A Review on Data Mining and Continuous Optimization Applications in Computational Biology and Medicine

Date

2009-06-01

Author

Weber, Gerhard Wilhelm
Ozogur-Akyuz, Sureyya
Kropat, Erik

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

234
views

0
downloads

An emerging research area in computational biology and biotechnology is devoted to mathematical modeling and prediction of gene-expression patterns; it nowadays requests mathematics to deeply understand its foundations. This article surveys data mining and machine learning methods for an analysis of complex systems in computational biology, It mathematically deepens recent advances in modeling and prediction by rigorously introducing the environment and aspects of errors and uncertainty into the genetic context within the framework of matrix and interval arithmetics. Given the data from DNA microarray experiments and environmental measurements, we extract nonlinear ordinary differential equations which contain parameters that are to be determined. This is done by a generalized Chebychev approximation and generalized semi-infinite optimization. Then, time-discretized dynamical systems are studied. By a combinatorial algorithm which constructs and follows polyhedra sequences, the region of parametric stability is detected. In addition, we analyze the topological landscape of gene-environment networks in terms of structural stability. As a second strategy, we will review recent model selection and kernel learning methods for binary classification which can be used to classify microarray data for cancerous cells or for discrimination of other kind of diseases. This review is practically motivated and theoretically elaborated; it is devoted to a contribution to better health care, progress in medicine, a better education, and more healthy living conditions. Birth Defects Research (Part C) 87:165-181, 2009. (c) 2009 Wiley-Liss, Inc.

URI

https://hdl.handle.net/11511/57923

Journal

BIRTH DEFECTS RESEARCH PART C-EMBRYO TODAY-REVIEWS

DOI

https://doi.org/10.1002/bdrc.20151

Collections

Graduate School of Applied Mathematics, Article

Suggestions

OpenMETU
Core

On optimization, dynamics and uncertainty: A tutorial for gene-environment networks WEBER, G. -W.; Uğur, Ömür; Taylan, P.; TEZEL, AYSUN (2009-05-28) An emerging research area in computational biology and biotechnology is devoted to mathematical modeling and prediction of gene-expression patterns; to fully understand its foundations requires a mathematical study. This paper surveys and mathematically expands recent advances in modeling and prediction by rigorously introducing the environment and aspects of errors and uncertainty into the genetic context within the framework of matrix and interval arithmetic. Given the data from DNA microarray experiments...
An algorithmic approach to analyse genetic networks and biological energy production: an introduction and contribution where OR meets biology Uğur, Ömür; WEBER, G. -W.; WUENSCHIERS, R. (2009-01-01) An emerging research area in computational biology and biotechnology is devoted to modelling and prediction of gene-expression patterns. In this article, after a short review of recent achievements we deepen and extend them, especially, by emphasizing and analysing the elegant means of matrix algebra. Based on experimental data, ordinary differential equations with nonlinearities on the right-hand side and a generalized treatment of the absolute shift term, representing the environmental effects, are invest...
On generalized semi-infinite optimization of genetic networks Weber, Gerhard Wilhelm; Tezel, Aysun (2007-07-01) Since some years, the emerging area of computational biology is looking for its mathematical foundations. Based on modem contributions given to this area, our paper approaches modeling and prediction of gene-expression patterns by optimization theory, with a special emphasis on generalized semi-infinite optimization. Based on experimental data, nonlinear ordinary differential equations are obtained by the optimization of least-squares errors. The genetic process can be investigated by a time-discretization ...
A New Mathematical Approach in Environmental and Life Sciences: Gene-Environment Networks and Their Dynamics Weber, Gerhard Wilhelm; Alparslan-Gok, S. Z.; Soyler, B. (Springer Science and Business Media LLC, 2009-04-01) An important research area in life sciences is devoted to modeling, prediction, and dynamics of gene-expression patterns. As clearly understood in these days, this enterprise cannot become satisfactory without acknowledging the role of the environment. To a representation of past, present, and most likely future states, we also encounter measurement errors and uncertainties. This paper surveys and improves recent advances in understanding the foundations and interdisciplinary implications of the newly intro...
Analysis and prediction of gene expression patterns by dynamical systems, and by a combinatorial algorithm Taştan, Mesut; Weber, Gerhard Wilhelm; Department of Scientific Computing (2005) Modeling and prediction of gene-expression patterns has an important place in computational biology and bioinformatics. The measure of gene expression is determined from the genomic analysis at the mRNA level by means of microarray technologies. Thus, mRNA analysis informs us not only about genetic viewpoints of an organism but also about the dynamic changes in environment of that organism. Different mathematical methods have been developed for analyzing experimental data. In this study, we discuss the mode...

Citation Formats

G. W. Weber, S. Ozogur-Akyuz, and E. Kropat, “A Review on Data Mining and Continuous Optimization Applications in Computational Biology and Medicine,” BIRTH DEFECTS RESEARCH PART C-EMBRYO TODAY-REVIEWS, pp. 165–181, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57923.