Search Results - (( (statneeeneeneeeeeeeoren OR stat) python code analysis ) OR ( stateene python code analysis ))~

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

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

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

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

Python Programming: Numerical Analysis/Machine Learning Series.

Authors: Doskas, Constantinos

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

Subject Terms: PYTHON programming language, NUMERICAL analysis, MACHINE learning

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

Authors: Dunker, Fabian Mendoza, Emil Reale, Marco

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

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

Automation of Software Code Analysis Using Machine Learning Methods.

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

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

Python Programming Log Analysis Series.

Authors: Doskas, Constantinos

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

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

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

Authors: Tarek A. Atwan

Resource Type: eBook.

Subjects: Python (Computer program language), Machine learning

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

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

Authors: Li, Jingmao Xu, Yaqing Ma, Shuangge et al.

Source: Bioinformatics; Sep2025, Vol. 41 Issue 9, p1-9, 9p

Subject Terms: CANCER research, GENOTYPE-environment interaction, DIAGNOSTIC imaging, DEEP learning, EMPIRICAL research, COMPUTER simulation

ImmobileParticles_MatchAndAnalyse: python code used for analysis of image data in Lenz et al. 2021, 'Measuring impacts of microplastic treatments via image recognition on immobilised particles below 100 µm'

Authors: Lenz, Robin Enders, Kristina Fischer, Franziska et al.

Subject Terms: microplastics microscopy image analysis python altair, scipo, stat

Relation: https://zenodo.org/record/4592320

Availability: https://zenodo.org/record/4592320

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

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

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

Compound-SNE: comparative alignment of t-SNEs for multiple single-cell omics data visualization.

Authors: Cess, Colin G Haghverdi, Laleh

Source: Bioinformatics; Jul2024, Vol. 40 Issue 7, p1-5, 5p

Subject Terms: RESEARCH personnel, DATA visualization, GENE expression, DATA analysis, PYTHON programming language

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

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

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

An extensive study of the effects of different deep learning models on code vulnerability detection in Python code.

Authors: Wang, Rongcun Xu, Senlei Ji, Xingyu et al.

Source: Automated Software Engineering; Jun2024, Vol. 31 Issue 1, p1-35, 35p

Programmed differently? Testing for gender differences in Python programming style and quality on GitHub.

Authors: Brooke, Siân

Source: Journal of Computer-Mediated Communication; Jan2024, Vol. 29 Issue 1, p1-13, 13p

Subject Terms: PYTHON programming language, PYTHONS, RANDOM forest algorithms, GENDER

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.

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

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

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

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

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

Subject Terms: MACHINE learning, CLUSTERING algorithms, COMPUTER science, ALGORITHMS, SOURCE code

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.

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

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

SparseCoder: Advancing source code analysis with sparse attention and learned token pruning.

Authors: Yang, Xueqi Jakubowski, Mariusz Kang, Li et al.

Source: Empirical Software Engineering; Feb2025, Vol. 30 Issue 1, p1-30, 30p

Analysis of Program Representations Based on Abstract Syntax Trees and Higher-Order Markov Chains for Source Code Classification Task.

Authors: Gorchakov, Artyom V. Demidova, Liliya A. Sovietov, Peter N.

Source: Future Internet; Sep2023, Vol. 15 Issue 9, p314, 28p

Subject Terms: MARKOV processes, K-nearest neighbor classification, SUPPORT vector machines, SOURCE code, SYNTAX (Grammar), CLASSIFICATION algorithms

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

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

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

tonalli: an asexual genetic code to characterize APOGEE-2 stellar spectra. I. Validation with synthetic and solar spectra.

Authors: Adame, Lucía Román-Zúñiga, Carlos Hernández, Jesús et al.

Source: RAS Techniques & Instruments; 2024, Vol. 3 Issue 1, p771-799, 29p

Subject Terms: STELLAR spectra, GENETIC algorithms, ASTRONOMICAL observations, SPECTRUM analysis, MONTE Carlo method, POWER spectra, TEMPERATURE of stars