Search Results - (( (stateeny OR stat) python code analysis ) OR ( (statenit OR statement) python code analysis ))*

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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Assessing code readability in Python programming courses using eye‐tracking by Segedinac, Milan, Savić, Goran, Zeljković, Ivana, Slivka, Jelena, Konjović, Zora

“… This paper assesses the readability of Python code statements commonly used in undergraduate programming courses…”
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Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements by Xu, Xiaoyan, Cogo, Filipe R., McIntosh, Shane

“…Understanding code coverage is an important precursor to software maintenance activities (e.g., better testing…”
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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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GraphPyRec: A novel graph-based approach for fine-grained Python code recommendation by Zong, Xing, Zheng, Shang, Zou, Haitao, Yu, Hualong, Gao, Shang

“… Significant progress has been made in code recommendation for static languages in recent years, but it remains challenging for dynamic languages like Python as accurately determining data flows…”
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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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Stress-Constrained Topology Optimization for Commercial Software: A Python Implementation for ABAQUS by Fernandes, Pedro, Ferrer, Àlex, Gonçalves, Paulo, Parente, Marco, Pinto, Ricardo, Correia, Nuno

“…, using validated FEM commercial software. The methodology was validated by comparing the sensitivity analysis with the results obtained through finite differences and solving two benchmark problems with the following optimizers…”
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Python for Everyone: Learn and polish your coding skills in Python (English Edition) by Saurabh Chandrakar, Dr. Nilesh Bhaskarrao Bahadure

“…A hands-on guide that will help you to write clean and efficient code in Python Key Features…”
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Data Science Fundamentals with R, Python, and Open Data by Cremonini, Marco

“…Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start data science projectsOrganized with a strong focus on open data, This book discusses concepts…”
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Robustness Analysis of Watermarking Techniques for LLM-Generated Code by Cao, Di, Liu, Shigang, Zhang, Jun, Xiang, Yang

“…The widespread adoption of large language models (LLMs) for code generation raises critical concerns regarding authorship attribution and accountability, motivating the development of watermarking techniques to trace model outputs…”
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Visualization of Aqueous Geochemical Data Using Python and WQChartPy by Yang, Jing, Liu, Honghua, Tang, Zhonghua, Peeters, Luk, Ye, Ming

“… This method note presents WQChartPy, an open‐source Python package developed to plot a total of 12 diagrams for analysis of aqueous geochemical data…”
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Nestedness for Dummies (NeD): A user friendly web interface for exploratory nestedness analysis by Strona, Giovanni, Galli, Paolo, Seveso, Davide, Montano, Simone, Fattorini, Simone

“…Recent theoretical advances in nestedness analysis have led to the introduction of several alternative metrics to overcome most of the problems biasing the use of matrix 'temperature' calculated…”
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Artificial intelligence programming with Python: from zero to hero by Xiao, Perry

“…A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python…”
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Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation by Oda, Yusuke, Fudaba, Hiroyuki, Neubig, Graham, Hata, Hideaki, Sakti, Sakriani, Toda, Tomoki, Nakamura, Satoshi

“…Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages…”
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The Python workshop: a practical, no-nonsense introduction to Python development by Andrew Bird, Bird, Dr Lau Cher Han, Han, Mario Corchero Jimenez, Jimenez, Graham Lee, Lee, Corey Wade, Wade

“…Cut through the noise and get real results with a step-by-step approach to learning Python 3…”
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NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs by Di Domenico, Daniel, Lima, João V. F., Cavalheiro, Gerson G. H.

“… As a counterpoint to that trend, this paper presents a performance and programming effort analysis with Python, an interpreted and high-level language, which was applied to develop the kernels…”
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Semi-Supervised Code Translation Overcoming the Scarcity of Parallel Code Data by Zhu, Ming, Karim, Mohimenul, Lourentzou, Ismini, Yao, Danfeng Daphne

“… MIRACLE leverages static analysis and compilation to generate synthetic parallel code datasets with enhanced quality and alignment to address the challenge of data scarcity…”
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