Results 81 to 90 of about 524,719 (351)
FEBS Letters, EarlyView.Enteropathogenic E. coli (EPEC) infects the human intestinal epithelium, resulting in severe illness and diarrhoea. In this study, we compared the infection of cancer‐derived cell lines with human organoid‐derived models of the small intestine. We observed a delayed in attachment, inflammation and cell death on primary cells, indicating that host ...
Mastura Neyazi +5 more
wiley +1 more source
Retrenching partial requirements into system definitions: A simple feature interaction case study
, 2003 In conventional model-oriented formal refinement, the abstract model is supposed to capture all the properties of interest in the system, in an as-clutter-free-as-possible manner. Subsequently, the refinement process guides development inexorably towards
Banach, Richard; id_orcid +5 more
core +1 more source
Feature engineering for machine learning enabled early prediction of battery lifetime
Journal of Power Sources, 2022 Accurate battery lifetime estimates enable accelerated design of novel battery materials and determination of optimal use protocols for longevity in deployments.
Noah H. Paulson +5 more
semanticscholar +1 more source
Organoids in pediatric cancer research
FEBS Letters, EarlyView.Organoid technology has revolutionized cancer research, yet its application in pediatric oncology remains limited. Recent advances have enabled the development of pediatric tumor organoids, offering new insights into disease biology, treatment response, and interactions with the tumor microenvironment.
Carla Ríos Arceo, Jarno Drost
wiley +1 more source
Local feature weighting in nearest prototype classification [PDF]
, 2008 The distance metric is the corner stone of nearest neighbor (NN)-based methods, and therefore, of nearest prototype (NP) algorithms. That is because they classify depending on the similarity of the data.
Isasi, Pedro +2 more
core +1 more source
Grasshopper Optimization Algorithm with Crossover Operators for Feature Selection and Solving Engineering Problems [PDF]
, 2022 Feature selection (FS) is an irreplaceable phase that makes data mining more efficient. It effectively enhances the implementation and decreases the computational problem of learning models.
Ewees, Ahmed A. +3 more
core +1 more source
Synergistic Feature Engineering and Ensemble Learning for Early Chronic Disease Prediction
IEEE AccessChronic diseases, a global public health challenge, necessitate the deployment of cutting-edge predictive models for early diagnosis and personalized interventions.
Hamdi A. Al-Jamimi
semanticscholar +1 more source
Improving materials property predictions for graph neural networks with minimal feature engineering
Machine Learning: Science and Technology, 2023 Graph neural networks (GNNs) have been employed in materials research to predict physical and functional properties, and have achieved superior performance in several application domains over prior machine learning approaches.
Guojing Cong, Victor Fung
doaj +1 more source
FEBS Letters, EarlyView.Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho +3 more
wiley +1 more source
Feature Interactions Outside a Telecom Domain [PDF]
, 2001 Feature interactions in the original sense of the term (i.e. within a telecommunications domain), have now been the subject of significant research activity for over ten years.
Lynne Blair (7588349) +7 more
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