Search Results - computing algorithms and ((bioinformatics OR (bioinformatika OR bioinformation)) OR informatika)

High Throughput Computational Studies of Macromolecular Structure Flexibility

Authors: Hospital Gasch, Adam

Contributors: University/Department: Universitat de Barcelona. Departament de Bioquímica i Biologia Molecular (Farmàcia)

Thesis Advisors: Gelpí Buchaca, Josep Lluís, Orozco López, Modesto

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Dinàmica molecular, Dinámica molecular, Molecular dynamics, Bioinformàtica, Bioninformática, Bioinformatics, Càlcul intensiu (Informàtica), Computación de altas prestaciones, High performance computing, Ciències Experimentals i Matemàtiques

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Access URL: http://hdl.handle.net/10803/284440

Derivation and Analysis of Distributed Computing Algorithms in Biological Systems

Authors: Chandrasekhar, Arjun

Subject Terms: Bioinformatics, Computer science, Ecology, Algorithms in Nature, Ant Biology, Distributed Computing, Neuroscience, Optimization

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FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets

Authors: Czajka, Jeffrey J Kim, Joonhoon Tang, Yinjie J et al.

Source: Bioinformatics. 41(9)

Subject Terms: 31 Biological Sciences (for-2020), 3106 Industrial Biotechnology (for-2020), Genetics (rcdc), Biotechnology (rcdc), Bioengineering (rcdc), Escherichia coli (mesh), Metabolic Engineering (mesh), Software (mesh), Synthetic Biology (mesh), Pseudomonas putida (mesh), Machine Learning (mesh), Genome, Bacterial (mesh), Algorithms (mesh), Metabolic Networks and Pathways (mesh), Pseudomonas putida (mesh), Escherichia coli (mesh), Genome, Bacterial (mesh), Algorithms (mesh), Software (mesh), Metabolic Networks and Pathways (mesh), Synthetic Biology (mesh), Metabolic Engineering (mesh), Machine Learning (mesh), Escherichia coli (mesh), Metabolic Engineering (mesh), Software (mesh), Synthetic Biology (mesh), Pseudomonas putida (mesh), Machine Learning (mesh), Genome, Bacterial (mesh), Algorithms (mesh), Metabolic Networks and Pathways (mesh), 01 Mathematical Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 46 Information and computing sciences (for-2020), 49 Mathematical sciences (for-2020)

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DNABERT-S: pioneering species differentiation with species-aware DNA embeddings

Authors: Zhou, Zhihan Wu, Weimin Ho, Harrison et al.

Source: Bioinformatics. 41(Supplement_1)

Subject Terms: 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), 3105 Genetics (for-2020), 46 Information and Computing Sciences (for-2020), 4611 Machine Learning (for-2020), Human Genome (rcdc), Genetics (rcdc), Generic health relevance (hrcs-hc), 3 Good Health and Well Being (sdg), Sequence Analysis, DNA (mesh), DNA (mesh), Genomics (mesh), Algorithms (mesh), Species Specificity (mesh), Genome (mesh), 46 Information and Computing Sciences (for-2020), 31 Biological Sciences (for-2020), 3105 Genetics (for-2020), 4611 Machine Learning (for-2020), Human Genome (rcdc), Genetics (rcdc), Generic health relevance (hrcs-hc), 3 Good Health and Well Being (sdg), DNA (mesh), Sequence Analysis, DNA (mesh), Genomics (mesh), Species Specificity (mesh), Genome (mesh), Algorithms (mesh), Sequence Analysis, DNA (mesh), DNA (mesh), Genomics (mesh), Algorithms (mesh), Species Specificity (mesh), Genome (mesh), 01 Mathematical Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 46 Information and computing sciences (for-2020), 49 Mathematical sciences (for-2020)

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SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

Authors: Ludwig, David W Guptil, Christopher Alexander, Nicholas R et al.

