Search Results - (( (stateni OR stat) python code analysis ) OR ( (statnych OR statneed) python code analysis ))~

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

Authors: Li J Xu Y Ma S et al.

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

Publication Type: Journal Article

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

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

Authors: Dufossé, Paul Benzekry, Sébastien Méthodes computationnelles pour la prise en charge thérapeutique en oncologie : Optimisation des stratégies par modélisation mécaniste et statistique (COMPO) et al.

Contributors: Dufossé, Paul Benzekry, Sébastien Méthodes computationnelles pour la prise en charge thérapeutique en oncologie : Optimisation des stratégies par modélisation mécaniste et statistique (COMPO) et al.

Source: 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⟩

Subject Terms: 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]

Subject Geographic: Bruxelles (BEL), Belgium

Relation: hal-04384220; https://hal.science/hal-04384220; https://hal.science/hal-04384220/document; https://hal.science/hal-04384220/file/preprint_arxiv.pdf

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

Prediction of Positive Patient Health Questionnaire-2 Screening Using Area Deprivation Index in Primary Care.

Authors: Duarte, Martha Salamanca, Mayra Gonzalez, Juan M. et al.

Source: Clinical Nursing Research. Jun2024, Vol. 33 Issue 5, p355-369. 15p.

Subject Terms: *MENTAL depression risk factors, *RISK assessment, *PREDICTION models, *SOCIAL determinants of health, *RECEIVER operating characteristic curves, *QUESTIONNAIRES, *PRIMARY health care, *RETROSPECTIVE studies, *QUANTITATIVE research, *DESCRIPTIVE statistics, *NURSING interventions, *SURVEYS, *HEALTH behavior, *MEDICAL records, *ACQUISITION of data, *SOCIODEMOGRAPHIC factors, *MACHINE learning, *DATA analysis software, *ACCURACY, *COMPARATIVE studies, *SOCIAL classes, *SENSITIVITY & specificity (Statistics), *SOCIAL isolation, *ALGORITHMS, *REGRESSION analysis, *MENTAL depression, *EVALUATION

Geographic Terms: FLORIDA

Extended sample size calculations for evaluation of prediction models using a threshold for classification.

Authors: Whittle R Ensor J Archer L et al.

Source: BMC medical research methodology [BMC Med Res Methodol] 2025 Jul 01; Vol. 25 (1), pp. 170. Date of Electronic Publication: 2025 Jul 01.

Publication Type: Journal Article

Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 100968545 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2288 (Electronic) Linking ISSN: 14712288 NLM ISO Abbreviation: BMC Med Res Methodol Subsets: MEDLINE

MeSH Terms: Models, Statistical*, Risk Assessment/methods ; Sample Size ; Humans ; Predictive Value of Tests ; Sensitivity and Specificity ; Algorithms

A practical guide to estimating treatment effects in patients with rheumatic diseases using real-world data.

Authors: Pripp AH Łosińska K Korkosz M et al.

Source: Rheumatology international [Rheumatol Int] 2024 Jul; Vol. 44 (7), pp. 1265-1274. Date of Electronic Publication: 2024 Apr 24.

Publication Type: Journal Article

Journal Info: Publisher: Springer International Country of Publication: Germany NLM ID: 8206885 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1437-160X (Electronic) Linking ISSN: 01728172 NLM ISO Abbreviation: Rheumatol Int Subsets: MEDLINE

MeSH Terms: Rheumatic Diseases*/therapy, Observational Studies as Topic/methods ; Humans ; Treatment Outcome ; Propensity Score ; Regression Analysis ; Data Interpretation, Statistical

Phenotype structuring in collective cell migration: a tutorial of mathematical models and methods.

Authors: Lorenzi T Painter KJ Villa C

Source: Journal of mathematical biology [J Math Biol] 2025 May 16; Vol. 90 (6), pp. 61. Date of Electronic Publication: 2025 May 16.

Publication Type: Journal Article; Review

Journal Info: Publisher: Springer Verlag Country of Publication: Germany NLM ID: 7502105 Publication Model: Electronic Cited Medium: Internet ISSN: 1432-1416 (Electronic) Linking ISSN: 03036812 NLM ISO Abbreviation: J Math Biol Subsets: MEDLINE

MeSH Terms: Cell Movement*/physiology , Models, Biological*, Neoplasms/pathology ; Phenotype ; Humans ; Mathematical Concepts ; Computer Simulation ; Animals

Comparative effectiveness of laparoscopic versus open colectomy in colon cancer patients: a study protocol for emulating a target trial using cancer registry data.

