Predictive model-building process. by Mukhtar Ijaiya (18935122)

An example of a key driver diagram to optimize triage rates was developed during the quality improvement process. by Rebecca Goertzen (22008461)

Optimal image settings for object detection of Iba1 immunolabelled cells in DAB. by Danish M. Anwer (15383441)

Rapid Prediction of Chemical Ecotoxicity Through Genetic Algorithm Optimized Neural Network Models by Ping Hou (17213)

“…To reduce the manual tuning effort on optimal network architecture, a genetic algorithm is investigated to automatically search and configure the network architecture. …”

A schematic view of the step-by-step computational approach in the current study. by Hamed Rezaei (11085366)

Spatial multivariate optimization for a sampling redesign with a reduced sample size of soil chemical properties by Tamara Cantú Maltauro (15199725)

Supporting data for “Digital immunochromatography: A bead-based bilateral zigzag flow immuno-assay with adaptive differential noise correction for rapid and precise protein detecti... by Zhiting Peng (9722516)

Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site by Mengyan Zhang (6686543)

“…We have integrated these machine learning algorithms with laboratory automation and high-throughput processes for reliable data generation. …”

Schematic overview of the XBSI framework. by Rajeev Bopche (20208606)

Workflow overview to reconstruct integrated host-virus genome-scale models and detect promising compounds with an antiviral activity. by Nantia Leonidou (14833068)

Data_Sheet_1_Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing.PD... by Sheng-Feng Sung (616343)

Video-to-Model Data Set by Sönke Knoch (8616117)

“…<div>This data set belongs to the paper "Video-to-Model: Unsupervised Trace Extraction from Videos for Process Discovery and Conformance Checking in Manual Assembly", submitted on March 24, 2020, to the 18th International Conference on Business Process Management (BPM).…”

Data_Sheet_1_Early Prediction of Cardiogenic Shock Using Machine Learning.PDF by Yale Chang (11701611)

“…The algorithm was trained on 8 years of de-identified data (from 2010 to 2017) collected from a large regional healthcare system. …”

Data Sheet 1_Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients—Machine learning models for clinical decision support.do... by Yuanguo Xiong (20135991)

“…Thirteen distinct machine learning (ML) algorithms were trained and benchmarked to construct an optimal risk stratification framework. …”

Image_1_Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation.tif by Qi Wang (22418)

“…</p>Methods<p>To this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. …”

DATASET AI by Elena Stamate (18836305)

“…Performance metrics include accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC).</p><p dir="ltr">All data have been de-identified and processed in accordance with institutional ethical standards.…”

Comparative analysis of clinical characteristics between ovarian cancer and ovarian cyst patients by Li (20568581)

“…This study aims to integrate serum biomarkers with clinical features to construct efficient diagnostic prediction models and staging prediction algorithms for ovarian cancer. This multidimensional prediction model has the potential to improve early diagnosis rates of ovarian cancer, optimize treatment decision-making processes, reduce unnecessary surgical interventions, and provide scientific basis for individualized treatment plans, ultimately improving patient prognosis and quality of life. …”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…</li><li>The dataframe of extracted colour features from all leaf images and lab variables (ecophysiological predictors and variables to be predicted)</li><li>Set of scripts used for image pre-processing, features extraction, data analytsis, visualization and Machine learning algorithms training, using ImageJ, R and Python.…”