Suchergebnisse - ((stateu OR stat) OR stateeni) python code analysis*

GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.

Autoren: Li J Xu Y Ma S et al.

Quelle: Bioinformatics (Oxford, England) [Bioinformatics] 2025 Sep 01; Vol. 41 (9).

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE

MeSH-Schlagworte: Gene-Environment Interaction* , Deep Learning* , Lung Neoplasms*/genetics , Skin Neoplasms*/genetics , Melanoma*/genetics, Datasets as Topic ; Humans ; Age Factors ; Sex Factors ; Smoking

GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries.

Autoren: Fachada, Nuno Fernandes, Daniel Fernandes, Carlos M. et al.

Quelle: Future Internet; Sep2025, Vol. 17 Issue 9, p412, 28p

Schlagwörter: LANGUAGE models, EXPERIMENTAL design, PYTHON programming language, GENERATIVE pre-trained transformers, QUANTITATIVE research, BENCHMARK problems (Computer science), CODE generators

The state of the late Quaternary megafauna extinction debate: a systematic review and analysis.

Autoren: Stewart, Mathew Peters, Carli Ziegler, Michael J. et al.

Quelle: Frontiers in Mammal Science. 2025, p1-27. 27p.

ASAS-NANP symposium: mathematical modeling in animal nutrition—Making sense of big data and machine learning: how open-source code can advance training of animal scientists.

Autoren: Brennan, Jameson R. Menendez, III, Hector M. Ehlert, Krista et al.

Quelle: Journal of Animal Science. Jan2023, Vol. 101 Issue 1, p1-11. 11p. 4 Diagrams, 3 Graphs.

A statistical approach to automated analysis of the low‐contrast object detectability test for the large ACR MRI phantom.

Autoren: Golestani, Ali M. Gee, Julia M.

Quelle: Journal of Applied Clinical Medical Physics; Jul2025, Vol. 26 Issue 7, p1-9, 9p

Schlagwörter: LINEAR statistical models, IMAGING phantoms, QUALITY control, PYTHON programming language, MAGNETIC resonance imaging

Survival Modelling Using Machine Learning and Immune–Nutritional Profiles in Advanced Gastric Cancer on Home Parenteral Nutrition.

Autoren: Matysiak, Konrad Hojdis, Aleksandra Szewczuk, Magdalena

Quelle: Nutrients. Aug2025, Vol. 17 Issue 15, p2414. 16p.

Data-Driven Early Warning Approach for Antimicrobial Resistance Prediction–Anomaly Detection Based on High-Level Indicators.

Autoren: Csorba, Szilveszter Vribék, Krisztián Farkas, Máté et al.

Quelle: Veterinary Sciences. Oct2025, Vol. 12 Issue 10, p935. 17p.

Sex-Specific Dietary Predictors of Blood Glucose Identified Through Decision Tree Modeling in Adults.

Autoren: Gautney, Joanna Aguilar, Christina Chan, Julian et al.

Quelle: Nutrients. Oct2025, Vol. 17 Issue 19, p3119. 12p.

Inferring leader-follower dynamics in three shark species using acoustic telemetry data.

Autoren: Kreuter, Nils Fernández-Gracia, Juan Eguíluz, Víctor M. et al.

Quelle: Movement Ecology. 8/28/2025, Vol. 13 Issue 1, p1-13. 13p.

Anti-Inflammatory Diet and Probiotic Supplementation as Strategies to Modulate Immune Dysregulation in Autism Spectrum Disorder.

Autoren: Naranjo-Galvis, Carlos Andrés Trejos-Gallego, Diana María Correa-Salazar, Cristina et al.

Quelle: Nutrients. Aug2025, Vol. 17 Issue 16, p2664. 21p.

Python Programming: Numerical Analysis/Machine Learning Series.

Autoren: Doskas, Constantinos

Quelle: ISSA Journal; Oct2023, Vol. 21 Issue 10, p34-38, 5p

Schlagwörter: PYTHON programming language, NUMERICAL analysis, MACHINE learning

A benchmark for computational analysis of animal behavior, using animal-borne tags.

