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Algorithmic complexity in computational biology: basics, challenges and limitations [article]

Davide Cirillo, Miguel Ponce-de-Leon, Alfonso Valencia
2021 arXiv   pre-print
Thus, the aim of this review is to survey the main algorithmic solutions to intractable problems in computational biology, highlighting the importance of High-Performance Computing in this area.  ...  The importance of defining the computational complexity of computational biology algorithms is a topic rarely surveyed for broad audiences of bioinformaticians and users of bioinformatics tools.  ...  Examples of heuristic approaches and methods for common complex algorithmic problems in computational biology.  ... 
arXiv:1811.07312v2 fatcat:f6qnbrilnrh3zomveqptsjbbju

On the choice of multiscale entropy algorithm for quantification of complexity in gait data

Peter C. Raffalt, William Denton, Jennifer M. Yentes
2018 Computers in Biology and Medicine  
The present study aimed at identifying a suitable multiscale entropy (MSE) algorithm for assessment of complexity in a stride-to-stride time interval time series.  ...  In general, acceptable sensitivity and good parameter consistency were observed for both algorithms; however, they were not able to differentiate the complexity of the stride-to-stride time interval time  ...  To address these issues, intrinsic mode entropy (IME) computes intrinsic mode functions of the signal in question and calculates the SaEn of the cumulative sums of each of the intrinsic mode functions  ... 
doi:10.1016/j.compbiomed.2018.10.008 pmid:30343216 pmcid:PMC6957257 fatcat:e2vmnanu6beyxhsqhc2y7afzba

A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection

Atiyeh Fotoohinasab, Toby Hocking, Fatemeh Afghah
2021 Computers in Biology and Medicine  
First, we define the constraint graph manually; then, we present a graph learning algorithm that can search for an optimal graph in a greedy scheme.  ...  We evaluate the performance of the algorithm using the MIT-BIH Arrhythmia Database.  ...  Computational Complexity As can be seen in Algorithm 1, the time complexity of the GCCD algorithm is theoretically proportional to the number of graph candidates at each iteration (Line 9) and the number  ... 
doi:10.1016/j.compbiomed.2021.104208 pmid:33484946 pmcid:PMC8026760 fatcat:ox6bjuzlcvauvdwl6bkwp54lfy

Modeling metal protein complexes from experimental extended X-ray absorption fine structure using evolutionary algorithms

Collin Price, Sheridan Houghten, Sergey Vassiliev, Doug Bruce
2014 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology  
The oxygen-evolving complex in S 1 is used as a benchmark for comparing the algorithms.  ...  Multiple population based algorithms, including a genetic algorithm, a restarting genetic algorithm, differential evolution, and particle swarm optimization, are studied for their effectiveness in structure  ...  Although these algorithms were successful in finding a new optimum for the oxygen-evolving complex in S 1 , future testing on other states of the oxygen-evolving complex or other complexes should be performed  ... 
doi:10.1109/cibcb.2014.6845524 dblp:conf/cibcb/PriceHVB14 fatcat:g7nlsmpr6jggzccgnnwoox66j4

On the computational complexity of the maximum parsimony reconciliation problem in the duplication-loss-coalescence model

Daniel Bork, Ricson Cheng, Jincheng Wang, Jean Sung, Ran Libeskind-Hadas
2017 Algorithms for Molecular Biology  
Conclusions: These intractability results are likely to guide future research on algorithmic aspects of the DLC-reconciliation problem.  ...  In the duplication-loss-coalescence (DLC) model, we seek a reconciliation that explains the incongruence between a gene and species tree using gene duplication, loss, and deep coalescence events.  ...  The reduction is based on the NP-hardness reduction in the previous section but introduces more complex gadgetry and uses nonzero cost for loss events.  ... 
doi:10.1186/s13015-017-0098-8 pmid:28316640 pmcid:PMC5349084 fatcat:6bnor5oedrbd5k7h7fdnfwrlj4

DifFUZZY: a fuzzy clustering algorithm for complex datasets

Ornella Cominetti, Anastasios Matzavinos, Sandhya Samarasinghe, Don Kulasiri, Sijia Liu, Philip K. Maini, Radek Erban
2010 International Journal of Computational Intelligence in Bioinformatics and Systems Biology  
The algorithm has been implemented in Matlab and C++ and is available at http://www.maths.ox.ac.uk/cmb/difFUZZY.  ...  A fuzzy clustering algorithm, DifFUZZY, which utilises concepts from diffusion processes in graphs and is applicable to a larger class of clustering problems than other fuzzy clustering algorithms is developed  ...  The research of S.L. is supported in part by an Alberta Wolfe Research Fellowship from the Iowa State University Mathematics department.  ... 
doi:10.1504/ijcibsb.2010.038222 fatcat:fcfjmvws3jhqvfrkudthjrjhwa

Association Mapping of Complex Diseases with Ancestral Recombination Graphs: Models and Efficient Algorithms [chapter]