Authors: Abera SF Robers G Kästner A et al.

Source: Journal of cancer research and clinical oncology [J Cancer Res Clin Oncol] 2025 Jan 11; Vol. 151 (1), pp. 34. Date of Electronic Publication: 2025 Jan 11.

Publication Type: Clinical Trial Protocol; Journal Article

Journal Info: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 7902060 Publication Model: Electronic Cited Medium: Internet ISSN: 1432-1335 (Electronic) Linking ISSN: 01715216 NLM ISO Abbreviation: J Cancer Res Clin Oncol Subsets: MEDLINE

MeSH Terms: Colectomy*/methods , Colonic Neoplasms*/surgery , Colonic Neoplasms*/pathology , Colonic Neoplasms*/mortality , Laparoscopy*/methods, Adult ; Female ; Humans ; Male ; Registries ; Survival Rate ; Clinical Trials, Phase III as Topic ; Randomized Controlled Trials as Topic ; Multicenter Studies as Topic

Quantification of racial and ethnic disparities in alcohol-related problems in light of different methodological approaches.

Authors: Ye Y Lui CK Martinez P et al.

Source: Addiction (Abingdon, England) [Addiction] 2025 May; Vol. 120 (5), pp. 873-883. Date of Electronic Publication: 2025 Jan 12.

Publication Type: Journal Article

Journal Info: Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 9304118 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1360-0443 (Electronic) Linking ISSN: 09652140 NLM ISO Abbreviation: Addiction Subsets: MEDLINE

MeSH Terms: Alcohol Drinking*/ethnology , Alcohol Drinking*/epidemiology , Alcohol-Related Disorders*/ethnology , Alcohol-Related Disorders*/epidemiology , Alcoholism*/ethnology , Alcoholism*/epidemiology , Ethnicity*/statistics & numerical data , Health Status Disparities*, Black or African American/statistics & numerical data ; Hispanic or Latino/statistics & numerical data ; United States/epidemiology ; White/statistics & numerical data ; Adolescent ; Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Cross-Sectional Studies

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

TRIQS/SOM 2.0: Implementation of the stochastic optimization with consistent constraints for analytic continuation.

Authors: Krivenko, Igor Mishchenko, Andrey S.

Source: Computer Physics Communications. Nov2022, Vol. 280, pN.PAG-N.PAG. 1p.

Subject Terms: *PYTHON programming language, *GREEN'S functions, *MAXIMUM entropy method, *COVARIANCE matrices, *STATISTICAL ensembles, *PROGRAMMING languages, *REGULARIZATION parameter

Generation of Scale-Free Assortative Networks via Newman Rewiring for Simulation of Diffusion Phenomena

Authors: Laura Di Lucchio Giovanni Modanese

Source: Stats, Vol 7, Iss 1, Pp 220-234 (2024)

Subject Terms: assortative networks, Physics - General Physics, General Physics (physics.gen-ph), network rewiring, Statistics, 0103 physical sciences, FOS: Physical sciences, scale-free networks, agent-based models, Bass innovation diffusion model, 01 natural sciences, HA1-4737

Access URL: http://arxiv.org/abs/2310.13716
https://doaj.org/article/1ed58599ad2b446e8fba7bbd67fd64e7

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

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

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

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

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.

Economic and Veterinary Evaluation of Pet Insurance in Türkiye: Potential, Role in Health, and Impact on Practice.

Authors: GENÇGÖNÜL, Betül Zehra¹ betul.zehra@hacettepe.edu.tr

Source: Kafkas Üniversitesi Veteriner Fakültesi Dergisi. Jan/Feb2026, Vol. 32 Issue 1, p147-153. 7p.

Machine learning for prioritizing security code-based Vulnerability discovery

Authors: Ballestas, Rafael García Bedoya, Olmer

Contributors: Ballestas, Rafael García Bedoya, Olmer

Source: instname:Universidad de Bogotá Jorge Tadeo Lozano ; reponame:Expeditio Repositorio Institucional UJTL

Subject Terms: Ingeniería, Máquinas, Automatización, Auditorías, Seguridad, Security auditing

