(oaftype exact result) and (resulttypeid exact software) en_US 10 1 732064

RECOLECTA___::ae5e96d7661fbbe19f4d31c52b214c05 2026-04-25T03:16:14.937481081 2026-04-08T21:17:03.135958Z

50|RECOLECTA___::ae5e96d7661fbbe19f4d31c52b214c05 oai:dnet:burjcdigital::33096168e32fc69a3df4428895ccfc3c 10115/114399 Chushig Muzo, Cristian David Buciulea Vlas, Andrei 2025-01-01 Software en python desarrollado y empleado en las prácticas de la asignatura Fundamentos Matemáticos de la Información, organizados secuencialmente desde la Práctica 1 hasta la Práctica 4. Todas las prácticas se desarrollan principalmente en el entorno Jupyter Notebook, utilizando el lenguaje de programación Python y sus diversas librerías específicas. SWHID: swh:1:dir:c97708b2aa947ffd0c5c0f005e9d92c24c267afb Repositorio: https://github.com/cdchushig/programa-ordenador-fundamentos-matematicos-urjc información mutua procesos estocásticos codificación de fuente compresión de datos reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos instname:Universidad Rey Juan Carlos application/pdf application/octet-stream false false 0.9 null 2025-01-01 10115/114399 CC BY SA https://hdl.handle.net/10115/114399

__bioTools__::0045f699cd8fa181a9c4c830ffcd3947 2026-04-25T03:16:06.892656256 2025-02-20T09:30:22.198Z

haste_toolkit 50|__bioTools__::0045f699cd8fa181a9c4c830ffcd3947 Blamey, Ben 2021-01-25 A collection of software components that can be used to build intelligent streaming pipelines for scientific data streams. Computer science bio.tools false false 0.9 2021-01-25 https://bio.tools/haste_toolkit

__bioTools__::00e2c835d3ae2a8c80ecdbb68a721927 2026-04-25T03:16:15.241530132 2025-02-20T09:30:22.314Z

50|__bioTools__::00e2c835d3ae2a8c80ecdbb68a721927 findpromoter Team, Galaxy Support 2016-12-19 Program to search for a promoter motif and optionally, an adapter sequence from a source species on a target species. Transcription factors and regulatory sites Sequence analysis bio.tools https://galaxy.pasteur.fr/root?tool_id=toolshed.pasteur.fr%2Frepos%2Ffmareuil%2Fpromoteur_tools%2Ffindpromoter%2F1.0 false false 0.9 2016-12-19 https://bio.tools/findpromoter

__bioTools__::00e382e892d1c04c4f10e1670b12eebc 2026-04-25T03:16:16.927807334 2025-02-20T09:30:22.317Z

autosite 50|__bioTools__::00e382e892d1c04c4f10e1670b12eebc Sanner, Michel F. 2018-05-19 Computational method for identifying and characterizing small molecules binding sites for receptors of known three-dimensional structure. Binding sites are identified using energetic aspects to select high affinity points in space around the receptor, and a clustering technique for segregating these points into clusters called fills and corresponding to potential binding pockets. Protein binding sites Protein sites, features and motifs bio.tools Python http://adfr.scripps.edu/AutoDockFR/AutoSite_tutorial.pdf false false 0.9 doi_dedup___::a0062387197db31880495173b460c801 2016-06-26 PMC5048065 2020-03-02 https://doi.org/10.1093/bioinformatics/btw367 https://doi.org/10.1093/bioinformatics/btw367 <jats:title>Abstract</jats:title><jats:p>Motivation: The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen 2016-06-26 https://doi.org/10.1093/bioinformatics/btw367 https://doi.org/10.1093/bioinformatics/btw367 Pradeep Anand Ravindranath Oxford University Press (OUP) 2017-08-14 https://pubmed.ncbi.nlm.nih.gov/27354702 https://pubmed.ncbi.nlm.nih.gov/27354702 https://dx.doi.org/10.1093/bioinformatics/btw367 https://dx.doi.org/10.1093/bioinformatics/btw367 Michel F. Sanner 10.1093/bioinformatics/btw367 https://academic.oup.com/bioinformatics/article-pdf/32/20/3142/25040432/btw367.pdf https://academic.oup.com/bioinformatics/article-pdf/32/20/3142/25040432/btw367.pdf 27354702 2018-05-19 https://bio.tools/autosite

__bioTools__::00ed53a666055a791bd5935e37e933ce 2026-04-25T03:16:07.419052241 2025-02-20T09:30:22.34Z

bottled_saft 50|__bioTools__::00ed53a666055a791bd5935e37e933ce Müller, E. A. 2018-05-29 SAFT-γ Mie Force Field Parameters for Thousands of Molecular Fluids. Chemistry Small molecules Molecular modelling bio.tools Python http://www.bottledsaft.org/about false false 0.9 2018-05-29 https://bio.tools/bottled_saft

__bioTools__::00fb81b0f860441c7d1a07f4031f7337 2026-04-25T03:16:16.203565110 2025-02-20T09:30:22.359Z

muscle-fascicle-tracking-ultrasound 50|__bioTools__::00fb81b0f860441c7d1a07f4031f7337 Lichtwark, Glen 2021-05-05 Implementation of an optical flow algorithm to track muscle length changes imaged with ultrasound. Biomedical science Echography Biology bio.tools MATLAB false false 0.9 2021-05-05 https://bio.tools/muscle-fascicle-tracking-ultrasound

