Search Results - (( (statne OR stat) python code analysis ) OR ( (statene OR state:ny) python code analysis ))

Search alternatives:

Refine Results
  1. 1

    Decoding the JAK-STAT Axis in Colorectal Cancer with AI-HOPE-JAK-STAT: A Conversational Artificial Intelligence Approach to Clinical–Genomic Integration by Yang, Ei-Wen, Waldrup, Brigette, Velazquez-Villarreal, Enrique

    ISSN: 2072-6694, 2072-6694
    Published: Switzerland MDPI AG 17.07.2025
    Published in Cancers (17.07.2025)
    “…: AI-HOPE-JAK-STAT combines large language models (LLMs), a natural language-to-code engine, and harmonized public CRC datasets from cBioPortal…”
    Get full text
    Journal Article
  2. 2

    Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial by Smith, Matthew J., Mansournia, Mohammad A., Maringe, Camille, Zivich, Paul N., Cole, Stephen R., Leyrat, Clémence, Belot, Aurélien, Rachet, Bernard, Luque‐Fernandez, Miguel A.

    ISSN: 0277-6715, 1097-0258, 1097-0258
    Published: England Wiley Subscription Services, Inc 30.01.2022
    Published in Statistics in medicine (30.01.2022)
    “…The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize…”
    Get full text
    Journal Article
  3. 3

    Python Data Analyst's Toolkit - Learn Python and Python-Based Libraries with Applications in Data Analysis and Statistics by Rajagopalan, Gayathri

    ISBN: 9781484263983, 1484263987, 9781484263990, 1484263995
    Published: Berkeley, CA Apress, an imprint of Springer Nature 2021
    “… The code is presented in Jupyter notebooks that can further be adapted and extended. This book is divided into three parts - programming with Python, data analysis and visualization, and statistics…”
    Get full text
    eBook
  4. 4

    LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data by Ross, Dylan H, Cho, Jang Ho, Zhang, Rutan, Hines, Kelly M, Xu, Libin

    ISSN: 1520-6882
    Published: United States 17.11.2020
    Published in Analytical chemistry (Washington) (17.11.2020)
    “… Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification…”
    Get more information
    Journal Article
  5. 5

    Python for MATLAB Development - Extend MATLAB with 300,000+ Modules from the Python Package Index by Danial, Albert

    ISBN: 9781484272220, 1484272226, 1484272234, 9781484272237
    Published: Berkeley, CA Apress, an imprint of Springer Nature 2022
    “…This book shows you how to enhance MATLAB with Python solutions to a vast array of computational problems in science, engineering, optimization, statistics, finance, and simulation…”
    Get full text
    eBook
  6. 6

    Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors by Peng, Yun, Gao, Shuzheng, Gao, Cuiyun, Huo, Yintong, Lyu, Michael R.

    ISSN: 1558-1225
    Published: ACM 14.04.2024
    “…As a dynamic programming language, Python has become increasingly popular in recent years…”
    Get full text
    Conference Proceeding
  7. 7

    Discovering Repetitive Code Changes in Python ML Systems by Dilhara, Malinda, Ketkar, Ameya, Sannidhi, Nikhith, Dig, Danny

    ISSN: 1558-1225
    Published: ACM 01.05.2022
    “… Despite the extraordinary rise in popularity of Python-based ML systems, they do not benefit from these advances…”
    Get full text
    Conference Proceeding
  8. 8

    Percolation theory using Python by Malthe-Sørenssen, Anders

    ISBN: 3031598997, 9783031598999, 3031599004, 9783031599002
    Published: Cham Springer 2024
    “… Readers will learn how to generate, analyze, and comprehend data and models, with detailed theoretical discussions complemented by accessible computer codes…”
    Get full text
    eBook Book
  9. 9

    PyAnalyzer: An Effective and Practical Approach for Dependency Extraction from Python Code by Jin, Wuxia, Xu, Shuo, Chen, Dawei, He, Jiajun, Zhong, Dinghong, Fan, Ming, Chen, Hongxu, Zhang, Huijia, Liu, Ting

    ISSN: 1558-1225
    Published: ACM 14.04.2024
    “… However, dynamic language features in Python make code behaviors obscure and nondeter-ministic…”
    Get full text
    Conference Proceeding
  10. 10

