20,929 research outputs found
On the Investigation of Biological Phenomena through Computational Intelligence
This paper is largely devoted for building a novel approach which is able to explain biological phenomena like splicing promoter gene identification disease and disorder identification and to acquire and exploit biological data This paper also presents an overview on the artificial neural network based computational intelligence technique to infer and analyze biological information from wide spectrum of complex problems Bioinformatics and computational intelligence are new research area which integrates many core subjects such as chemistry biology medical science mathematics computer and information science Since most of the problems in bioinformatics are inherently hard ill defined and possesses overlapping boundaries Neural networks have proved to be effective in solving those problems where conventional com-putation tools failed to provide solution Our experiments demonstrate the endeavor of biological phenomena as an effec-tive description for many intelligent applications Having a computational tool to predict genes and other meaningful in-formation is therefore of great value and can save a lot of expensive and time consuming experiments for biologists This paper will focus on issues related to design methodology comprising neural network to analyze biological information and investigate them for powerful application
Evaluation of IoT-Based Computational Intelligence Tools for DNA Sequence Analysis in Bioinformatics
In contemporary age, Computational Intelligence (CI) performs an essential
role in the interpretation of big biological data considering that it could
provide all of the molecular biology and DNA sequencing computations. For this
purpose, many researchers have attempted to implement different tools in this
field and have competed aggressively. Hence, determining the best of them among
the enormous number of available tools is not an easy task, selecting the one
which accomplishes big data in the concise time and with no error can
significantly improve the scientist's contribution in the bioinformatics field.
This study uses different analysis and methods such as Fuzzy, Dempster-Shafer,
Murphy and Entropy Shannon to provide the most significant and reliable
evaluation of IoT-based computational intelligence tools for DNA sequence
analysis. The outcomes of this study can be advantageous to the bioinformatics
community, researchers and experts in big biological data
Soft Computing, Artificial Intelligence, Fuzzy Logic & Genetic Algorithm in Bioinformatics
Abstract Soft computing is creating several possibilities in bioinformatics, especially by generating low-cost, low precision (approximate), good solutions. Bioinformatics is an interdisciplinary research area that is the interface between the biological and computational sciences. Bioinformatics deals with algorithms, databases and information systems, web technologies, artificial intelligence and soft computing, information and computation theory, structural biology, software engineering, data mining, image processing, modeling and simulation, discrete mathematics, control and system theory, circuit theory, and statistics. Despite of a high number of techniques specifically dedicated to bioinformatics problems as well as many successful applications, we are in the beginning of a process to massively integrate the aspects and experiences in the different core subjects such as biology, medicine, computer science, engineering, and mathematics. Recently the use of soft computing tools for solving bioinformatics problems have been gaining the attention of researchers because of their ability to handle imprecision, uncertainty in large and complex search spaces. The paper will focus on soft computing paradigm in bioinformatics with particular emphasis on integrative research
What is Computational Intelligence and where is it going?
What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed
Computational Protein Design Using AND/OR Branch-and-Bound Search
The computation of the global minimum energy conformation (GMEC) is an
important and challenging topic in structure-based computational protein
design. In this paper, we propose a new protein design algorithm based on the
AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional
branch-and-bound search algorithm, to solve this combinatorial optimization
problem. By integrating with a powerful heuristic function, AOBB is able to
fully exploit the graph structure of the underlying residue interaction network
of a backbone template to significantly accelerate the design process. Tests on
real protein data show that our new protein design algorithm is able to solve
many prob- lems that were previously unsolvable by the traditional exact search
algorithms, and for the problems that can be solved with traditional provable
algorithms, our new method can provide a large speedup by several orders of
magnitude while still guaranteeing to find the global minimum energy
conformation (GMEC) solution.Comment: RECOMB 201
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
Many machine learning problems can be formulated as predicting labels for a
pair of objects. Problems of that kind are often referred to as pairwise
learning, dyadic prediction or network inference problems. During the last
decade kernel methods have played a dominant role in pairwise learning. They
still obtain a state-of-the-art predictive performance, but a theoretical
analysis of their behavior has been underexplored in the machine learning
literature.
In this work we review and unify existing kernel-based algorithms that are
commonly used in different pairwise learning settings, ranging from matrix
filtering to zero-shot learning. To this end, we focus on closed-form efficient
instantiations of Kronecker kernel ridge regression. We show that independent
task kernel ridge regression, two-step kernel ridge regression and a linear
matrix filter arise naturally as a special case of Kronecker kernel ridge
regression, implying that all these methods implicitly minimize a squared loss.
In addition, we analyze universality, consistency and spectral filtering
properties. Our theoretical results provide valuable insights in assessing the
advantages and limitations of existing pairwise learning methods.Comment: arXiv admin note: text overlap with arXiv:1606.0427
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