Data Sheet 1_Does advancement in marker-less pose-estimation mean more quality research? A systematic review.docx by Shivam Bhola (22106639)

Algorithms and evaluation metrics integrated in the COPS framework. by Teemu J. Rintala (19333003)

Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

Data Sheet 1_AI-driven optimization of dynamic vibration absorbers with hydraulic amplifier and mechanical inerter integration.pdf by Ahmed Shamseldin (19759437)

Data_Sheet_1_Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms.docx by Xinping Lin (1591438)

The PS-VAE enables targeted downstream neural analyses of the mouse face video. by Matthew R. Whiteway (11467528)

Data_Sheet_1_A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.docx by Çaǧlar Çaǧlayan (12253934)

Machine Learning-Driven Kinetic Elucidation for Sustainable Solvent-Free Continuous ε‑Caprolactone Production via Propionaldehyde-Mediated Nanocarbon Catalysis by Qing Hu (152962)

Machine Learning-Driven Kinetic Elucidation for Sustainable Solvent-Free Continuous ε‑Caprolactone Production via Propionaldehyde-Mediated Nanocarbon Catalysis by Qing Hu (152962)

A data-driven machine learning approach for discovering potent LasR inhibitors by Christabel Ming Ming Koh (16798162)

Table_1_Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.docx by Liyin Zhang (6371999)

Data used to drive the Double Layer Carbon Model in the Qinling Mountains. by Huiwen Li (17705280)

“…The model divides the soil profile into topsoil (0-20 cm) and subsoil (20–100 cm) layers to match the SOC maps of the corresponding two layers generated by data-driven models. Each of these layers contains a young carbon pool (CY) with a higher decomposition rate and an old carbon pool (CO) with a lower decomposition rate. …”

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning by Lu Xin (728966)

“…Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.…”

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning by Lu Xin (728966)

“…Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.…”

Data_Sheet_1_Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.PDF by Michael Moor (10887888)

Data_Sheet_1_A GLM-based zero-inflated generalized Poisson factor model for analyzing microbiome data.pdf by Jinling Chi (18698947)

“…The complex characteristics of microbiome data, including high dimensionality, zero inflation, and over-dispersion, pose new statistical challenges for downstream analysis.…”

Data_Sheet_2_A GLM-based zero-inflated generalized Poisson factor model for analyzing microbiome data.ZIP by Jinling Chi (18698947)

“…The complex characteristics of microbiome data, including high dimensionality, zero inflation, and over-dispersion, pose new statistical challenges for downstream analysis.…”

Table_1_Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.pdf by Michael Moor (10887888)

DataSheet1_Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study.docx by Sergei Evteev (18294157)

DataSheet_1_Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis.zip by Asefa Adimasu Taddese (8602431)

“…Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model.…”