Subject Geographic: Colombia

File Description: 58 páginas; application/pdf

Relation: Alon, U., Zilberstein, M., Levy, O., and Yahav, E. (2019). code2vec: learning distributed representations of code. Proc. ACM Program. Lang., 3(POPL):1– 29.; Antunes, N. and Vieira, M. (2009). Comparing the Effectiveness of Penetration Testing and Static Code Analysis on the Detection of SQL Injection Vulnerabilities in Web Services. In 2009 15th IEEE Pacific Rim International Symposium on Dependable Computing, pages 301–306, Shanghai, China. IEEE.; Chang, Y., Liu, B., Cong, L., Deng, H., Li, J., and Chen, Y. (2019). Vulnerability Parser: A Static Vulnerability Analysis System for Android Applications. J. Phys.: Conf. Ser., 1288:012053.; Chong, S., Guttman, J., Datta, A., Myers, A., Pierce, B., Schaumont, P., Sherwood, T., and Zeldovich, N. (2016). Report on the NSF Workshop on Formal Methods for Security. arXiv:1608.00678 [cs]. arXiv: 1608.00678.; Dauber, E., Caliskan, A., Harang, R., Shearer, G., Weisman, M., Nelson, F., and Greenstadt, R. (2019). Git Blame Who?: Stylistic Authorship Attribution of Small, Incomplete Source Code Fragments. Proceedings on Privacy Enhancing Technologies, 2019(3):389–408. arXiv: 1701.05681; Ferreira, A. M. and Kleppe, H. (2011). Effectiveness of Automated Application Penetration Testing Tools. Technical report, OS3 University of Amsterdam.; FluidAttacks (2020). Integrates.; Free Software Foundation (2020). GNU diffutils; Ghaffarian, S. M. and Shahriari, H. R. (2017). Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey. ACM Computing Surveys, 50(4):1–36.; Li, Z., Zou, D., Xu, S., Jin, H., Zhu, Y., and Chen, Z. (2018a). SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities. arXiv:1807.06756 [cs, stat]. arXiv: 1807.06756.; Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., Deng, Z., and Zhong, Y. (2018b). VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. Proceedings 2018 Network and Distributed System Security Symposium. arXiv: 1801.01681.; Moor, O. d., Verbaere, M., Hajiyev, E., Avgustinov, P., Ekman, T., Ongkingco, N., Sereni, D., and Tibble, J. (2007). Keynote Address: .QL for Source Code Analysis. In Seventh IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2007), pages 3–16, Paris, France. IEEE; Ng, A. (2016). What Artificial Intelligence Can and Can’t Do Right Now. Harvard Business Review. Section: Analytics.; Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.; Rice, H. G. (1953). Classes of recursively enumerable sets and their decision problems. Trans. Amer. Math. Soc., 74(2):358–366.; Schwartz, E. J., Avgerinos, T., and Brumley, D. (2010). All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask). In 2010 IEEE Symposium on Security and Privacy, pages 317–331, Oakland, CA, USA. IEEE.; Sommer, R. and Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. In 2010 IEEE Symposium on Security and Privacy, pages 305–316, Oakland, CA, USA. IEEE.; Stefinko, Y., Piskozub, A., and Banakh, R. (2016). Manual and automated penetration testing. Benefits and drawbacks. Modern tendency. In 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), pages 488–491, Lviv, Ukraine. IEEE.; Yamaguchi, F., Wressnegger, C., Gascon, H., and Rieck, K. (2013). Chucky: exposing missing checks in source code for vulnerability discovery. In Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security - CCS ’13, pages 499–510, Berlin, Germany. ACM Press.; http://hdl.handle.net/20.500.12010/17241; http://expeditio.utadeo.edu.co

Availability: https://hdl.handle.net/20.500.12010/17241
http://expeditio.utadeo.edu.co

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

Authors: Murphy, Rebecca Sharpe, Helen

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

Portable Electronic Olfactometer for Non-Invasive Screening of Canine Ehrlichiosis: A Proof-of-Concept Study Using Machine Learning.

Authors: Durán Cotrina, Silvana Valentina Acevedo, Cristhian Manuel Durán Carrillo Gómez, Jeniffer Katerine

Source: Veterinary Sciences. Jan2026, Vol. 13 Issue 1, p88. 20p.

Varietal Differences in Kidney Beans Modulate Gut Microbiota and Inflammation During High-Fat Diet-Induced Obesity in Male Mice.

Authors: Rodrigue, Alexane F. Pereira, Bruna B. Freije, Giorgio et al.

Source: Nutrients. Feb2026, Vol. 18 Issue 3, p461. 34p.

Zinc as a Biomarker of Nutritional Status and Clinical Burden in Recessive Dystrophic Epidermolysis Bullosa: Implications for Preventive Monitoring.

Authors: Quintana-Castanedo, Lucía Maseda, Rocío Sánchez-Ramón, Silvia et al.

Source: Nutrients. Jan2026, Vol. 18 Issue 2, p232. 19p.

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.