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• Boosting alignment accuracy by adaptive local realignment.- A concurrent subtractive assembly approach for identification of disease associated sub-meta-genomes.- A flow procedure for the linearization of genome variation graphs.- Dynamic alignment-free and reference-free read compression.- A fast approximate algorithm for mapping long reads to large reference databases.- Determining the consistency of resolved triplets and fan triplets.- Progressive calibration and averaging for tandem mass spectrometry statistical confidence estimation: Why settle for a single decoy.- Resolving multi-copy duplications de novo using polyploid phasing.- A Bayesian active learning experimental design for inferring signaling networks.- BBK* (Branch and Bound over K*): A provable and efficient ensemble-based algorithm to optimize stability and binding affinity over large sequence spaces.- Super-bubbles, ultra-bubbles and cacti.- EPR-dictionaries: A practical and fast data structure for constant time searches in unidirectional and bidirectional FM indices.- A Bayesian framework for estimating cell type composition from DNA methylation without the need for methylation reference.- Towards recovering Allele-specific cancer genome graphs.- Using stochastic approximation techniques to efficiently construct confidence intervals for heritability.- Improved search of large transcriptomic sequencing databases using split sequence bloom trees.- All some sequence bloom trees.- Longitudinal genotype-phenotype association study via temporal structure auto-learning predictive model.- Improving imputation accuracy by inferring causal variants in genetic studies.- The copy-number tree mixture deconvolution problem and applications to multi-sample bulk sequencing tumor data.- Quantifying the impact of non-coding variants on transcription factor-DNA binding.- aBayesQR: A Bayesian method for reconstruction of viral populations characterized by low diversity.- BeWith: A between-within method for module discovery in cancer using integrated analysis of mutual exclusivity, co-occurrence and functional interactions.- K-mer Set Memory (KSM) motif representation enables accurate prediction of the impact of regulatory variants.- Network-based coverage of mutational profiles reveals cancer genes.- Ultra-accurate complex disorder prediction: case study of neurodevelopmental disorders.- Inference of the human polyadenylation Code.- Folding membrane proteins by deep transfer learning.- A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.- Epistasis in genomic and survival data of cancer patients.- Ultra-fast identity by descent detection in biobank-scale cohorts using positional burrows-wheeler transform.- Joker de Bruijn: sequence libraries to cover all k-mers using joker characters.- GATTACA: Lightweight metagenomic binning using kmer counting.- Species tree estimation using ASTRAL: how many genes are enough.- Reconstructing antibody repertoires from error-prone immune-sequencing datasets.- NetREX: Network rewiring using EXpression - Towards context specific regulatory networks.- E pluribus unum: United States of single cells.- ROSE: a deep learning based framework for predicting ribosome stalling. .
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 21th Annual Conference on Research in Computational Molecular Biology, RECOMB 2017, held in Hong Kong, China, in May 2017. The 22 regular papers presented in this volume were carefully reviewed and selected from 184 submissions. 16 short abstracts are included in the back matter of the volume. They report on original research in all areas of computational molecular biology and bioinformatics.
(source: Nielsen Book Data)

• Computing the rearrangement distance of natural genomes / Leonard Bohnenkämper, Marília D. V. Braga, Daniel Doerr, Jens Stoye
• Deep large-scale multi-task learning network for gene expression inference / Kamran Ghasedi Dizaji, Wei Chen, Heng Huang
• A randomized parallel algorithm for efficiently finding near-optimal universal hitting sets / Barış Ekim, Bonnie Berger, Yaron Orenstein
• Multiple competition-based FDR control and its application to peptide detection / Kristen Emery, Syamand Hasam, William Stafford Noble, Uri Keich
• Supervised adversarial alignment of single-cell RNA-seq data / Songwei Ge, Haohan Wang, Amir Alavi, Eric Xing, Ziv Bar-Joseph
• Bagging MSA learning : enhancing low-quality PSSM with deep learning for accurate rotein structure property prediction / Yuzhi Guo, Jiaxiang Wu, Hehuan Ma, Sheng Wang, Junzhou Huang
• AStarix : fast and optimal sequence-to-graph alignment / Pesho Ivanov, Benjamin Bichsel, Harun Mustafa, André Kahles, Gunnar Rätsch, Martin Vechev
• Polynomial-time statistical estimation of species trees under gene duplication and loss / Brandon Legried, Erin K. Molloy, Tandy Warnow, Sébastien Roch
• RoboCOP : multivariate state space model integrating epigenomic accessibility data to elucidate genome-wide chromatin occupancy / Sneha Mitra, Jianling Zhong, David M. MacAlpine, Alexander J. Hartemink
• Representation of k-mer sets using spectrum-preserving string sets / Amatur Rahman, Paul Medvedev
• NetMix : a network-structured mixture model for reduced-bias estimation of altered subnetworks / Matthew A. Reyna, Uthsav Chitra, Rebecca Elyanow, Benjamin J. Raphael
• Stochastic sampling of structural contexts improves the scalability and accuracy of RNA 3D module identification / Roman Sarrazin-Gendron, Hua-Ting Yao, Vladimir Reinharz, Carlos G. Oliver, Yann Ponty, Jérôme Waldispühl

