Suchergebnisse - jana AND python source code~

Marginalizing Sample Consensus.

Autoren: Barath, Daniel Noskova, Jana Matas, Jiri

Quelle: IEEE Transactions on Pattern Analysis & Machine Intelligence. Nov2022, Vol. 44 Issue 11, p8420-8432. 13p.

Schlagwörter: *C++, *DECISION making, *ALGORITHMS, SOURCE code, PYTHON programming language

Guide assignment in single-cell CRISPR screens using crispat.

Autoren: Braunger, Jana M Velten, Britta

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

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

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

Autoren: Soulios, Kyriakos Scheibe, Patrick Bernt, Matthias et al.

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

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

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

Autoren: Brown, Colin Agarwal, Anuradha Luque, Antoni

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

Schlagwörter: SOURCE code, PROTEINS, CAPSIDS, PYTHONS

Firma/Körperschaft: MASSACHUSETTS Institute of Technology

Source code and example data for the paper: 'Magmatism Controls Global Oceanic Transform Fault Topography'

Autoren: Tian, Xiaochuan Behn, Mark Ito, Garrett et al.

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

Autoren: Straka, Milan Straková, Jana

Index Begriffe: Computer Science - Computation and Language, text

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

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

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

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

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

A Review of Deep Learning-Based Binary Code Similarity Analysis.

Autoren: Du, Jiang Wei, Qiang Wang, Yisen et al.

Quelle: Electronics (2079-9292); Nov2023, Vol. 12 Issue 22, p4671, 18p

Schlagwörter: BINARY codes, DEEP learning, SOFTWARE engineering, ARTIFICIAL intelligence, COMPUTER network security, SOURCE code

SBMLDiagrams: a python package to process and visualize SBML layout and render.

Autoren: Xu, Jin Jiang, Jessie Sauro, Herbert M

Quelle: Bioinformatics; Jan2023, Vol. 39 Issue 1, p1-2, 2p

Schlagwörter: PYTHON programming language, BIOCHEMICAL models, PDF (Computer file format), SYSTEMS biology, SOURCE code

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

Autoren: Milan Straka Jana Straková

Quelle: Lecture Notes in Computer Science ISBN: 9783031705625

Schlagwörter: FOS: Computer and information sciences, Computer Science - Computation and Language, 15. Life on land, Computation and Language (cs.CL), 3. Good health

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

Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools.

Autoren: Modesti, Paolo Golightly, Lewis Holmes, Louis et al.

Quelle: Journal of Cybersecurity & Privacy; Sep2024, Vol. 4 Issue 3, p410-448, 39p

Schlagwörter: PENETRATION testing (Computer security), SOURCE code, COMPUTER software, CLASSIFICATION, SURVEYS

Unleashing the power of pseudo-code for binary code similarity analysis.

Autoren: Zhang, Weiwei Xu, Zhengzi Xiao, Yang et al.

Quelle: Cybersecurity (2523-3246); 12/1/2022, Vol. 5 Issue 1, p1-18, 18p

Schlagwörter: BINARY codes, CONVOLUTIONAL neural networks, FLOWGRAPHS, SIMILARITY (Psychology), SOURCE code

OCR Using Python and Its Application.

Autoren: Mukherjee, Sumita Tyagi, Hritik Tyagi, Purushautam et al.

Quelle: Journal of Advanced Zoology. 2023 Supplement, Vol. 44, p1083-1092. 10p.

Integrating diverse data sources to predict disease risk in dairy cattle—a machine learning approach.

Autoren: Lasser, Jana Matzhold, Caspar Egger-Danner, Christa et al.

Quelle: Journal of Animal Science. Nov2021, Vol. 99 Issue 11, p1-14. 14p.

Schlagworte: Dairy cattle, Machine learning, Sensitivity & specificity (Statistics), Animal herds, Sports nutrition, Livestock farms, Mastitis, Animal breeding

Geografische Kategorien: Austria

DSCA-PSPNet: Dynamic spatial-channel attention pyramid scene parsing network for sugarcane field segmentation in satellite imagery.

Autoren: Yujian Yuan Lina Yang Kan Chang et al.

Quelle: Frontiers in Plant Science; 2024, p1-16, 16p

Schlagwörter: REMOTE-sensing images, SUGARCANE, PYRAMIDS, DEEP learning, PRECISION farming, SUSTAINABLE development, SOURCE code

Geografische Kategorien: GUANGXI Zhuangzu Zizhiqu (China)

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

Autoren: Badia-i-Mompel, Pau Santiago, Jesús Vélez Braunger, Jana et al.

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

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

Reviews & Products: BIOCONDUCTOR (Computer software)

Indoor Airborne VOCs from Water-Based Coatings: Transfer Dynamics and Health Implications †.

Autoren: Růžičková, Jana Raclavská, Helena Kucbel, Marek et al.

Quelle: Journal of Xenobiotics; Dec2025, Vol. 15 Issue 6, p197, 29p

Schlagwörter: VOLATILE organic compounds, INDOOR air quality, SICK building syndrome, HEALTH outcome assessment, HEALTH risk assessment, AQUEOUS polymeric coatings, HAZARDS

Evidence-Based Recommendations for Geriatric Trauma Care: Systematic Review and AI-Assisted Consolidation of Clinical Practice Guidelines Between 2016 and 2021.

Autoren: Kocar, Thomas D. Fotteler, Marina L. Willems, Jana et al.

Quelle: Annals of Surgery Open; Dec2025, Vol. 6 Issue 4, p1-9, 9p

iACP-GE: accurate identification of anticancer peptides by using gradient boosting decision tree and extra tree.

Autoren: Liang, Y. Ma, X.

Quelle: SAR & QSAR in Environmental Research; Jan2023, Vol. 34 Issue 1, p1-19, 19p

Schlagwörter: DECISION trees, PEPTIDES, AMINO acids, SOURCE code, IDENTIFICATION

1-Azinyl-1′-Alkenylferrocenes with Anticholinesterase, Antioxidant, and Antiaggregating Activities as Multifunctional Agents for Potential Treatment of Alzheimer's Disease.

Autoren: Makhaeva, Galina F. Utepova, Irina A. Rudakova, Elena V. et al.

Quelle: Pharmaceuticals (14248247); Dec2025, Vol. 18 Issue 12, p1862, 42p

Schlagwörter: ALZHEIMER'S disease, CHOLINESTERASE inhibitors, ANTIOXIDANTS, BIPYRIDINE derivatives, QUINOLINE derivatives