Optimal Timings Codebook.xlsx by Emma Spillane (11249121)

Supplementary file 1_Development of a venous thromboembolism risk prediction model for patients with primary membranous nephropathy based on machine learning.docx by Lian Li (49049)

“…Objective<p>This study utilizes real-world data from primary membranous nephropathy (PMN) patients to preliminarily develop a venous thromboembolism (VTE) risk prediction model with machine learning. …”

Data Sheet 1_TBESO-BP: an improved regression model for predicting subclinical mastitis.pdf by Kexin Han (10933209)

“…In this study, an enhanced neural backpropagation (BP) network model for predicting somatic cell count is introduced. The model is based on TBESO (Multi-strategy Boosted Snake Optimizer) and utilizes monthly Dairy Herd Improvement (DHI) data to forecast the status of subclinical mastitis in cows.…”

Data_Sheet_1_A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer.docx by Tingting Ma (228102)

Data_Sheet_1_Risk factors of lymph node metastasis or lymphovascular invasion for superficial esophageal squamous cell carcinoma: A practical and effective predictive nomogram base... by Yali Tao (10772160)

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

Data_Sheet_3_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

Data_Sheet_4_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

Data_Sheet_2_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

Data_Sheet_1_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

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.…”

primary ATRT single cell RNA-seq by Mamy ANDRIANTERANAGNA (12913196)

Supporting data for “The role of forest composition heterogeneity on temperate ecosystem carbon dynamic under climate change" by Ziyu Lin (9151064)

“…The process includes (1) harmonizing Landsat 5, 7, 8, and Sentinel-2 data using the HLS algorithm, and (2) filling temporal gaps with an optimized object-based STARFM fusion algorithm. …”

Optimal Time Intervals of Breech Births Dataset by Emma Spillane (11249121)

Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

Confusion matrix for multiclass classification. by Ebru Ergün (21395498)

“…The experimental protocol involved eight participants performing tasks across four classes of scrolling text. To optimize system accuracy and speed, EEG and NIRS data were segmented into discrete temporal windows. …”

General flow chart of the proposed method. by Ebru Ergün (21395498)

“…The experimental protocol involved eight participants performing tasks across four classes of scrolling text. To optimize system accuracy and speed, EEG and NIRS data were segmented into discrete temporal windows. …”

Image 2_Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.png by Min Liang (363007)

“…</p>Conclusions<p>This study presents a robust machine learning model and a web-based tool that assist healthcare practitioners in personalized clinical decision-making and treatment optimization for ASC patients following primary tumor resection.…”

Image 1_Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.png by Min Liang (363007)

“…</p>Conclusions<p>This study presents a robust machine learning model and a web-based tool that assist healthcare practitioners in personalized clinical decision-making and treatment optimization for ASC patients following primary tumor resection.…”