Výsledky vyhľadávania - jana AND python (source OR resource) code*

Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data.

Autori: Idkowiak, Jakub Dehairs, Jonas Schwarzerová, Jana a ďalší

Zdroj: Nature Communications; 9/30/2025, Vol. 16 Issue 1, p1-19, 19p

Predmety: LIPIDOMICS, METABOLOMICS, MASS spectrometry, PYTHON programming language, MATHEMATICAL statistics, VISUALIZATION, DATA analysis, PROGRAMMING languages

Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems.

Autori: Sun, Zhensu Du, Xiaoning Song, Fu a ďalší

Zdroj: ACM Transactions on Software Engineering & Methodology; Jan2025, Vol. 34 Issue 1, p1-22, 22p

Predmety: LANGUAGE models, NEURAL codes, POWER resources, DEEP learning, SCIENTIFIC community

deepFPlearn⁺: enhancing toxicity prediction across the chemical universe using graph neural networks.

Autori: Soulios, Kyriakos Scheibe, Patrick Bernt, Matthias a ďalší

Zdroj: Bioinformatics; Dec2023, Vol. 39 Issue 12, p1-4, 4p

Predmety: CHEMICAL testing, MOLECULAR structure, SOFTWARE compatibility, MOLECULAR weights, SOURCE code

pyCapsid: identifying dominant dynamics and quasi-rigid mechanical units in protein shells.

Autori: Brown, Colin Agarwal, Anuradha Luque, Antoni

Zdroj: Bioinformatics; Jan2024, Vol. 40 Issue 1, p1-5, 5p

Predmety: SOURCE code, PROTEINS, CAPSIDS, PYTHONS

Korporácia: MASSACHUSETTS Institute of Technology

RareLink: scalable REDCap-based framework for rare disease interoperability linking international registries to FHIR and Phenopackets.

Autori: Graefe, Adam S. L. Rehburg, Filip Alkarkoukly, Samer a ďalší

Zdroj: NPJ Genomic Medicine; 11/18/2025, Vol. 10 Issue 1, p1-11, 11p

An empirical study on the effectiveness of large language models for binary code understanding.

Autori: Shang, Xiuwei Fu, Zhenkan Cheng, Shaoyin a ďalší

Zdroj: Empirical Software Engineering; Feb2026, Vol. 31 Issue 1, p1-38, 38p

Reproducibility of density functional approximations: How new functionals should be reported.

Autori: Lehtola, Susi Marques, Miguel A. L.

Zdroj: Journal of Chemical Physics; 9/21/2023, Vol. 159 Issue 11, p1-14, 14p

Predmety: FUNCTIONALS, DENSITY functional theory, DENSITY, NUMERICAL calculations, SOURCE code

Benchmarking WebAssembly for Embedded Systems.

Autori: MORON, KONARD WALLENTOWITZ, STEFAN

Zdroj: ACM Transactions on Architecture & Code Optimization; Sep2025, Vol. 22 Issue 3, p1-21, 21p

A comparison of large language models and model-driven reverse engineering for reverse engineering.

Autori: Siala, Hanan Lano, Kevin

Zdroj: Frontiers in Computer Science; 2025, p1-14, 14p

Open-Source Web Service with Morphological Dictionary-Supplemented Deep Learning for Morphosyntactic Analysis of Czech

Autori: Straka, Milan Straková, Jana

Témy: Computer Science - Computation and Language, text

URL: http://arxiv.org/abs/2406.12422

Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs.

Autori: Alami, Adam Jensen, Victor Ernst, Neil

Zdroj: ACM Transactions on Software Engineering & Methodology; Nov2025, Vol. 34 Issue 8, p1-44, 44p

Predmety: COMPUTER software development, LANGUAGE models, ORGANIZATIONAL accountability, INCENTIVE (Psychology), GOVERNMENT accountability, ARTIFICIAL intelligence, PROFESSIONAL peer review, SOCIAL systems

Integrating ytopt and libEnsemble to autotune OpenMC.

Autori: Wu, Xingfu Tramm, John R Larson, Jeffrey a ďalší

Zdroj: International Journal of High Performance Computing Applications; Jan2025, Vol. 39 Issue 1, p79-103, 25p

Predmety: INTEGRATED software, ENERGY consumption, MACHINE learning, COMPUTER software, LEADERSHIP

Guide assignment in single-cell CRISPR screens using crispat.

Autori: Braunger, Jana M Velten, Britta

Zdroj: Bioinformatics; Sep2024, Vol. 40 Issue 9, p1-6, 6p

Predmety: GENETIC engineering, FUNCTIONAL genomics, CRISPRS, SOURCE code, MEDICAL screening

Massive Scale Data Analytics at LCLS-II.

Autori: Thayer, Jana Chen, Zhantao Claus, Richard a ďalší

Zdroj: EPJ Web of Conferences; 5/6/2024, Vol. 295, p1-12, 12p

Predmety: LIGHT sources, DATA analysis, ACQUISITION of data, DATA reduction, DATA visualization

Highway traffic monitoring independent to shadows in OpenCV.

Autori: Hasal, Martin Nowakova, Jana Tomaszek, Lukas a ďalší

Zdroj: AIP Conference Proceedings; 2023, Vol. 2849 Issue 1, p1-4, 4p

Predmety: TRAFFIC monitoring, SUNSHINE, PYTHON programming language

Detecting Misuse of Security APIs: A Systematic Review.

Autori: Mousavi, Zahra Islam, Chadni Babar, Muhammad Ali a ďalší

Zdroj: ACM Computing Surveys; Dec2025, Vol. 57 Issue 12, p1-39, 39p

Opinion Pieces of the BTW 2025 Workshop On Advances in Cloud Data Management.

Alternate Title: Meinungsbeiträge des BTW-Workshops zu Entwicklungen im Cloud-Datenmanagement. (German)

Autori: Bodner, Thomas Böhm, Alexander Böther, Maximilian a ďalší

Zdroj: Datenbank-Spektrum; Nov2025, Vol. 25 Issue 3, p187-195, 9p

VexIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similarity.

Autori: VenkataKeerthy, S. Banerjee, Soumya Dey, Sayan a ďalší

Zdroj: ACM Transactions on Software Engineering & Methodology; Nov2025, Vol. 34 Issue 8, p1-54, 54p

Predmety: MALWARE, FLOWGRAPHS, FEATURE extraction, SOFTWARE frameworks

decoupleR: ensemble of computational methods to infer biological activities from omics data.

Autori: Badia-i-Mompel, Pau Santiago, Jesús Vélez Braunger, Jana a ďalší

Zdroj: Bioinformatics Advances; 2022, Vol. 2 Issue 1, p1-3, 3p

Predmety: PYTHON programming language, INFORMATION retrieval, PERTURBATION theory, BIOINFORMATICS

Reviews & Products: BIOCONDUCTOR (Computer software)

Creating accessible browser-based interactive content for teaching physiology and pharmacology: options for nonprogrammers.

Autori: Samways, Damien S. K. Yousef, Lara Sheffield, Scott A.

Zdroj: Advances in Physiology Education; Dec2025, Vol. 49 Issue 4, p1-7, 7p

Predmety: PHYSIOLOGY education, JAVASCRIPT programming language, PHARMACOLOGISTS, GENERATIVE artificial intelligence, EDUCATIONAL resources, COLLEGE teachers, EXPERIENTIAL learning