Autoren: Hoffman, Benjamin Cusimano, Maddie Baglione, Vittorio et al.

Quelle: Movement Ecology. 12/18/2024, Vol. 12 Issue 1, p1-25. 25p.

Regularized Maximum Likelihood Estimation for the Random Coefficients Model in Python.

Autoren: Dunker, Fabian Mendoza, Emil Reale, Marco

Quelle: Mathematics (2227-7390); Dec2025, Vol. 13 Issue 23, p3764, 29p

Schlagwörter: PYTHON programming language, MAXIMUM likelihood statistics, CONSUMPTION (Economics), MATHEMATICAL programming, MULTILEVEL models, SCIENTIFIC computing

Strong correlation of gene counts and differentially expressed genes between a 3′ RNA-Seq and an RNA hybridization platform in transcriptome analyses from canine archival tissues.

Autoren: Haake, Alexander F. H. Loriani Fard, Alina K. Jovanovic, Vladimir M. et al.

Quelle: Frontiers in Veterinary Science. 2025, p1-13. 13p.

Recapitulation of Peste des Petits Ruminants (PPR) Prevalence in Small Ruminant Populations of Pakistan from 2004 to 2023: A Systematic Review and Meta-Analysis.

Autoren: Zafar, Saad Sarfraz, Muhammad Shehroz Ali, Sultan et al.

Quelle: Veterinary Sciences. Jun2024, Vol. 11 Issue 6, p280. 17p.

Tailoring the Nutritional Composition of Italian Foods to the US Nutrition5k Dataset for Food Image Recognition: Challenges and a Comparative Analysis.

Autoren: Bianco, Rachele Marinoni, Michela Coluccia, Sergio et al.

Quelle: Nutrients. Oct2024, Vol. 16 Issue 19, p3339. 19p.

Comparative virulence study of locally circulating peste des petits ruminants virus (PPRV) strains in indigenous Ethiopian goats.

Autoren: Aklilu, Fasil Ashenafi, Hagos Kassa, Tesfu et al.

Quelle: BMC Veterinary Research. 10/6/2025, Vol. 21 Issue 1, p1-11. 11p.

Schlagworte: Peste des petits ruminants, Microbial virulence, Symptoms, Goats, Comparative studies, Seroconversion, Communicable disease control

Geografische Kategorien: Ethiopia

Genome-wide analyses reveal intricate genetic mechanisms underlying egg production efficiency in chickens.

Autoren: Tan, Lizhi Cai, Xinyu Kong, Yuan et al.

Quelle: Journal of Animal Science & Biotechnology. 8/11/2025, Vol. 16 Issue 1, p1-21. 21p.

Classification performance and reproducibility of GPT-4 omni for information extraction from veterinary electronic health records.

Autoren: Wulcan, Judit M. Jacques, Kevin L. Lee, Mary Ann et al.

Quelle: Frontiers in Veterinary Science. 2025, p1-9. 9p.

Uncertainty quantification for severe-accident reactor modelling: Results and conclusions of the MUSA reactor applications work package

Autoren: Brumm, S. Gabrielli, F. Sanchez Espinoza, V. et al.

Weitere Verfasser: Brumm, S. Gabrielli, F. Sanchez Espinoza, V. et al.

Quelle: ISSN: 0306-4549 ; Annals of Nuclear Energy ; https://hal.science/hal-04777159 ; Annals of Nuclear Energy, 2025, 211, pp.110962. ⟨10.1016/j.anucene.2024.110962⟩.

Schlagwörter: Nuclear reactor, Severe accident, Modelling, Uncertainty quantification, MUSA, Source Term, [PHYS]Physics [physics], [SPI]Engineering Sciences [physics], [STAT]Statistics [stat]

Relation: info:eu-repo/grantAgreement//847441/EU/Euratom research and training programme 2014 – 2018/Euratom

Verfügbarkeit: https://hal.science/hal-04777159
https://hal.science/hal-04777159v1/document
https://hal.science/hal-04777159v1/file/Final_version_ANE20225.pdf
https://doi.org/10.1016/j.anucene.2024.110962