• Lower density selection schemes via small universal hitting sets with short remaining path length / Hongyu Zheng, Carl Kingsford, Guillaume Marçais
• Strain-aware assembly of genomes from mixed samples using flow variation graphs / Jasmijn A. Baaijens, Leen Stougie, Alexander Schönhuth
• Spectral Jaccard similarity : a new approach to estimating pairwise sequence alignments / Tavor Z. Baharav, Govinda M. Kamath, David N. Tse, Ilan Shomorony
• MosaicFlye : resolving long mosaic repeats using long reads / Anton Bankevich, Pavel Pevzner
• Bayesian non-parametric clustering of single-cell mutation profiles / Nico Borgsmüller, Jose Bonet, Francesco Marass, Abel Gonzalez-Perez, Nuria Lopez-Bigas, Niko Beerenwinkel
• PaccMannRL : designing anticancer drugs from transcriptomic data via reinforcement learning / Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, María Rodríguez Martínez
• CluStrat : a structure informed clustering strategy for population stratification / Aritra Bose, Myson C. Burch, Agniva Chowdhury, Peristera Paschou, Petros Drineas
• PWAS : proteome-wide association study / Nadav Brandes, Nathan Linial, Michal Linial
• Estimating the rate of cell type degeneration from epigenetic sequencing of cell-free DNA / Christa Caggiano, Barbara Celona, Fleur Garton, Joel Mefford, Brian Black, Catherine Lomen-Hoerth et al.
• Potpourri : an epistasis test prioritization algorithm via diverse SNP selection / Gizem Caylak, A. Ercument Cicek
• Privacy-preserving biomedical database queries with optimal privacy-utility trade-offs / Hyunghoon Cho, Sean Simmons, Ryan Kim, Bonnie Berger
• Iterative refinement of cellular identity from single-cell data using online learning / Chao Gao, Joshua D. Welch
• A guided network propagation approach to identify disease genes that combines prior and new information / Borislav H. Hristov, Bernard Chazelle, Mona Singh

• A scalable method for estimating the regional polygenicity of complex traits / Ruth Johnson, Kathryn S. Burch, Kangcheng Hou, Mario Paciuc, Bogdan Pasaniuc, Sriram Sankararaman
• Efficient and accurate inference of microbial trajectories from longitudinal count data / Tyler A. Joseph, Amey P. Pasarkar, Itsik Pe'er
• Identifying causal variants by fine mapping across multiple studies / Nathan LaPierre, Kodi Taraszka, Helen Huang, Rosemary He, Farhad Hormozdiari, Eleazar Eskin
• MONN : a multi-objective neural network for predicting pairwise non-covalent interactions and binding affinities between compounds and proteins / Shuya Li, Fangping Wan, Hantao Shu, Tao Jiang, Dan Zhao, Jianyang Zeng
• Evolutionary context-integrated deep sequence modeling for protein engineering / Yunan Luo, Lam Vo, Hantian Ding, Yufeng Su, Yang Liu, Wesley Wei Qian et al.
• Log transformation improves dating of phylogenies / Uyen Mai, Siavash Mirarab
• Reconstructing genotypes in private genomic databases from genetic risk scores / Brooks Paige, James Bell, Aurélien Bellet, Adrià Gascón, Daphne Ezer
• d-PBWT : dynamic positional Burrows-Wheeler transform / Ahsan Sanaullah, Degui Zhi, Shaojie Zhang
• A mixture model for signature discovery from sparse mutation data / Itay Sason, Yuexi Chen, Mark D. M. Leiserson, Roded Sharan
• Single-cell tumor phylogeny inference with copy-number constrained mutation losses / Gryte Satas, Simone Zaccaria, Geoffrey Mon, Benjamin J. Raphael
• Reconstruction of gene regulatory networks by integrating biological mode and a recommendation system / Yijie Wang, Justin M. Fear, Isabelle Berger, Hangnoh Lee, Brian Oliver, Teresa M. Przytycka
• Probing multi-way chromatin interaction with hypergraph representation learning / Ruochi Zhang, Jian Ma.

