however, rests on the designer’s choice. g-generalization undoubtedly will re-discover fuzzy logic in a new form. 2. Neural networks Neurons are the fundamental building blocks of a biological nervous system. A neuron has four main structural components50,51 : the dendrites, the cell body, the axon and the synapse.

64 Obviously, an exhaustive searching for all potential motifs will guarantee detection of the motif if there is one in the input sequences. However, there is a tradeoff between the sensitivity and computer time. 6. Protein structure prediction All classical topics we discussed so far are about analyzing the sequences of biomolecules. There is a second source of bioinformatics research coming from the modern biochemistry. During the 1960s, soon after Sanger designed the experimental method for determining the sequence of a protein, Anfinsen concluded from his protein refolding experiments that the native structure of a protein can be determined from its sequence.

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