Suchergebnisse - computing algorithms and ((bioinformatics OR bioinformatika) OR informatics)

Porting of an Atmospheric Chemistry Solver to Parallel CPU-GPU Execution

Autoren: Guzman Ruiz, Christian

Thesis Advisors: Acosta, Mario C. (Mario César), César Galobardes, Eduardo

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Informàtica d'alt rendiment, High-Performance Computing, Computación de alto rendimient, GPU, Models climàtics químics, Chemistry Climate models, Modelos climáticos químicos, Tecnologies

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/693090

Massive cosmological data generation and distribution

Autoren: Tallada Crespi, Pau

Thesis Advisors: Tonello , Nadia, Carretero Palacios, Jorge, César Galobardes, Eduardo

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Gestió de dades, Data management, Gestión de datos, Distribució de dades, Data distribution, Distribución de datos, Optimització d'algorismes, Algorithm optimization, Optimización de algoritmos, Tecnologies

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/691707

Post-quantum cryptography acceleration for next generation computers

Autoren: Kostalampros, Ioannis-Vatistas

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors

Thesis Advisors: Moretó Planas, Miquel, Hernández Luz, Carles

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Àrees temàtiques de la UPC::Informàtica, 004 - Informàtica

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/695219
https://dx.doi.org/10.5821/dissertation-2117-441612

NEMO: computational challenges in ocean simulation

Autoren: Tintó Prims, Oriol

Weitere Verfasser: University/Department: Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius

Thesis Advisors: oriol.tinto@bsc.es, Cortés Fité, Ana, Acosta, Mario C. (Mario César), Doblas-Reyes, Francisco J.

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Model oceànic, Modelo oceánico, Oceanic model, Computació d'altes prestacions, Computación de altas prestaciones, High performance computing, Paral·lelisme, Paralelismo, Parallelism, Tecnologies

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/669877

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

Particle-in-cell algorithms for plasma simulations on heterogeneous architectures

Autoren: Sáez Pous, Xavier

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors

Thesis Advisors: Cela Espín, José M. (José María), Soba Pascual, Alejandro

Quelle: TDX (Tesis Doctorals en Xarxa)

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/381258
https://dx.doi.org/10.5821/dissertation-2117-96109

A hierarchical parallel implementation model for algebra-based CFD simulations on hybrid supercomputers

Autoren: Álvarez Farré, Xavier

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament de Màquines i Motors Tèrmics

Thesis Advisors: Oliva Llena, Asensio, Gorobets, Andrei

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Àrees temàtiques de la UPC::Física

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/687793
https://dx.doi.org/10.5821/dissertation-2117-384828

Quantum computing algorithms: getting closer to critical problems in computational biology.

Autoren: Marchetti, Laura Nifosì, Riccardo Martelli, Pier Luigi et al.

Quelle: Briefings in Bioinformatics. Nov2022, Vol. 23 Issue 6, p1-15. 15p.

Schlagwörter: *COMPUTATIONAL biology, *QUANTUM computing, *PROTEIN structure prediction, *METABOLOMICS, *BIOCOMPLEXITY, *ALGORITHMS

Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)

Autoren: Badrul Hisham Ahmad

Resource Type: eBook.

Schlagworte: Computational intelligence--Congresses, Soft computing--Congresses, Electronic data processing--Congresses, Data mining--Statistical methods--Congresses, Computer algorithms--Congresses

Categories: COMPUTERS / General, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Database Administration & Management, COMPUTERS / Security / General

Zero-touch service orchestration in 5G and beyond mobile networks

Autoren: Dalgkitsis, Anestis

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions

Thesis Advisors: Verikoukis, Christos, Ramantas, Konstantinos, Alonso Zárate, Luis Gonzaga

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Zero-touch Management & Orchestration (ZSM), Machine Learning (ML), Reinforcement Learning (RL), Network Function Virtualization (NFV), Network Slicing (NS), Software Defined Networking (SDN), Ultra-Reliable Low Latency Communications (URLLC), Gestió i orquestració de zero toc (ZSM), Aprenentatge automàtic (ML), Aprenentatge de reforç (RL), Virtualització de funcions de xarxa (NFV), Xarxa definida per programari (SDN), Baixa latència ultra fiable Comunicacions (URLLC), Gestión y orquestación sin contacto (ZSM), Aprendizaje automático (ML), Aprendizaje por Refuerzo (RL), Virtualización de funciones de red (NFV), Redes definidas por software (SDN), Baja latencia ultraconfiable Comunicaciones (URLLC), Àrees temàtiques de la UPC::Enginyeria de la telecomunicació

Time: 621.3

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/690299
https://dx.doi.org/10.5821/dissertation-2117-404663

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

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

Leveraging gene correlations in single cell transcriptomic data

Autoren: Silkwood, Kai Dollinger, Emmanuel Gervin, Joshua et al.

