Open AccessBook
Integer Linear Programming in Computational and Systems Biology: An Entry-Level Text and Course
Dan Gusfield
- 01 Aug 2019
21
TL;DR: This paper uses in-depth examples from genomics, phylogenetics, RNA, protein folding, network analysis, cancer, ecology, co-evolution, DNA sequencing, sequence analysis, pedigree and sibling inference, haplotyping, and more, to establish the power of ILP.
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Abstract: Integer linear programming (ILP) is a versatile modeling and optimization technique that is increasingly used in non-traditional ways in biology, with the potential to transform biological computation. However, few biologists know about it. This how-to and why-do text introduces ILP through the lens of computational and systems biology. It uses in-depth examples from genomics, phylogenetics, RNA, protein folding, network analysis, cancer, ecology, co-evolution, DNA sequencing, sequence analysis, pedigree and sibling inference, haplotyping, and more, to establish the power of ILP. This book aims to teach the logic of modeling and solving problems with ILP, and to teach the practical 'work flow' involved in using ILP in biology. Written for a wide audience, with no biological or computational prerequisites, this book is appropriate for entry-level and advanced courses aimed at biological and computational students, and as a source for specialists. Numerous exercises and accompanying software (in Python and Perl) demonstrate the concepts.
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Citations
Metabolite discovery through global annotation of untargeted metabolomics data
Li Chen,Li Chen,Wenyun Lu,Lin Wang,Xi Xing,Ziyang Chen,Ziyang Chen,Xin Teng,Xianfeng Zeng,Antonio D Muscarella,Yihui Shen,Alexis J. Cowan,Melanie R. McReynolds,Brandon J Kennedy,Ashley M. Lato,Shawn R. Campagna,Mona Singh,Joshua D. Rabinowitz +17 more
TL;DR: In this article, a global network optimization approach, NetID, was proposed to annotate untargeted LC-MS metabolomics data, which aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and tandem mass spectrometry fragmentation patterns.
Metabolite discovery through global annotation of untargeted metabolomics data
Li Chen,Li Chen,Wenyun Lu,Lin Wang,Xi Xing,Xin Teng,Xianfeng Zeng,Antonio D Muscarella,Yihui Shen,Alexis J. Cowan,Melanie R. McReynolds,Brandon J Kennedy,Ashley M. Lato,Shawn R. Campagna,Mona Singh,Joshua D. Rabinowitz +15 more
TL;DR: This work presents a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data, and identifies a half-dozen novel metabolites, including thiamine and taurine derivatives.
105
PhISCS-BnB: A Fast Branch and Bound Algorithm for the Perfect Tumor Phylogeny Reconstruction Problem
Erfan Sadeqi Azer,Farid Rashidi Mehrabadi,Farid Rashidi Mehrabadi,Xuan Cindy Li,Xuan Cindy Li,Salem Malikic,Alejandro A. Schäffer,E. Michael Gertz,Chi-Ping Day,Eva Pérez-Guijarro,Kerrie L. Marie,Maxwell P. Lee,Glenn Merlino,Funda Ergün,S. Cenk Sahinalp +14 more
TL;DR: PhISCS-BnB, a Branch and Bound algorithm to compute the most likely perfect phylogeny (PP) on an input genotype matrix extracted from a SCS data set, which not only offers an optimality guarantee, but is also 10 to 100 times faster than the best available methods on simulated tumorSCS data.
Time-Cost-Quality Trade-off Model for Optimal Pile Type Selection Using Discrete Particle Swarm Optimization Algorithm
Hanaa H. Lateef,Abbas M. Burhan +1 more
TL;DR: The main objective of this study is to solve the time–cost–quality trade-off (TCQT) problem by finding an optimal pile type with the target of "minimizing the cost and time while "maximizing" quality.
Molecular 'barcodes' reveal lost whale hunts.
TL;DR: A new technique for identifying species from short snippets of DNA in bone scraps has enabled researchers to identify the remains of five whale species in early New Zealand settlements, including smaller or slow-moving species, the likely targets for hunters.
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Modularity and community structure in networks
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Fast unfolding of communities in large networks
Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
TL;DR: In this paper, the authors proposed a simple method to extract the community structure of large networks based on modularity optimization, which is shown to outperform all other known community detection methods in terms of computation time.
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Network Flows: Theory, Algorithms, and Applications
Ravindra K. Ahuja,Thomas L. Magnanti,James B. Orlin +2 more
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TL;DR: In-depth, self-contained treatments of shortest path, maximum flow, and minimum cost flow problems, including descriptions of polynomial-time algorithms for these core models are presented.
A new statistical method for haplotype reconstruction from population data.
TL;DR: A new statistical method is presented, applicable to genotype data at linked loci from a population sample, that improves substantially on current algorithms and performs well in absolute terms, suggesting that reconstructing haplotypes experimentally or by genotyping additional family members may be an inefficient use of resources.
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