Suchergebnisse - (( (statne OR stat) python code analysis ) OR ( stateeeeeeeeen 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: Lung Neoplasms*/genetics , Skin Neoplasms*/genetics , Melanoma*/genetics , Gene-Environment Interaction* , Deep Learning*, 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

CLM App: Interlamellar Distance of Pearlite via CLM Revisited and Automated.

Autoren: Zouhar, Martin Mikmeková, Šárka Hovjacký, Jan et al.

Quelle: Materials (1996-1944); Feb2026, Vol. 19 Issue 3, p635, 17p

Schlagwörter: AUTOMATION, IMAGE analysis, QUALITY control, MICROSTRUCTURE

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

Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial: Introduction to computational causal inference using reproducible Stata, R, and Python code: a tutorial

Autoren: Matthew J. Smith Mohammad A. Mansournia Camille Maringe et al.

Weitere Verfasser: Matthew J. Smith Mohammad A. Mansournia Camille Maringe et al.

Quelle: Stat Med
RISalud-ANDALUCIA. Repositorio Institucional de Salud de Andalucía
instname

Schlagwörter: regression adjustment, 01 natural sciences, Article, Applications of statistics to biology and medical sciences, meta analysis, Modelos estadísticos, double-robust methods, Humans, Computer Simulation, causal inference, 0101 mathematics, Propensity Score, propensity score, Probability, Simulación por computador, Proyectos de investigación, Models, Statistical, g-formula, Causalidad, Humanos, 3. Good health, Causality, machine learning, Puntaje de propensión, G-methods, Research Design, targeted maximum likelihood estimation, Probabilidad, inverse probability weighting

Dateibeschreibung: application/pdf; application/xml

Zugangs-URL: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/sim.9234
https://pubmed.ncbi.nlm.nih.gov/34713468
http://hdl.handle.net/10668/22539
https://hdl.handle.net/10668/24648
https://researchonline.lshtm.ac.uk/id/eprint/4664004/1/sim.9234.pdf

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

Triglycerides and Hypertension in a Korean Population: An Individual-Level Mendelian Randomization Analysis.

Autoren: Huang, Ximei Kim, Minjoo

Quelle: Nutrients. Feb2026, Vol. 18 Issue 4, p633. 15p.

Explainable Machine Learning for Functional Data.

Autoren: GOODE, KATHERINE TUCKER, J. DEREK RIES, DANIEL et al.

Quelle: Journal of Data Science; Jan2026, Vol. 24 Issue 1, p125-145, 21p

Schlagwörter: SUPERVISED learning, MACHINE learning, FUNCTIONAL analysis, FEATURE extraction, FORENSIC sciences

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

Automation of Software Code Analysis Using Machine Learning Methods.

Autoren: Moshkin, V. S. Dyrnochkin, A. A. Yarushkina, N. G.

Quelle: Pattern Recognition & Image Analysis; Sep2023, Vol. 33 Issue 3, p417-424, 8p

Prediction of Chemically Modified Antimicrobial Peptides and Their Sub-functional Activities Using Hybrid Features.

Autoren: Yao Y Zhang D Fan H et al.

Quelle: Probiotics and antimicrobial proteins [Probiotics Antimicrob Proteins] 2026 Jan; Vol. 18 (1), pp. 921-935. Date of Electronic Publication: 2025 May 21.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Springer Country of Publication: United States NLM ID: 101484100 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1867-1314 (Electronic) Linking ISSN: 18671306 NLM ISO Abbreviation: Probiotics Antimicrob Proteins Subsets: MEDLINE

MeSH-Schlagworte: Antimicrobial Peptides*/chemistry , Antimicrobial Peptides*/pharmacology , Computational Biology*/methods, Humans

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

Relation: info:eu-repo/grantAgreement//847441/EU/MANAGEMENT AND UNCERTAINTIES OF SEVERE ACCIDENTS/MUSA

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

Tiger colour morphs in zoos raise conservation and welfare concerns.

Autoren: Erzinçlioğlu, Tanya S. Piaz, Mina T. Chinpilas, Tunya et al.

Quelle: Discover Animals (3004-894X). 2/18/2026, Vol. 3 Issue 1, p1-20. 20p.

Schlagworte: Inbreeding, Animal welfare, Animal coloration, Environmental protection, Policy analysis, Human-animal relationships, Animal breeding

Geografische Kategorien: Thailand

Time Series Analysis with Python Cookbook : Practical Recipes for the Complete Time Series Workflow, From Modern Data Engineering to Advanced Forecasting and Anomaly Detection

Autoren: Tarek A. Atwan

Resource Type: eBook.

Schlagworte: Python (Computer program language), Machine learning

Categories: COMPUTERS / Data Science / Data Modeling & Design, COMPUTERS / Data Science / General, COMPUTERS / Data Science / Data Analytics

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.

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.

A comparison of machine learning algorithms for survival with missing data ; Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes

Autoren: Dufossé, Paul Benzekry, Sébastien Aix Marseille Université (AMU) et al.

Weitere Verfasser: Dufossé, Paul Benzekry, Sébastien Aix Marseille Université (AMU) et al.

Quelle: 54es Journées de Statistique de la SFdS
JDS 2023 - 54e Journées de Statistique de la SFds
https://hal.science/hal-04384220
JDS 2023 - 54e Journées de Statistique de la SFds, SFdS, Jul 2023, Bruxelles (BEL), Belgique. ⟨10.48550/arXiv.2303.13590⟩

Schlagwörter: Machine Learning (stat.ML), Machine Learning (cs.LG), Machine learning, neural networks, survival data, simulated data, missing data, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.AP]Statistics [stat]/Applications [stat.AP]

Geographisches Schlagwort: Bruxelles (BEL), Belgium

Verfügbarkeit: https://hal.science/hal-04384220
https://hal.science/hal-04384220v1/document
https://hal.science/hal-04384220v1/file/preprint_arxiv.pdf
https://doi.org/10.48550/arXiv.2303.13590

Python Programming Log Analysis Series.

Autoren: Doskas, Constantinos

Quelle: ISSA Journal; Oct2021, Vol. 19 Issue 10, p20-23, 4p

Schlagwörter: PYTHON programming language, SAWLOGS, RECORDS management, INFORMATION assurance, DATABASES, TEXT files

Eating Disorders in People Who Identify as Gender-Diverse: Associations Between Gender Diversity, Eating Disorder Diagnosis, Minority Stress Experiences and Mental Health Comorbidity.

Autoren: Murphy, Rebecca Sharpe, Helen

Quelle: Nutrients. Feb2026, Vol. 18 Issue 3, p458. 24p.

Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions.

Autoren: Stoica, Alexandru Stefan Babiceanu, Daniel Mihaescu, Marian Cristian et al.

Quelle: Mathematics (2227-7390); Nov2025, Vol. 13 Issue 22, p3589, 19p

Schlagwörter: MACHINE learning, CLUSTERING algorithms, COMPUTER science, ALGORITHMS, SOURCE code