Výsledky vyhľadávania - (( (statene OR state:md) python code analysis ) OR ( stat python code analysis ))*

  1. 1

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

    ISSN: 2072-6694, 2072-6694
    Vydavateľské údaje: Switzerland MDPI AG 17.07.2025
    Vydané v 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…”
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    Journal Article
  2. 2

    Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial Autor 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
    Vydavateľské údaje: England Wiley Subscription Services, Inc 30.01.2022
    Vydané v 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…”
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    Journal Article
  3. 3

    PypKa: A Flexible Python Module for Poisson-Boltzmann-Based pKa Calculations Autor Reis, Pedro B P S, Vila-Viçosa, Diogo, Rocchia, Walter, Machuqueiro, Miguel

    ISSN: 1549-960X, 1549-960X
    Vydavateľské údaje: 26.10.2020
    “…The protonation of titratable residues has a significant impact on the structure and function of biomolecules, influencing many physicochemical and ADME…”
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  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 Autor Ross, Dylan H, Cho, Jang Ho, Zhang, Rutan, Hines, Kelly M, Xu, Libin

    ISSN: 1520-6882, 1520-6882
    Vydavateľské údaje: United States 17.11.2020
    Vydané v Analytical chemistry (Washington) (17.11.2020)
    “… Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification…”
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  5. 5

    Percolation theory using Python Autor Malthe-Sørenssen, Anders

    ISBN: 3031598997, 9783031598999, 3031599004, 9783031599002
    Vydavateľské údaje: Cham Springer 2024
    “… 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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    E-kniha Kniha
  6. 6

    ACPYPE - AnteChamber PYthon Parser interfacE Autor Sousa da Silva, Alan W, Vranken, Wim F

    ISSN: 1756-0500, 1756-0500
    Vydavateľské údaje: London BioMed Central 23.07.2012
    Vydané v BMC research notes (23.07.2012)
    “… (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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  7. 7

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

    ISSN: 2574-3864
    Vydavateľské údaje: 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…”
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    Konferenčný príspevok..
  8. 8

    PSHRisk-Tool: A Python-Based Computational Tool for Developing Site Seismic Hazard Analysis and Failure Risk Assessment of Infrastructure Autor Nahar, Tahmina Tasnim, Rahman, Md Motiur, Kim, Dookie

    ISSN: 2076-3417, 2076-3417
    Vydavateľské údaje: Basel MDPI AG 01.10.2020
    Vydané v Applied sciences (01.10.2020)
    “… vulnerable condition for the infrastructure. This python-code based tool can demonstrate the source identification, probability distribution plot of magnitude and distance…”
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  9. 9

    Statistics Using Python Autor Campesato, Oswald

    ISBN: 9781683928805, 1683928806
    Vydavateľské údaje: Berlin Mercury Learning and Information 2024
    “…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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    E-kniha
  10. 10

    mdciao: Accessible Analysis and Visualization of Molecular Dynamics Simulation Data Autor Pérez-Hernández, Guillermo, Hildebrand, Peter W.

    ISSN: 1553-7358, 1553-734X, 1553-7358
    Vydavateľské údaje: United States Public Library of Science 21.04.2025
    Vydané v PLoS computational biology (21.04.2025)
    “…) for easy, one-shot analysis and representation of molecular dynamics (MD) simulation data. Building upon the widely used concept of residue-residue contact-frequencies…”
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  11. 11

    Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data Autor 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

    ISSN: 1754-2189, 1750-2799, 1750-2799
    Vydavateľské údaje: London Nature Publishing Group UK 01.01.2025
    Vydané v Nature protocols (01.01.2025)
    “… We provide explanations and code in two scripting languages (R and Python) as well…”
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    From code to reliability: Python-powered Paraconsistent Logic for alarm detection in the power gris/ Do codigo a confiabilidade: Logica Paraconsistente impulsionada por Python para a deteccao de alarmes na rede de energia Autor de Oliveira, Joseffe Barroso, Gino, Joao Vitor Santa Rosa, de Lima, Carlos Jose, Filho, Joao Inacio da Silva

    ISSN: 2178-9010, 2178-9010
    Vydavateľské údaje: Sindicato das Secretarias e Secretarios do Estado de Sao Paulo 01.10.2023
    “… This application enables the comparison of triggered alarms with the resultant logical states obtained during the analysis…”
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    WEDAP: A Python Package for Streamlined Plotting of Molecular Simulation Data Autor Yang, Darian T, Chong, Lillian T