    Code2graph: Automatic Generation of Static Call Graphs for Python Source Code by Gharibi, Gharib, Tripathi, Rashmi, Lee, Yugyung

    ISSN: 2643-1572
    Published: ACM 01.09.2018
    “… However, there is a lack of software tools that can automatically analyze the Python source-code and construct its static call graph…”
    Get full text
    Conference Proceeding
  11. 11

    TypeEvalPy: A Micro-Benchmarking Framework for Python Type Inference Tools by Venkatesh, Ashwin Prasad S., Sabu, Samkutty, Wang, Jiawei, Mir, Amir M., Li, Li, Bodden, Eric

    ISSN: 2574-1934
    Published: ACM 14.04.2024
    “… TYPE-EVALPy contains 154 code snippets with 845 type annotations across 18 categories that target various Python features…”
    Get full text
    Conference Proceeding
  12. 12

    Towards Effective Static Type-Error Detection for Python by Oh, Wonseok, Oh, Hakjoo

    ISSN: 2643-1572
    Published: ACM 27.10.2024
    “… This empirical investigation revealed four key static-analysis features that are crucial for the effective detection of Python type errors in practice…”
    Get full text
    Conference Proceeding
  13. 13
  14. 14

    Multi-Step Automated Generation of Parameter Docstrings in Python: An Exploratory Study by Venkatkrishna, Vatsal, Nagabushanam, Durga Shree, Simon, Emmanuel Iko-Ojo, Vidoni, Melina

    ISSN: 2574-1934
    Published: ACM 14.04.2024
    “…Documentation debt hinders the effective utilisation of open-source software. Although code summarisation tools have been helpful for developers, most would…”
    Get full text
    Conference Proceeding
  15. 15

    PyTy: Repairing Static Type Errors in Python by Chow, Yiu Wai, Di Grazia, Luca, Pradel, Michael

    ISSN: 1558-1225
    Published: ACM 14.04.2024
    “… As more and more code bases get type-annotated, static type checkers detect an increasingly large number of type errors…”
    Get full text
    Conference Proceeding
  16. 16

    A Python Instrument Control and Data Acquisition Suite for Reproducible Research by Koerner, Lucas J., Caswell, Thomas A., Allan, Daniel B., Campbell, Stuart I.

    ISSN: 0018-9456, 1557-9662
    Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.04.2020
    “… The National Synchrotron Light Source-II (NSLS-II) has generated an open-source Python data acquisition, management, and analysis software suite that automates X-ray…”
    Get full text
    Journal Article
  17. 17

    AI Writes, We Analyze: The ChatGPT Python Code Saga by Rabbi, Md Fazle, Champa, Arifa, Zibran, Minhaz, Islam, Md Rakibul

    ISSN: 2574-3864
    Published: ACM 15.04.2024
    “…In this study, we quantitatively analyze 1,756 AI-written Python code snippets in the DevGPT dataset and evaluate them for quality and security issues…”
    Get full text
    Conference Proceeding
  18. 18

    Language Models for Code Completion: A Practical Evaluation by Izadi, Maliheh, Katzy, Jonathan, van Dam, Tim, Otten, Marc, Popescu, Razvan Mihai, van Deursen, Arie

    ISSN: 1558-1225
    Published: ACM 14.04.2024
    “…Transformer-based language models for automatic code completion have shown great promise so far, yet the evaluation of these models rarely uses real data…”
    Get full text
    Conference Proceeding
  19. 19

    A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts by Grotov, Konstantin, Titov, Sergey, Sotnikov, Vladimir, Golubev, Yaroslav, Bryksin, Timofey

    ISSN: 2574-3864
    Published: ACM 01.05.2022
    “…: high number of code clones, low reproducibility, etc. In this work, we carry out a comparison between Python code written in Jupyter Notebooks and in traditional Python scripts…”
    Get full text
    Conference Proceeding
  20. 20

    Bloat beneath Python’s Scales: A Fine-Grained Inter-Project Dependency Analysis by Drosos, Georgios-Petros, Sotiropoulos, Thodoris, Spinellis, Diomidis, Mitropoulos, Dimitris

    ISSN: 2994-970X, 2994-970X
    Published: New York, NY, USA ACM 12.07.2024
    “… In this work, we conduct a large-scale, fine-grained analysis to understand bloated dependency code in the PyPI ecosystem…”
    Get full text
    Journal Article