Suchergebnisse - (( jana AND python source code ) OR ( jana AND python resource code ))*

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

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

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)

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

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.

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

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

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)

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

Minimising by Simulation‐Based Optimisation the Cycle Time for the Line Balancing Problem in Real‐World Environments.

Autoren: Moncayo–Martínez, Luis A. He, Naihui Arias–Nava, Elias H.

Quelle: Applied Stochastic Models in Business & Industry; Jan/Feb2025, Vol. 41 Issue 1, p1-18, 18p

Schlagwörter: ASSEMBLY line balancing, STOCHASTIC programming, PREDICTION models, TURNAROUND time, INDUSTRY 4.0, MOTORCYCLE industry

sAMP‐VGG16: Force‐field assisted image‐based deep neural network prediction model for short antimicrobial peptides.

Autoren: Pandey, Poonam Srivastava, Anand

Quelle: Proteins; Jan2025, Vol. 93 Issue 1, p372-383, 12p

Do fish gut microbiotas vary across spatial scales? A case study of Diplodus vulgaris in the Mediterranean Sea.

Autoren: Ginevra, Lilli Charlotte, Sirot Hayley, Campbell et al.

Quelle: Animal Microbiome. 6/13/2024, Vol. 6 Issue 1, p1-24. 24p.

Assessing by Simulation the Effect of Process Variability in the SALB-1 Problem.

Autoren: Moncayo-Martínez, Luis A. Arias-Nava, Elias H.

Quelle: AppliedMath; Sep2023, Vol. 3 Issue 3, p563-581, 19p

Schlagwörter: ASSEMBLY line balancing, FUZZY numbers, STOCHASTIC analysis, PROGRAMMING languages, STOCHASTIC control theory, DETERMINISTIC processes

A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices.

Autoren: Feng, Ruitao Chen, Sen Xie, Xiaofei et al.

Quelle: IEEE Transactions on Information Forensics & Security; 2021, Vol. 16, p1563-1578, 16p

Abstracts.

Quelle: Anatomia, Histologia, Embryologia: Journal of Veterinary Medicine Series C. Jan2022 Supplement S1, Vol. 51, p3-64. 62p.

Schlagworte: Anatomy, Sexual cycle, Biological specimens, Medical sciences, Physiology, Islands of Langerhans, Inner ear, Blended learning

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

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

Autoren: Jakub Idkowiak Jonas Dehairs Jana Schwarzerová et al.

Quelle: Nature Communications, Vol 16, Iss 1, Pp 1-19 (2025)

Schlagwörter: Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2041-1723

Zugangs-URL: https://doaj.org/article/4e901a73a34347ffbef54049de5ac663

Individual behavior tracking of heifers by using object detection algorithm YOLOv4.

Autoren: Jahn, Sarah Schmidt, Gabriel Bachmann, Lisa et al.

Quelle: Frontiers in Animal Science. 2025, p1-11. 11p.