Source: Bioinformatics. 41(7)

Subject Terms: 46 Information and Computing Sciences (for-2020), 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), Bioengineering (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Human Genome (rcdc), Genetics (rcdc), Machine Learning and Artificial Intelligence (rcdc), 3 Good Health and Well Being (sdg), Deep Learning (mesh), High-Throughput Nucleotide Sequencing (mesh), Sequence Analysis, DNA (mesh), Microbiota (mesh), Software (mesh), Computational Biology (mesh), Algorithms (mesh), Sequence Analysis, DNA (mesh), Computational Biology (mesh), Algorithms (mesh), Software (mesh), High-Throughput Nucleotide Sequencing (mesh), Microbiota (mesh), Deep Learning (mesh), Deep Learning (mesh), High-Throughput Nucleotide Sequencing (mesh), Sequence Analysis, DNA (mesh), Microbiota (mesh), Software (mesh), Computational Biology (mesh), Algorithms (mesh), 01 Mathematical Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 46 Information and computing sciences (for-2020), 49 Mathematical sciences (for-2020)

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Application of High-Throughput Computing in Bioinformatics

Authors: Swindells, Mark Rae, Mark Pearce, Martyn et al.

Source: Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 2002 Jun . 360(1795), 1179-1189.

Access URL: https://www.jstor.org/stable/3066432

Replacing non-biomedical concepts improves embedding of biomedical concepts

Authors: Niyonkuru, Enock Gomez, Mauricio Soto Casarighi, Elena et al.

Source: PLOS ONE. 20(5)

Subject Terms: Information and Computing Sciences, Biological Sciences, Bioinformatics and Computational Biology, Algorithms, Machine Learning, Semantics, Medical Informatics Computing, General Science & Technology

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Functional protein mining with conformal guarantees

Authors: Boger, Ron S Chithrananda, Seyone Angelopoulos, Anastasios N et al.

Source: Nature Communications. 16(1)

Subject Terms: 46 Information and Computing Sciences (for-2020), 3101 Biochemistry and Cell Biology (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), 31 Biological Sciences (for-2020), Proteins (mesh), Algorithms (mesh), Computational Biology (mesh), Protein Conformation (mesh), Data Mining (mesh), Databases, Protein (mesh), Reproducibility of Results (mesh), Proteins (mesh), Reproducibility of Results (mesh), Computational Biology (mesh), Protein Conformation (mesh), Algorithms (mesh), Databases, Protein (mesh), Data Mining (mesh), Proteins (mesh), Algorithms (mesh), Computational Biology (mesh), Protein Conformation (mesh), Data Mining (mesh), Databases, Protein (mesh), Reproducibility of Results (mesh)

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A Fast and Numerically Robust Method for Exact Multinomial Goodness-of-Fit Test

Authors: Keich, Uri Nagarajan, Niranjan

Source: Journal of Computational and Graphical Statistics, 2006 Dec 01. 15(4), 779-802.

Access URL: https://www.jstor.org/stable/27594213

Bipartite tight spectral clustering (BiTSC) algorithm for identifying conserved gene co-clusters in two species

Authors: Sun, Yidan Eden Zhou, Heather J Li, Jingyi Jessica

Source: Bioinformatics. 37(9)

Subject Terms: 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), Networking and Information Technology R&D (NITRD) (rcdc), Genetics (rcdc), Bioengineering (rcdc), Generic health relevance (hrcs-hc), Algorithms (mesh), Animals (mesh), Cluster Analysis (mesh), Drosophila melanogaster (mesh), Gene Expression (mesh), Gene Expression Profiling (mesh), Animals (mesh), Drosophila melanogaster (mesh), Cluster Analysis (mesh), Gene Expression Profiling (mesh), Gene Expression (mesh), Algorithms (mesh), Algorithms (mesh), Animals (mesh), Cluster Analysis (mesh), Drosophila melanogaster (mesh), Gene Expression (mesh), Gene Expression Profiling (mesh), 01 Mathematical Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 46 Information and computing sciences (for-2020), 49 Mathematical sciences (for-2020)

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Poisson hurdle model-based method for clustering microbiome features

Authors: Qiao, Zhili Barnes, Elle Tringe, Susannah et al.

