Suchergebnisse - computing algorithms and (((bioinformatics OR bioinformation) OR bioinformatika) OR informatika)

High Throughput Computational Studies of Macromolecular Structure Flexibility

Autoren: Hospital Gasch, Adam

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

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

Quelle: TDX (Tesis Doctorals en Xarxa)

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

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/284440

Derivation and Analysis of Distributed Computing Algorithms in Biological Systems

Autoren: Chandrasekhar, Arjun

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

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/03v7w5sf
https://escholarship.org/content/qt03v7w5sf/qt03v7w5sf.pdf

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

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

Quelle: Bioinformatics. 41(9)

Schlagwörter: 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)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/1n5748bq
https://escholarship.org/content/qt1n5748bq/qt1n5748bq.pdf

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

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

Quelle: Bioinformatics. 41(Supplement_1)

Schlagwörter: 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)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/9kf0s9g0
https://escholarship.org/content/qt9kf0s9g0/qt9kf0s9g0.pdf

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

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

Quelle: Bioinformatics. 41(7)

Schlagwörter: 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)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/7b07t0b7
https://escholarship.org/content/qt7b07t0b7/qt7b07t0b7.pdf

Application of High-Throughput Computing in Bioinformatics

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

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

Zugangs-URL: https://www.jstor.org/stable/3066432

Replacing non-biomedical concepts improves embedding of biomedical concepts

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

Quelle: PLOS ONE. 20(5)

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

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/43k9s6k4
https://escholarship.org/content/qt43k9s6k4/qt43k9s6k4.pdf

Functional protein mining with conformal guarantees

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

Quelle: Nature Communications. 16(1)

Schlagwörter: 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)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/3br9k1dr
https://escholarship.org/content/qt3br9k1dr/qt3br9k1dr.pdf

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

Autoren: Keich, Uri Nagarajan, Niranjan

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

Zugangs-URL: https://www.jstor.org/stable/27594213

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

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

Quelle: Bioinformatics. 37(9)

Schlagwörter: 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)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/0zk6839v
https://escholarship.org/content/qt0zk6839v/qt0zk6839v.pdf

Poisson hurdle model-based method for clustering microbiome features

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

Quelle: Bioinformatics. 39(1)

Schlagwörter: 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)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/23q4g5j0
https://escholarship.org/content/qt23q4g5j0/qt23q4g5j0.pdf

Maast: genotyping thousands of microbial strains efficiently

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

Quelle: Genome Biology. 24(1)

Schlagwörter: 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)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/8th8g8sc
https://escholarship.org/content/qt8th8g8sc/qt8th8g8sc.pdf

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

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

Quelle: Bioinformatics. 32(4)

Schlagwörter: 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)

Zugangs-URL: https://escholarship.org/uc/item/44717940

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

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

Quelle: Nucleic Acids Research. 46(6)

Schlagwörter: 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)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/7rm2t22k
https://escholarship.org/content/qt7rm2t22k/qt7rm2t22k.pdf

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

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

Schlagwörter: 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)

Zugangs-URL: https://escholarship.org/uc/item/08z137v5

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

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

Quelle: PeerJ. 7(7)

Schlagwörter: 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

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/0wp465kn
https://escholarship.org/content/qt0wp465kn/qt0wp465kn.pdf

EcoPLOT: dynamic analysis of biogeochemical data

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

Quelle: Bioinformatics. 38(5)

Schlagwörter: 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)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/9h33q1m0
https://escholarship.org/content/qt9h33q1m0/qt9h33q1m0.pdf

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

Autoren: Hui, Sandra Nielsen, Rasmus

Quelle: Bioinformatics. 38(7)

Schlagwörter: 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)

Zugangs-URL: https://escholarship.org/uc/item/5z19c0cp

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

Autoren: Pipes, Lenore Nielsen, Rasmus

Quelle: Bioinformatics. 38(3)

Schlagwörter: 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)

Zugangs-URL: https://escholarship.org/uc/item/0f78b3n8

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

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

Quelle: Bioinformatics. 37(Supplement_1)

Schlagwörter: 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)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/30b354nt
https://escholarship.org/content/qt30b354nt/qt30b354nt.pdf