We present RegressionExplorer, a Visual Analytics tool for the interactive exploration of logistic regression models. Our application domain is Clinical Biostatistics, where models are derived from patient data with the aim to obtain clinically meaningful insights and consequences. Development and interpretation of a proper model requires domain expertise and insight into model characteristics. Because of time constraints, often a limited number of candidate models is evaluated. RegressionExplorer enables experts to quickly generate, evaluate, and compare many different models, taking the workflow for model development as starting point. Global patterns in parameter values of candidate models can be explored effectively. In addition, experts are enabled to compare candidate models across multiple subpopulations. The insights obtained can be used to formulate new hypotheses or to steer model development. The effectiveness of the tool is demonstrated for two uses cases: prediction of a cardiac conduction disorder in patients after receiving a heart valve implant and prediction of hypernatremia in critically ill patients.
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http://dx.doi.org/10.1109/TVCG.2018.2865043 | DOI Listing |
J Med Internet Res
December 2024
Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Background: Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored.
Objective: This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide.
Chem Rev
December 2024
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States.
Conventional artificial intelligence (AI) systems are facing bottlenecks due to the fundamental mismatches between AI models, which rely on parallel, in-memory, and dynamic computation, and traditional transistors, which have been designed and optimized for sequential logic operations. This calls for the development of novel computing units beyond transistors. Inspired by the high efficiency and adaptability of biological neural networks, computing systems mimicking the capabilities of biological structures are gaining more attention.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Horticultural Crops Disease and Pest Management Research Unit, United States Department of Agriculture-Agricultural Research Service, Corvallis, OR, USA.
Pathogens have evolved effector proteins to suppress host immunity and facilitate plant infections. RxLR effectors are small, secreted effector proteins with conserved RxLR and dEER amino acid motifs at the N terminus and highly variable C termini and are commonly found in oomycete species. We provide computational approaches to annotate RxLR candidate effector genes in a genome assembly in FASTA format with an available GFF file.
View Article and Find Full Text PDFJ Mol Model
December 2024
Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, Avenida Ferrocarril San Rafael Atlixco, Número 186, Colonia Leyes de Reforma 1A Sección, Alcaldía Iztapalapa, Código Postal 09310, Ciudad de Mexico, Mexico.
Context: Antioxidants are known to play a beneficial role in human health. Caffeic acid has been previously recognized as efficient in this context. However, such a capability can be enhanced through structural modification.
View Article and Find Full Text PDFTheor Appl Genet
December 2024
Key Laboratory of Germplasm Enhancement, Physiology and Ecology of Food Crops in Cold Region, Ministry of Education, Northeast Agricultural University, Harbin, 150030, China.
Integrated genome-wide association study and linkage mapping revealed genetic basis of alkalinity tolerance during rice germination. The key gene OsWRKY49 was further verified in transgenic plants. With the widespread use of the rice direct seeding cultivation model, improving the tolerance of rice varieties to salinity-alkalinity at the germination stage has become increasingly important.
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