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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

“…: AI-HOPE-JAK-STAT combines large language models (LLMs), a natural language-to-code engine, and harmonized public CRC datasets from cBioPortal…”
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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.

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

“… 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…”
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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

“… Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification…”
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Python for MATLAB Development - Extend MATLAB with 300,000+ Modules from the Python Package Index by Danial, Albert

“…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…”
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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.

“…As a dynamic programming language, Python has become increasingly popular in recent years…”
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Discovering Repetitive Code Changes in Python ML Systems by Dilhara, Malinda, Ketkar, Ameya, Sannidhi, Nikhith, Dig, Danny

“… Despite the extraordinary rise in popularity of Python-based ML systems, they do not benefit from these advances…”
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Percolation theory using Python by Malthe-Sørenssen, Anders

“… Readers will learn how to generate, analyze, and comprehend data and models, with detailed theoretical discussions complemented by accessible computer codes…”
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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

“… However, dynamic language features in Python make code behaviors obscure and nondeter-ministic…”
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Code2graph: Automatic Generation of Static Call Graphs for Python Source Code by Gharibi, Gharib, Tripathi, Rashmi, Lee, Yugyung

“… However, there is a lack of software tools that can automatically analyze the Python source-code and construct its static call graph…”
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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

“… TYPE-EVALPy contains 154 code snippets with 845 type annotations across 18 categories that target various Python features…”
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Towards Effective Static Type-Error Detection for Python by Oh, Wonseok, Oh, Hakjoo

“… This empirical investigation revealed four key static-analysis features that are crucial for the effective detection of Python type errors in practice…”
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Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data by Pakkir Shah, Abzer K., Walter, Axel, Ottosson, Filip, Russo, Francesco, Navarro-Diaz, Marcelo, Boldt, Judith, Kalinski, Jarmo-Charles J., Kontou, Eftychia Eva, Elofson, James, Polyzois, Alexandros, González-Marín, Carolina, Farrell, Shane, Aggerbeck, Marie R., Pruksatrakul, Thapanee, Chan, Nathan, Wang, Yunshu, Pöchhacker, Magdalena, Brungs, Corinna, Cámara, Beatriz, Caraballo-Rodríguez, Andrés Mauricio, Cumsille, Andres, de Oliveira, Fernanda, Dührkop, Kai, El Abiead, Yasin, Geibel, Christian, Graves, Lana G., Hansen, Martin, Heuckeroth, Steffen, Knoblauch, Simon, Kostenko, Anastasiia, Kuijpers, Mirte C. M., Mildau, Kevin, Papadopoulos Lambidis, Stilianos, Portal Gomes, Paulo Wender, Schramm, Tilman, Steuer-Lodd, Karoline, Stincone, Paolo, Tayyab, Sibgha, Vitale, Giovanni Andrea, Wagner, Berenike C., Xing, Shipei, Yazzie, Marquis T., Zuffa, Simone, de Kruijff, Martinus, Beemelmanns, Christine, Link, Hannes, Mayer, Christoph, van der Hooft, Justin J. J., Damiani, Tito, Pluskal, Tomáš, Dorrestein, Pieter, Stanstrup, Jan, Schmid, Robin, Wang, Mingxun, Aron, Allegra, Ernst, Madeleine, Petras, Daniel

“… We provide explanations and code in two scripting languages (R and Python) as well…”
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Multi-Step Automated Generation of Parameter Docstrings in Python: An Exploratory Study by Venkatkrishna, Vatsal, Nagabushanam, Durga Shree, Simon, Emmanuel Iko-Ojo, Vidoni, Melina

“…Documentation debt hinders the effective utilisation of open-source software. Although code summarisation tools have been helpful for developers, most would…”
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PyTy: Repairing Static Type Errors in Python by Chow, Yiu Wai, Di Grazia, Luca, Pradel, Michael

“… As more and more code bases get type-annotated, static type checkers detect an increasingly large number of type errors…”
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A Python Instrument Control and Data Acquisition Suite for Reproducible Research by Koerner, Lucas J., Caswell, Thomas A., Allan, Daniel B., Campbell, Stuart I.

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

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

“…Transformer-based language models for automatic code completion have shown great promise so far, yet the evaluation of these models rarely uses real data…”
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A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts by Grotov, Konstantin, Titov, Sergey, Sotnikov, Vladimir, Golubev, Yaroslav, Bryksin, Timofey

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

“… In this work, we conduct a large-scale, fine-grained analysis to understand bloated dependency code in the PyPI ecosystem…”
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