06113nam 2200529 450 99646450340331620220127124749.03-030-74432-9(CKB)4100000011949913(MiAaPQ)EBC6633491(Au-PeEL)EBL6633491(OCoLC)1253548131(PPN)25588138X(EXLCZ)99410000001194991320220127d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAlgorithms for computational biology 8th international conference, AlCoB 2021 Missoula, MT, USA, June 7-11, 2021 proceedings /Carlos Martin-Vide, Miguel A. Vega-Rodriguez, Travis Wheeler, editorsCham, Switzerland :Springer,[2021]©20211 online resource (177 pages)Lecture Notes in Computer Science ;127153-030-74431-0 Intro -- Preface -- Organization -- Contents -- Biological Dynamical Systems and Other Biological Processes -- Learning Molecular Classes from Small Numbers of Positive Examples Using Graph Grammars -- 1 Introduction -- 2 Related Work -- 3 Learning the Graph Grammar -- 3.1 Learning a Graph-Grammar -- 4 Experiments -- 4.1 Comparison to Machine Learning on SMILES -- 4.2 Comparison to Standard Machine Learning Using Graph-Grammar Rules as Features -- 5 Conclusion -- References -- Can We Replace Reads by Numeric Signatures? Lyndon Fingerprints as Representations of Sequencing Reads for Machine Learning -- 1 Introduction -- 2 Lyndon Factorizations and Overlapping Reads -- 3 Probabilistic Behaviour of Fingerprints -- 4 Methodology and Experiments -- 4.1 Fingerprint-Based Approach -- 4.2 k-Finger-Based Approach -- 5 Discussion -- References -- Exploiting Variable Sparsity in Computing Equilibria of Biological Dynamical Systems by Triangular Decomposition -- 1 Introduction -- 2 Preliminaries -- 2.1 Autonomous Biological Dynamical Systems and Their Equilibria -- 2.2 Polynomial Sets and Associated Graphs -- 2.3 Triangular Sets and Sparse Triangular Decomposition -- 3 Variable Sparsity in Biological Dynamical Systems -- 3.1 Original Variable Sparsity -- 3.2 Variable Sparsity After Chordal Completion -- 4 Sparse Triangular Decomposition for Computing Equilibria -- 5 Concluding Remarks -- References -- A Recovery Algorithm and Pooling Designs for One-Stage Noisy Group Testing Under the Probabilistic Framework -- 1 Introduction -- 2 Methods -- 2.1 Noisy Group Testing Under the Probabilistic Framework -- 2.2 A Group Testing Protocol -- 2.3 Recovery Algorithm -- 3 Results -- 3.1 Performance of the Recovery Algorithm -- 3.2 Pooling Matrices -- 4 Discussion -- References -- Phylogenetics -- Novel Phylogenetic Network Distances Based on Cherry Picking.1 Introduction -- 2 Preliminaries -- 2.1 Network Definitions -- 2.2 Cherry Operations -- 3 Distances Based on Cherry-Picking Sequences -- 4 Computing the Tail Distance Between Two Trees -- 4.1 Computing an LR-MAST -- 5 NP-hardness of dtail Between a Tree and a Network -- 6 Discussion and Future Work -- 1 Proof of Lemma 1 (Page 6) -- 2 Proof of Theorems in Sect.3 (Theorem 2 Page 7, Theroem 3 Page 8, Theorem 4 Page 8, Theorem 5 Page 8) -- 3 Proof of Lemma 6, 7 and Theorem 8 (Page 9) -- 4 LR-MAST Algorithm, and Associated Theorem (Page 9) -- 5 Proof of Theorem 10 (Page 12) and Theorem 11 (Page 12) -- References -- Best Match Graphs with Binary Trees -- 1 Introduction -- 2 Best Match Graphs -- 3 Binary Trees Explaining a BMG in Near Cubic Time -- 4 The Binary-Refinable Tree of a BMG -- 5 Simulation Results -- 6 Concluding Remarks -- References -- Scalable and Accurate Phylogenetic Placement Using pplacer-XR -- 1 Introduction -- 2 pplacer-XR -- 3 Experimental Study Design -- 4 Results -- 4.1 Query Placement Accuracy -- 4.2 Time Analysis -- 5 Conclusions -- References -- Comparing Methods for Species Tree Estimation with Gene Duplication and Loss -- 1 Introduction -- 2 Experimental Design -- 3 Results -- 4 Discussion and Conclusion -- References -- Sequence Alignment and Genome Rearrangement -- Reversal Distance on Genomes with Different Gene Content and Intergenic Regions Information -- 1 Introduction -- 2 Background -- 3 Labeled Intergenic Breakpoint Graph -- 4 3-Approximation Algorithm -- 5 Conclusions -- References -- Reversals Distance Considering Flexible Intergenic Regions Sizes -- 1 Introduction -- 2 Definitions -- 2.1 Flexible Weighted Cycle Graph -- 3 Results -- 3.1 Lower Bound -- 3.2 Approximation Algorithm -- 4 Conclusion -- References -- Improved DNA-versus-Protein Homology Search for Protein Fossils -- 1 Introduction -- 2 Methods.2.1 Alignment Elements -- 2.2 Scoring Scheme -- 2.3 Finding a Maximum-Score Local Alignment -- 2.4 Probability Model -- 2.5 Sum over All Alignments Passing Through (i,j) -- 2.6 Significance Calculation -- 2.7 Seed-and-Extend Heuristic -- 2.8 Fitting Substitution and Gap Parameters to Sequence Data -- 3 Results -- 3.1 Software -- 3.2 Parameter Fitting -- 3.3 Significance Calculation and Simple Sequences -- 3.4 Comparison to Blastx -- 3.5 Discovery of Missing TE Orders in the Human Genome -- 4 Discussion -- References -- The Maximum Weight Trace Alignment Merging Problem -- 1 Introduction -- 2 Maximum Weight Trace Alignment Merging -- 3 Graph Clustering Merger (GCM) -- 4 Experimental Study -- 5 Results -- 6 Conclusions -- References -- Author Index.Lecture notes in computer science ;12715.Computational biologyCongressesAlgorithmsCongressesAlgorithmsComputational biologyAlgorithmsAlgorithms.570.285Martín Vide CarlosVega-Rodríguez Miguel A.Wheeler TravisMiAaPQMiAaPQMiAaPQBOOK996464503403316Algorithms for Computational Biology2077899UNISA