This book constitutes the proceedings of the 24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020, held in Padua, Italy, in May 2020. The 13 regular and 24 short papers presented were carefully reviewed and selected from 206 submissions. The papers report on original research in all areas of computational molecular biology and bioinformatics.

This book constitutes the proceedings of the 22nd Annual Conference on Research in Computational Molecular Biology, RECOMB 2018, held in Paris, France, in April 2018. The 16 extended and 22 short abstracts presented were carefully reviewed and selected from 193 submissions. The short abstracts are included in the back matter of the volume. They report on original research in all areas of computational molecular biology and bioinformatics.
(source: Nielsen Book Data)

• Mitochondrial Haplogroup Assignment for High-Throughput Sequencing Data from Single Individual and Mixed DNA Samples.- Signet Ring Cell Detection with Classi cation Reinforcement Detection Network.- SPOC: Identification of Drug Targets in Biological Networks via Set Preference Output Control.- Identification of a novel compound heterozygous variant in NBAS causing bone fragility by the type of osteogenesis imperfecta.- Isoform-disease association prediction by data fusion.- EpIntMC: Detecting Epistatic Interactions using Multiple Clusterings.- Improving Metagenomic Classi cation using discriminative k-mers from sequencing data.- Dilated-DenseNet For Macromolecule Classifi cation In Cryo-electron Tomography.- Ess-NEXG: Predict Essential Proteins by Constructing a Weighted.- Protein Interaction Network based on Node Embedding and XGBoost.- mapAlign: an efficient approach for mapping and aligning long reads to reference genomes.- Functional Evolutionary Modeling Exposes Overlooked Protein-Coding Genes Involved in Cancer.- Testing the Agreement of Trees with Internal Labels.- SVLR: Genome Structure Variant Detection Using Long Read Sequencing Data.- De novo prediction of drug-target interaction via Laplacian regularized Schatten-p norm minimization.- Diagnosis of ASD from rs-fMRIs based on brain dynamic networks.- miRNA-Disease Associations Prediction Based on Negative Sample Selection and Multi-layer Perceptron.- Checking Phylogenetic Decisiveness in Theory and in Practice.- TNet: Phylogeny-Based Inference of Disease Transmission Networks Using Within-Host Strain Diversity.- Cancer breakpoint hotspots versus individual breakpoints prediction by machine learning models.- Integer Linear Programming Formulation for the Uni ed DuplicationLoss-Coalescence Model.- In silico-guided discovery of potential HIV-1 entry inhibitors mimicking bNAb
• N6: virtual screening, docking, molecular dynamics, and post-molecular modeling analysis.- Learning Structural Genetic Information via Graph Neural Embedding.- A New Network-based Tool to Analyse Competing Endogenous RNAs.- Deep Ensemble models for 16S Ribosomal Gene Classification.- Search for tandem repeats in the rst chromosome from the rice genome.- Deep Learning approach with rotate-shift invariant input to predict protein homodimer structure.- Development of a Neural Network-Based Approach for Prediction of Potential HIV-1 Entry Inhibitors Using Deep Learning and Molecular Modeling Methods.- In Silico Design and Evaluation of Novel Triazole-Based Compounds as Promising Drug Candidates Against Breast Cancer.- Identification of essential genes with NemoPro le and various machine learning models.- NemoLib: Network Motif Libraries for network motif detection and analysis.- Estimating enzyme participation in metabolic pathways for microbial communities from RNA-seq data.- Identication of Virus-Receptor Interactions based on Network Enhancement and Similarity.- Enhanced functional pathway annotations for differentially expressed gene clusters.- Automated Detection of Sleep Apnea from Abdominal Respiratory Signal using Hilbert-Huang Transform.- Na/K-ATPase glutathionylation: in silico modeling of reaction mechanisms.- HiChew: a tool for TAD clustering in embryogenesis.- Generation of Hi-C maps from DNA sequence data using Deep Learning.- S
• C1: A Tool for Interactive Web-Based Single Cell RNA-Seq Data Analysis.- Quantitative analysis of the dynamics of maternal gradients in the early Drosophila embryo.- Atom Tracking Using Cayley Graphs.- SPOC: Identification of Drug Targets in Biological Networks via Set Preference Output Control.- Identification of a novel compound heterozygous variant in NBAS causing bone fragility by the type of osteogenesis imperfecta. .
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020, held in Moscow, Russia, in December 2020. The 23 full papers and 18 short papers presented in this book were carefully reviewed and selected from 131 submissions. They were organized in topical sections named: genome analysis; systems biology; computational proteomics; machine and deep learning; and data analysis and methodology.
(source: Nielsen Book Data)