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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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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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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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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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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 by de Oliveira, Joseffe Barroso, Gino, Joao Vitor Santa Rosa, de Lima, Carlos Jose, Filho, Joao Inacio da Silva

“… This application enables the comparison of triggered alarms with the resultant logical states obtained during the analysis…”
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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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Generation of Scale-Free Assortative Networks via Newman Rewiring for Simulation of Diffusion Phenomena by Di Lucchio, Laura, Modanese, Giovanni

“…By collecting and expanding several numerical recipes developed in previous work, we implement an object-oriented Python code, based on the networkX library, for the realization of the configuration…”
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Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models by Graham, Matthew M., Thiery, Alexandre H., Beskos, Alexandros

“…Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that has prompted the development of a number of algorithms,…”
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False Discovery Rate Smoothing by Tansey, Wesley, Koyejo, Oluwasanmi, Poldrack, Russell A., Scott, James G.

“…We present false discovery rate (FDR) smoothing, an empirical-Bayes method for exploiting spatial structure in large multiple-testing problems. FDR smoothing…”
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Missing Value Imputation in Relational Data Using Variational Inference by Fontaine, Simon, Kang, Jian, Zhu, Ji

“…In real-world networks, node attributes are often only partially observed, necessitating imputation to support analysis or enable downstream tasks…”
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Estimating Local Prevalence of Obesity Via Survey Under Cost Constraints: Stratifying ZCTAs in Virginia's Thomas Jefferson Health District by Lobo, Benjamin J., Bonds, Denise E., Kafadar, Karen

“… (here, ZIP Code Tabulation Areas, or ZCTAs) prior to conducting the survey for those situations where cost considerations make it infeasible to sample each geographic unit (here, ZCTA) in the region (here, TJHD…”
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Prediction of Positive Patient Health Questionnaire-2 Screening Using Area Deprivation Index in Primary Care by Duarte, Martha, Salamanca, Mayra, Gonzalez, Juan M, Roman Laporte, Roberto, Gattamorta, Karina, Lopez Martinez, Fernando Enrique, Clochesy, John, Rincon Acuna, Juan Carlos

“…Depression is recognized as a significant public health issue in the United States. The National Survey on Drug Use and Health reports that 21.0 million adults…”
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Black Box Variational Bayesian Model Averaging by Kejzlar, Vojtech, Bhattacharya, Shrijita, Son, Mookyong, Maiti, Tapabrata

“…For many decades now, Bayesian Model Averaging (BMA) has been a popular framework to systematically account for model uncertainty that arises in situations…”
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Feature and functional form selection in additive models via mixed-integer optimization by Navarro-García, Manuel, Guerrero, Vanesa, Durban, María, del Cerro, Arturo

“…Feature selection is a recurrent research topic in modern regression analysis, which strives to build interpretable models, using sparsity as a proxy, without sacrificing predictive power…”
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Graph analysis and visualization: discovering business opportunity in linked data by Brath, Richard, Jonker, David

“… The companion website offers data sets, full code examples in Python, and links to all the tools covered in the book…”
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SiFFS: A Scalable in-Fault Functional Simulation Framework for Fault Criticality Analysis by Haripriya, R S, Kollepara, Naveen, Tudu, Jaynarayan T

“… The problem of fault criticality analysis can be stated as a measure of the impact of a hardware fault on the model accuracy…”
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NODESTUDIO, A VISUAL PROGRAMMING FRAMEWORK FOR MUSCULOSKELETAL MRI ANALYSIS by Mendoza, M.A., Huo, Z., Diao, Y., Jin, Y., Huang, J., Bangerter, N.K.

“… to either implement or integrate software repositories in a clinical researcher's analysis pipeline…”
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Multilinear Regression Predictive Analysis of Additive Manufactured High-Carbon Steel Powder by Aladesanmi, Victor, Laseinde, Timothy

“… Multilinear Regression was conducted in the prediction of its yield strength and the ultimate tensile stress with the microhardness and varying the processing parameters of the laser power. Python…”
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Spatial Analysis Using Big Data by Hajime Seya, Yoshiki Yamagata

“…Spatial Analysis Using Big Data: Methods and Urban Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems…”
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Weather Prediction Using Logistic Regression (AI/ML Techniques)

“…’ steps along with choosing responsible dependent or independent parameters called as predictors to find results or outputs by various machine learning packages of Python like ‘Pandas’, ‘SEABORN’, ‘STATS MODEL’ etc. ,under ‘SCIKIT LEARN…”
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