    ISSN: 1549-960X, 1549-960X
    Vydavateľské údaje: United States 12.08.2024
    “… Here, we present the WEDAP Python package for simplifying the analysis of data generated from either conventional MD simulations or the weighted ensemble…”
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  14. 14

    MDAnalysis: A toolkit for the analysis of molecular dynamics simulations Autor Michaud-Agrawal, Naveen, Denning, Elizabeth J., Woolf, Thomas B., Beckstein, Oliver

    ISSN: 0192-8651, 1096-987X, 1096-987X
    Vydavateľské údaje: Hoboken Wiley Subscription Services, Inc., A Wiley Company 30.07.2011
    Vydané v Journal of computational chemistry (30.07.2011)
    “…MDAnalysis is an object‐oriented library for structural and temporal analysis of molecular dynamics (MD…”
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    xNose: A Test Smell Detector for C# Autor Paul, Partha P., Akanda, Md Tonoy, Ullah, M. Raihan, Mondal, Dipto, Chowdhury, Nazia S., Tawsif, Fazle M.

    ISSN: 2574-1934
    Vydavateľské údaje: ACM 14.04.2024
    “…Test smells, similar to code smells, can negatively impact both the test code and the production code being tested…”
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    Konferenčný príspevok..
  16. 16

    Nestedness for Dummies (NeD): A user friendly web interface for exploratory nestedness analysis Autor Strona, Giovanni, Galli, Paolo, Seveso, Davide, Montano, Simone, Fattorini, Simone

    ISSN: 1548-7660, 1548-7660
    Vydavateľské údaje: Foundation for Open Access Statistics 01.08.2014
    Vydané v Journal of statistical software (01.08.2014)
    “…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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  17. 17

    RETRACTED: PSHRisk-Tool: A Python-Based Computational Tool for Developing Site Seismic Hazard Analysis and Failure Risk Assessment of Infrastructure Autor Nahar, Tahmina, Rahman, Md, Kim, Dookie

    ISSN: 2076-3417, 2076-3417
    Vydavateľské údaje: 24.10.2020
    Vydané v Applied sciences (24.10.2020)
    “… vulnerable condition for the infrastructure. This python-code based tool can demonstrate the source identification, probability distribution plot of magnitude and distance…”
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    Journal Article
  18. 18

    Sarkas: A Fast Pure-Python Molecular Dynamics Suite for Non-Ideal Plasmas Autor Silvestri, Luciano G., Stanek, Luke J., Choi, Yongjun, Murillo, Michael S., Dharuman, Gautham

    ISSN: 2576-7208
    Vydavateľské údaje: IEEE 12.09.2021
    “… As a matter of fact for each of these research fields we can find MD codes, open-source or proprietary, optimized for the simulation of their respective dynamical systems…”
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    Konferenčný príspevok..
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    Enabling grand-canonical Monte Carlo: Extending the flexibility of GROMACS through the GromPy python interface module Autor Pool, René, Heringa, Jaap, Hoefling, Martin, Schulz, Roland, Smith, Jeremy C., Feenstra, K. Anton

    ISSN: 0192-8651, 1096-987X, 1096-987X
    Vydavateľské údaje: Hoboken Wiley Subscription Services, Inc., A Wiley Company 05.05.2012
    Vydané v Journal of computational chemistry (05.05.2012)
    “…We report on a python interface to the GROMACS molecular simulation package, GromPy (available at https://github.com/GromPy…”
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    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories Autor Mukhaleva, Elizaveta, Manookian, Babgen, Chen, Hanyu, Sivaraj, Indira R, Ma, Ning, Wei, Wenyuan, Urbaniak, Konstancja, Gogoshin, Grigoriy, Bhattacharya, Supriyo, Vaidehi, Nagarajan, Rodin, Andrei S, Branciamore, Sergio

    ISSN: 1549-960X, 1549-960X
    Vydavateľské údaje: United States 10.02.2025
    “… However, the analysis of MD simulation trajectories has not been data-driven but rather dependent on the user's prior knowledge of the systems, thus limiting the scope and utility of the MD simulations…”
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