Search Results - (( (statene OR studies) python code analysis ) OR ( stat python code analysis ))*

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…”
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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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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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Statistics Using Python by Campesato, Oswald

“…This book is designed to offer a fast-paced yet thorough introduction to essential statistical concepts using Python code samples, and aims to assist data scientists in their daily endeavors…”
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Statistical Analyses and Reproducible Research by Gentleman, Robert, Temple Lang, Duncan

“…It is important, if not essential, to integrate the computations and code used in data analyses, methodological descriptions, simulations, and so on with the documents that describe and rely…”
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Facilitating the Calculation of the Efficient Score Using Symbolic Computing by B. Sibley, Alexander, Li, Zhiguo, Jiang, Yu, Li, Yi-Ju, Chan, Cliburn, Allen, Andrew, Owzar, Kouros

“… In the analysis of data from high-throughput genomic assays, inference on the basis of the score usually enjoys greater stability, considerably higher computational efficiency, and lends itself…”
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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

“…Explore the fundamentals of data analysis, and statistics with case studies using Python…”
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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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An extensive study of the effects of different deep learning models on code vulnerability detection in Python code by Wang, Rongcun, Xu, Senlei, Ji, Xingyu, Tian, Yuan, Gong, Lina, Wang, Ke

“… However, few studies empirically studied the impacts of different deep learning models on code vulnerability detection in Python…”
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PyNose: A Test Smell Detector For Python by Wang, Tongjie, Golubev, Yaroslav, Smirnov, Oleg, Li, Jiawei, Bryksin, Timofey, Ahmed, Iftekhar

“… In this paper, we strive to extend the research to Python, build a tool for detecting test smells in this language, and conduct an empirical analysis of test smells in Python…”
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Detecting and Explaining Python Name Errors by Wang, Jiawei, Li, Li, Liu, Kui, Du, Xiaoning

“… NameError is among one of those errors that are widespread in the Python community, as confirmed in our empirical study…”
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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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Comparative Analysis of AI Models for Python Code Generation: A HumanEval Benchmark Study by Bayram, Ali, Menekse Dalveren, Gonca Gokce, Derawi, Mohammad

“…This study conducts a comprehensive comparative analysis of six contemporary artificial intelligence models for Python code generation using the HumanEval benchmark…”
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PBjam: A Python Package for Automating Asteroseismology of Solar-like Oscillators by Nielsen, M. B., Davies, G. R., Ball, W. H., Lyttle, A. J., Li 李坦, T. 达, Hall, O. J., Chaplin, W. J., Gaulme, P., Carboneau, L., Ong 王加, J. M. J. 冕, García, R. A., Mosser, B., Roxburgh, I. W., Corsaro, E., Benomar, O., Moya, A., Lund, M. N.

“… So far the process of performing an asteroseismic analysis of a star has remained somewhat esoteric and inaccessible to nonexperts…”
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A Severity Assessment of Python Code Smells by Gupta, Aakanshi, Gandhi, Rashmi, Jatana, Nishtha, Jatain, Divya, Panda, Sandeep Kumar, Ramesh, Janjhyam Venkata Naga

“… This study investigates five code smells diffused in 20 Python software comprising 10550 classes and analyses its severity index using metric distribution at the class level…”
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SBML2HYB: a Python interface for SBML compatible hybrid modeling by Pinto, José, Costa, Rafael S, Alexandre, Leonardo, Ramos, João, Oliveira, Rui

“…Abstract Summary Here, we present sbml2hyb, an easy-to-use standalone Python tool that facilitates the conversion of existing mechanistic models of biological systems in Systems Biology Markup Language (SBML…”
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vcf2gwas: Python API for comprehensive GWAS analysis using GEMMA by Vogt, Frank, Shirsekar, Gautam, Weigel, Detlef

“…Abstract Motivation Genome-wide association study (GWAS) requires a researcher to perform a multitude of different actions during analysis…”
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ACPYPE - AnteChamber PYthon Parser interfacE by Sousa da Silva, Alan W, Vranken, Wim F

“… (for NMR data analysis) and ARIA (for structure calculations from NMR data). ACPYPE is open source code, under GNU GPL v3, and is available as a stand-alone application at http…”
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Why Do Machine Learning Notebooks Crash? An Empirical Study on Public Python Jupyter Notebooks by Wang, Yiran, Meijer, Willem, Lopez, Jose Antonio Hernandez, Nilsson, Ulf, Varro, Daniel

“… This paper presents a comprehensive empirical study focusing on crashes in publicly available Python ML notebooks…”
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“Who is sexually harassed? A python code haha”: imaginaries of a post-violent AI world by Lee, Jeehyun Jenny

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