Quelle: BMC Bioinformatics. 25(1)

Schlagwörter: 31 Biological Sciences (for-2020), 3102 Bioinformatics and Computational Biology (for-2020), Human Genome (rcdc), Genetics (rcdc), Single-Cell Analysis (mesh), Humans (mesh), Sequence Analysis, RNA (mesh), Transcriptome (mesh), Algorithms (mesh), Gene Expression Profiling (mesh), Gene Regulatory Networks (mesh), Single cell RNA sequencing, Gene-gene correlation, Gene regulatory network, Gene co-expression network, Melanoma, Humans (mesh), Gene Expression Profiling (mesh), Sequence Analysis, RNA (mesh), Algorithms (mesh), Gene Regulatory Networks (mesh), Single-Cell Analysis (mesh), Transcriptome (mesh), Gene co-expression network, Gene regulatory network, Gene–gene correlation, Melanoma, Single cell RNA sequencing, Single-Cell Analysis (mesh), Humans (mesh), Sequence Analysis, RNA (mesh), Transcriptome (mesh), Algorithms (mesh), Gene Expression Profiling (mesh), Gene Regulatory Networks (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/15k712w5

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

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

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

Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images

Autoren: Wu, Allison Chia-Yi Rifkin, Scott A

Quelle: BMC Bioinformatics. 16(1)

Schlagwörter: 46 Information and Computing Sciences (for-2020), 31 Biological Sciences (for-2020), 4603 Computer Vision and Multimedia Computation (for-2020), Networking and Information Technology R&D (NITRD) (rcdc), Bioengineering (rcdc), Algorithms (mesh), Animals (mesh), Artificial Intelligence (mesh), Caenorhabditis elegans (mesh), Computational Biology (mesh), Embryo, Nonmammalian (mesh), Fluorescence (mesh), Fluorescent Dyes (mesh), Image Processing, Computer-Assisted (mesh), In Situ Hybridization, Fluorescence (mesh), Microscopy, Fluorescence (mesh), Nanotechnology (mesh), RNA, Messenger (mesh), Software (mesh), Staining and Labeling (mesh), Single molecule imaging, smFISH, Random forest, Image informatics, RNA, Fluorescence microscopy, Machine learning, Image quality, Embryo, Nonmammalian (mesh), Animals (mesh), Caenorhabditis elegans (mesh), RNA, Messenger (mesh), Fluorescent Dyes (mesh), Microscopy, Fluorescence (mesh), Staining and Labeling (mesh), In Situ Hybridization, Fluorescence (mesh), Computational Biology (mesh), Algorithms (mesh), Nanotechnology (mesh), Fluorescence (mesh), Artificial Intelligence (mesh), Image Processing, Computer-Assisted (mesh), Software (mesh), Algorithms (mesh), Animals (mesh), Artificial Intelligence (mesh), Caenorhabditis elegans (mesh), Computational Biology (mesh), Embryo, Nonmammalian (mesh), Fluorescence (mesh), Fluorescent Dyes (mesh), Image Processing, Computer-Assisted (mesh), In Situ Hybridization, Fluorescence (mesh), Microscopy, Fluorescence (mesh), Nanotechnology (mesh), RNA, Messenger (mesh), Software (mesh), Staining and Labeling (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/21v4j6jw
https://escholarship.org/content/qt21v4j6jw/qt21v4j6jw.pdf

Kingdom-wide CRISPR guide design with ALLEGRO

Autoren: Mohseni, Amirsadra Nia, Reyhane Ghorbani Tafrishi, Aida et al.

Quelle: Nucleic Acids Research. 53(15)

Schlagwörter: Biological Sciences, Bioinformatics and Computational Biology, Biotechnology, Genetics, Human Genome, Generic health relevance, RNA, Guide, CRISPR-Cas Systems, CRISPR-Cas Systems, Genome, Fungal, Gene Editing, Algorithms, Kluyveromyces, Saccharomyces cerevisiae, Clustered Regularly Interspaced Short Palindromic Repeats, Yarrowia, Rhodotorula, Gene Library, Saccharomycetales, Environmental Sciences, Information and Computing Sciences, Developmental Biology, Biological sciences, Chemical sciences, Environmental sciences

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/9zm954ng
https://escholarship.org/content/qt9zm954ng/qt9zm954ng.pdf

TRFill: synergistic use of HiFi and Hi-C sequencing enables accurate assembly of tandem repeats for population-level analysis.

Autoren: Wen, Huaming Yang, Jinbao Zhao, Xianjia et al.

Quelle: Genome biology. 26(1)

Schlagwörter: Centromere, Telomere, Humans, Sequence Analysis, DNA, Tandem Repeat Sequences, Algorithms, Software, Solanum lycopersicum, Gap filling, Genome assembly, Reference-guided genome assembly, Segmental duplications, Tandem repeats, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/1s4297xt
https://escholarship.org/content/qt1s4297xt/qt1s4297xt.pdf