Source: Bioinformatics. 39(1)

Subject Terms: 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), 46 Information and Computing Sciences (for-2020), 4905 Statistics (for-2020), 49 Mathematical Sciences (for-2020), Microbiome (rcdc), Bioengineering (rcdc), Algorithms (mesh), Computer Simulation (mesh), Microbiota (mesh), Cluster Analysis (mesh), High-Throughput Nucleotide Sequencing (mesh), Software (mesh), Cluster Analysis (mesh), Algorithms (mesh), Computer Simulation (mesh), Software (mesh), High-Throughput Nucleotide Sequencing (mesh), Microbiota (mesh), Algorithms (mesh), Computer Simulation (mesh), Microbiota (mesh), Cluster Analysis (mesh), High-Throughput Nucleotide Sequencing (mesh), Software (mesh), 01 Mathematical Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 46 Information and computing sciences (for-2020), 49 Mathematical sciences (for-2020)

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Maast: genotyping thousands of microbial strains efficiently

Authors: Shi, Zhou Jason Nayfach, Stephen Pollard, Katherine S

Source: Genome Biology. 24(1)

Subject Terms: 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), 3105 Genetics (for-2020), Infectious Diseases (rcdc), Genetics (rcdc), Human Genome (rcdc), Emerging Infectious Diseases (rcdc), Generic health relevance (hrcs-hc), Humans (mesh), Genotype (mesh), SARS-CoV-2 (mesh), COVID-19 (mesh), Genome (mesh), Algorithms (mesh), Polymorphism, Single Nucleotide (mesh), Genotyping Techniques (mesh), Humans (mesh), Genotype (mesh), Polymorphism, Single Nucleotide (mesh), Genome (mesh), Algorithms (mesh), Genotyping Techniques (mesh), COVID-19 (mesh), SARS-CoV-2 (mesh), Humans (mesh), Genotype (mesh), SARS-CoV-2 (mesh), COVID-19 (mesh), Genome (mesh), Algorithms (mesh), Polymorphism, Single Nucleotide (mesh), Genotyping Techniques (mesh), 05 Environmental Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Bioinformatics (science-metrix)

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MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets

Authors: Wu, Yu-Wei Simmons, Blake A Singer, Steven W

Source: Bioinformatics. 32(4)

Subject Terms: 3107 Microbiology (for-2020), 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), Genetics (rcdc), Human Genome (rcdc), Infection (hrcs-hc), Algorithms (mesh), Genome, Bacterial (mesh), Genome, Microbial (mesh), Metagenome (mesh), Metagenomics (mesh), Software (mesh), Genome, Bacterial (mesh), Algorithms (mesh), Software (mesh), Metagenome (mesh), Metagenomics (mesh), Genome, Microbial (mesh), Algorithms (mesh), Genome, Bacterial (mesh), Genome, Microbial (mesh), Metagenome (mesh), Metagenomics (mesh), Software (mesh), 01 Mathematical Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 46 Information and computing sciences (for-2020), 49 Mathematical sciences (for-2020)

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HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

Authors: Azad, Ariful Pavlopoulos, Georgios A Ouzounis, Christos A et al.

Source: Nucleic Acids Research. 46(6)

Subject Terms: 3101 Biochemistry and Cell Biology (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), 31 Biological Sciences (for-2020), Bioengineering (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Genetics (rcdc), Generic health relevance (hrcs-hc), Algorithms (mesh), Cluster Analysis (mesh), Computational Biology (mesh), Gene Expression (mesh), Gene Regulatory Networks (mesh), Markov Chains (mesh), Protein Interaction Maps (mesh), Cluster Analysis (mesh), Markov Chains (mesh), Computational Biology (mesh), Gene Expression (mesh), Algorithms (mesh), Gene Regulatory Networks (mesh), Protein Interaction Maps (mesh), Algorithms (mesh), Cluster Analysis (mesh), Computational Biology (mesh), Gene Expression (mesh), Gene Regulatory Networks (mesh), Markov Chains (mesh), Protein Interaction Maps (mesh), 05 Environmental Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Developmental Biology (science-metrix), 31 Biological sciences (for-2020), 34 Chemical sciences (for-2020), 41 Environmental sciences (for-2020)

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MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies

Authors: Kang, Dongwan Li, Feng Kirton, Edward S et al.

Subject Terms: 46 Information and Computing Sciences (for-2020), 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), Networking and Information Technology R&D (NITRD) (rcdc), Bioengineering (rcdc)

Access URL: https://escholarship.org/uc/item/08z137v5

MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies

Authors: Kang, Dongwan D Li, Feng Kirton, Edward et al.