Yufeng Wu
Lecture Notes in Computer Science  
both complex and Mendelian traits.  ...  We present novel efficient algorithms on extensions of the "phenotype likelihood" problem, a key step in the method in [35] .  ...  Here, we give an algorithm (Algorithm 1) for counting the number of selfderived ARGs for M . The algorithm runs in O(2 n + n 3 m) time.  ... 
doi:10.1007/978-3-540-71681-5_34 dblp:conf/recomb/Wu07 fatcat:xatnbepe35gbpf42ocbnqodtce

Review of "Bioinformatics: A Computing Perspective" edited by Shuba Gopal, Anne Haake, Rhys Price Jones and Paul Tymann

Dae-Won Kim, Hong-Seog Park
2009 Algorithms for Molecular Biology  
Topics include the organization and roles of a bioinformatics team and the challenges of computational algorithms in molecular biology.  ...  Many algorithms are built up in steps, showing how successive insights from both computation and biology can make existing techniques work better.  ... 
doi:10.1186/1748-7188-4-9 pmcid:PMC2705354 fatcat:hhbr7c5nmjblfpxq527fztjrme

Bioinformatics Algorithms [article]

Yu Geng
2020 figshare.com  
MATH 4981/6350:Introduction toBioinformatics Algorithms  ...  • Bioinformatics is a mutually beneficial collaboration between Biology and Computer Science. • For CS, Biology provides a motivation for studing new challenging problems and developing new algorithms.  ...  • Usually a biological problem can be transformed into a computational problem in a number of ways that feature different levels of accuracy and complexity.  ... 
doi:10.6084/m9.figshare.12981068.v1 fatcat:ney5srepgnhlre57uv4h3dnahq

EDITORIAL On the border between biology, mathematics and computer science

Piotr Formanowicz
2011 BioTechnologia  
On the other hand, systems biology is related to the systems analysis of biological complex objects which, in turn, includes the development of mathematical and algorithmic methods for such an analysis  ...  It may be said that, at least in some sense, bioinformatics and systems biology are parts of computational biology.  ...  On the other hand, systems biology is related to the systems analysis of biological complex objects which, in turn, includes the development of mathematical and algorithmic methods for such an analysis  ... 
doi:10.5114/bta.2011.46536 fatcat:aw3y2iduxzhlheds5jhocn3nju

Computationally Driven Experimental Biology

T M Murali
2012 Computer  
The complexity, diversity, and richness of experimental data on cellular systems are inspiring the development of computational analysis techniques that can directly prioritize and suggest new experiments  ...  Researchers in this field anticipate that the interplay between computation and biology will continue to yield improved quantitative models, novel and powerful algorithms, and sophisticated biological  ...  His article stresses that improvements in methods to model and measure combinatorial dysregulation of genes can lead to more refined algorithms to tease out the mechanisms that underlie complex diseases  ... 
pmid:24976642 pmcid:PMC4071611 fatcat:x42fgdto3zbyrfifv5on5lauti

Foreword

Sorin Istrail, Pavel Pevzner, Ron Shamir
1998 Discrete Applied Mathematics  
Foreword This is the second special volume of Discrete Applied Mathematics focusing on the development of new combinatorial and algorithmic techniques in computational molecular biology.  ...  new area of computational biology.  ...  This is the first computational biology paper providing connections with matroid theory. In the papers "On testing Consecutive Ones Property in parallel" by F.  ... 
doi:10.1016/s0166-218x(98)00109-7 fatcat:i326lfib3zfadb5cyirqgwvbra

Editorial: Special Section on High-Performance Computational Biology

S. Aluru, N.M. Amato, D.A. Bader
2006 IEEE Transactions on Parallel and Distributed Systems  
Certain design patterns repeatedly occur in algorithms for several computational biology applications.  ...  However, research in high-performance computational biology has not grown as rapidly as computational biology itself.  ...  His contributions to computational biology are in computational genomics, string algorithms, and parallel methods for solving large-scale problems arising in biology.  ... 
doi:10.1109/tpds.2006.102 fatcat:y6lpybmg3fcclhrd2xoc73kfua

Page 1269 of Mathematical Reviews Vol. , Issue 99b [page]

1991 Mathematical Reviews  
But, in my opinion, the main interest of the book resides in the description, by a specialist in algorithms, of problems and solu- tions arising in computational biology.  ...  In this model, the complexity of a computation is the number of cells accessed in the random access memory containing the data structure during the computation.  ... 

COMPUTATIONAL BIOLOGY

Matthew N. O. Sadiku, Yonghui Wang, Suxia Cui, Sarhan M. Musa
2018 International Journal of Advanced Research in Computer Science and Software Engineering  
It involves using computers to model biological problems and interpret data, especially problems in evolutionary and molecular biology.  ...  The application of computational tools to all areas of biology is producing excitements and insights into biological problems too complex for conventional approaches.  ...  Given the broad nature of the field of computational biology, it is impossible for students to master the full complexities and have a background in each area of the field.  ... 
doi:10.23956/ijarcsse.v8i6.616 fatcat:654ptrdpezfejbubxksbqevkti
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