__bioTools__::019813a398da665764643d79868e4bd3 2026-04-25T03:16:07.430067986 2025-02-20T09:30:22.484Z

pydockweb 50|__bioTools__::019813a398da665764643d79868e4bd3 Jiménez-Garcí­a, Brian Lumbreras, Luis Ángel Rodriguez 2016-10-11 Web server for the structural prediction of protein-protein interactions. Given the 3D coordinates of two interacting proteins, it returns the best rigid-body docking orientations generated by FTDock (Gabb HA. et al. J Mol Biol 1997;272:106-120) and evaluated by pyDock scoring function (Cheng et al. 2007 Proteins 68, 503-515), which includes electrostatics, desolvation energy and limited van der Waals contribution. Computational chemistry bio.tools https://life.bsc.es/pid/pydockweb/info/faq_and_help false false 0.9 doi_dedup___::b6807cfef2c2e17990326dd8645cf7b6 2020-03-02 https://doi.org/10.1093/bioinformatics/btt262 https://doi.org/10.1093/bioinformatics/btt262 https://dx.doi.org/10.1093/bioinformatics/btt262 https://dx.doi.org/10.1093/bioinformatics/btt262 2013-12-17 https://pubmed.ncbi.nlm.nih.gov/23661696 https://pubmed.ncbi.nlm.nih.gov/23661696 23661696 Juan Fernández-Recio Brian Jiménez-García Carles Pons https://academic.oup.com/bioinformatics/article-pdf/29/13/1698/18536412/btt262.pdf https://academic.oup.com/bioinformatics/article-pdf/29/13/1698/18536412/btt262.pdf <jats:title>Abstract</jats:title> <jats:p>Summary: pyDockWEB is a web server for the rigid-body docking prediction of protein–protein complex structures using a new version of the pyDock scoring algorithm. We use here a new custom parallel FTDock implementation, with adjusted grid size for optimal FFT calculations, and a new version of pyDock, which dramatically speeds up calculations while keeping the same predictive accuracy. Given the 3D coordinates of two interacting proteins, Oxford University Press (OUP) 2013-05-09 10.1093/bioinformatics/btt262 2013-05-09 https://doi.org/10.1093/bioinformatics/btt262 https://doi.org/10.1093/bioinformatics/btt262 doi_dedup___::b6807cfef2c2e17990326dd8645cf7b6 2020-03-02 https://doi.org/10.1093/bioinformatics/btt262 https://doi.org/10.1093/bioinformatics/btt262 https://dx.doi.org/10.1093/bioinformatics/btt262 https://dx.doi.org/10.1093/bioinformatics/btt262 2013-12-17 https://pubmed.ncbi.nlm.nih.gov/23661696 https://pubmed.ncbi.nlm.nih.gov/23661696 23661696 Juan Fernández-Recio Brian Jiménez-García Carles Pons https://academic.oup.com/bioinformatics/article-pdf/29/13/1698/18536412/btt262.pdf https://academic.oup.com/bioinformatics/article-pdf/29/13/1698/18536412/btt262.pdf <jats:title>Abstract</jats:title> <jats:p>Summary: pyDockWEB is a web server for the rigid-body docking prediction of protein–protein complex structures using a new version of the pyDock scoring algorithm. We use here a new custom parallel FTDock implementation, with adjusted grid size for optimal FFT calculations, and a new version of pyDock, which dramatically speeds up calculations while keeping the same predictive accuracy. Given the 3D coordinates of two interacting proteins, Oxford University Press (OUP) 2013-05-09 10.1093/bioinformatics/btt262 2013-05-09 https://doi.org/10.1093/bioinformatics/btt262 https://doi.org/10.1093/bioinformatics/btt262 2016-10-11 https://bio.tools/pydockweb

__bioTools__::01b68f18b3e3b3977837aa3fc2ca37fd 2026-04-25T03:16:14.732100395 2025-02-20T09:30:22.503Z

50|__bioTools__::01b68f18b3e3b3977837aa3fc2ca37fd msacquisitionsimulator Wang W MB, Major 2017-08-03 Collection of three command-line tools that simulate data-dependent acquisition algorithms on in silico generated ground truth data of liquid chromatography-mass spectrometry proteomics experiments. Proteomics Simulation experiment Proteomics experiment bio.tools C++ https://github.com/UNC-Major-Lab/MSAcquisitionSimulator/blob/master/README.md false false 0.9 2017-08-03 https://bio.tools/msacquisitionsimulator

__bioTools__::01eb68abd5b2810d956d1ca57a0bab03 2026-04-25T03:16:08.879549634 2025-02-20T09:30:22.559Z

biojs-model 50|__bioTools__::01eb68abd5b2810d956d1ca57a0bab03 BioJS 2015-01-22 Biological data models. Bioinformatics bio.tools JavaScript https://github.com/biojs/biojs-model false false 0.9 2015-01-22 https://bio.tools/biojs-model

__bioTools__::0250936b9827600453c6116d9d97544c 2026-04-25T03:16:15.261928001 2025-02-20T09:30:22.641Z

star-genome-browser 50|__bioTools__::0250936b9827600453c6116d9d97544c Wang, Wei 2018-05-21 An integrated solution to management and visualization of sequencing data. Sequencing Data management Data visualisation bio.tools PHP C++ Perl https://github.com/angell1117/STAR-genome-browser false false 0.9 2018-05-21 GPL-2.0 https://bio.tools/star-genome-browser