scispace - formally typeset
Search or ask a question
Book ChapterDOI

Soft Computing in Bioinformatics

01 Jan 2021-pp 431-446
TL;DR: In this paper, the authors explored the soft computing based techniques for bioinformatics and discussed the necessity of soft computing techniques and their compatibility for solving wide spectrum of bio-informatic related problems.
Abstract: In this chapter, we explored the soft computing based techniques for bioinformatics. Necessity of soft computing techniques and their compatibility for solving wide spectrum of bioinformatics related problems is reviewed. Basics of soft computing techniques are discussed and their relevancy in solving many bioinformatics based problems is also elaborated. Actual experimental results on two real world bioinformatics data demonstrated the efficacy of soft computing techniques over conventional one for biological data problems.
References
More filters
Journal ArticleDOI
TL;DR: This compilation and analysis of promoters with known transcriptional start points for E. coli genes should be useful for studies of promoter structure and function and for programs which identify potential promoter sequences.
Abstract: We have compiled and analyzed 263 promoters with known transcriptional start points for E. coli genes. Promoter elements (-35 hexamer, -10 hexamer, and spacing between these regions) were aligned by a program which selects the arrangement consistent with the start point and statistically most homologous to a reference list of promoters. The initial reference list was that of Hawley and McClure (Nucl. Acids Res. 11, 2237-2255, 1983). Alignment of the complete list was used for reference until successive analyses did not alter the structure of the list. In the final compilation, all bases in the -35 (TTGACA) and -10 (TATAAT) hexamers were highly conserved, 92% of promoters had inter-region spacing of 17 +/- 1 bp, and 75% of the uniquely defined start points initiated 7 +/- 1 bases downstream of the -10 region. The consensus sequence of promoters with inter-region spacing of 16, 17 or 18 bp did not differ. This compilation and analysis should be useful for studies of promoter structure and function and for programs which identify potential promoter sequences.

1,028 citations

Journal ArticleDOI
TL;DR: Modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization, are presented.
Abstract: This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.

805 citations

Journal ArticleDOI
01 Mar 2009
TL;DR: An up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering.
Abstract: This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the paper. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or nonoriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer, or real encodings, centroid-based, medoid-based, label-based, tree-based, or graph-based representations, among others. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.

690 citations

Journal ArticleDOI
TL;DR: The framework of multiobjective optimization is used to tackle the unsupervised learning problem, data clustering, following a formulation first proposed in the statistics literature and an evolutionary approach to the problem is developed.
Abstract: The framework of multiobjective optimization is used to tackle the unsupervised learning problem, data clustering, following a formulation first proposed in the statistics literature. The conceptual advantages of the multiobjective formulation are discussed and an evolutionary approach to the problem is developed. The resulting algorithm, multiobjective clustering with automatic k-determination, is compared with a number of well-established single-objective clustering algorithms, a modern ensemble technique, and two methods of model selection. The experiments demonstrate that the conceptual advantages of multiobjective clustering translate into practical and scalable performance benefits

657 citations

Journal ArticleDOI
TL;DR: The recent progress of SVMs in cancer genomic studies is reviewed and the strength of the SVM learning and its future perspective incancer genomic applications is comprehended.
Abstract: Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications.

635 citations