Source: PeerJ. 7(7)

Subject Terms: Information and Computing Sciences, Biological Sciences, Bioinformatics and Computational Biology, Bioengineering, Networking and Information Technology R&D (NITRD), Metagenomics, Metagenome binning, Clustering, Medical and Health Sciences

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EcoPLOT: dynamic analysis of biogeochemical data

Authors: Sanchez, Christopher D Brown, J Benjamin Gal-Oz, Omree et al.

Source: Bioinformatics. 38(5)

Subject Terms: 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), Machine Learning and Artificial Intelligence (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), 14 Life Below Water (sdg), Software (mesh), Algorithms (mesh), Language (mesh), Data Interpretation, Statistical (mesh), Microbiota (mesh), Data Interpretation, Statistical (mesh), Language (mesh), Algorithms (mesh), Software (mesh), Microbiota (mesh), Software (mesh), Algorithms (mesh), Language (mesh), Data Interpretation, Statistical (mesh), Microbiota (mesh), 01 Mathematical Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 46 Information and computing sciences (for-2020), 49 Mathematical sciences (for-2020)

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SCONCE: a method for profiling copy number alterations in cancer evolution using single-cell whole genome sequencing

Authors: Hui, Sandra Nielsen, Rasmus

Source: Bioinformatics. 38(7)

Subject Terms: 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), 3105 Genetics (for-2020), Cancer Genomics (rcdc), Human Genome (rcdc), Cancer (rcdc), Genetics (rcdc), Bioengineering (rcdc), Biotechnology (rcdc), Cancer (hrcs-hc), Humans (mesh), DNA Copy Number Variations (mesh), Whole Genome Sequencing (mesh), Neoplasms (mesh), Algorithms (mesh), Genome (mesh), High-Throughput Nucleotide Sequencing (mesh), Software (mesh), Humans (mesh), Neoplasms (mesh), Genome (mesh), Algorithms (mesh), Software (mesh), DNA Copy Number Variations (mesh), High-Throughput Nucleotide Sequencing (mesh), Whole Genome Sequencing (mesh), Humans (mesh), DNA Copy Number Variations (mesh), Whole Genome Sequencing (mesh), Neoplasms (mesh), Algorithms (mesh), Genome (mesh), High-Throughput Nucleotide Sequencing (mesh), Software (mesh), 01 Mathematical Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 46 Information and computing sciences (for-2020), 49 Mathematical sciences (for-2020)

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AncestralClust: clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees

Authors: Pipes, Lenore Nielsen, Rasmus

Source: Bioinformatics. 38(3)

Subject Terms: 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), 3105 Genetics (for-2020), Networking and Information Technology R&D (NITRD) (rcdc), Bioengineering (rcdc), Software (mesh), Phylogeny (mesh), Base Sequence (mesh), Algorithms (mesh), Cluster Analysis (mesh), Cluster Analysis (mesh), Phylogeny (mesh), Base Sequence (mesh), Algorithms (mesh), Software (mesh), Software (mesh), Phylogeny (mesh), Base Sequence (mesh), Algorithms (mesh), Cluster Analysis (mesh), 01 Mathematical Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 46 Information and computing sciences (for-2020), 49 Mathematical sciences (for-2020)

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scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

Authors: Song, Dongyuan Li, Kexin Hemminger, Zachary et al.

Source: Bioinformatics. 37(Supplement_1)

Subject Terms: 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), Genetics (rcdc), Biotechnology (rcdc), Human Genome (rcdc), 1.4 Methodologies and measurements (hrcs-rac), 3 Good Health and Well Being (sdg), Algorithms (mesh), Gene Expression Profiling (mesh), Sequence Analysis, RNA (mesh), Single-Cell Analysis (mesh), Software (mesh), Gene Expression Profiling (mesh), Sequence Analysis, RNA (mesh), Algorithms (mesh), Software (mesh), Single-Cell Analysis (mesh), Algorithms (mesh), Gene Expression Profiling (mesh), Sequence Analysis, RNA (mesh), Single-Cell Analysis (mesh), Software (mesh), 01 Mathematical Sciences (for), 06 Biological Sciences (for), 08 Information and Computing Sciences (for), Bioinformatics (science-metrix), 31 Biological sciences (for-2020), 46 Information and computing sciences (for-2020), 49 Mathematical sciences (for-2020)

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