Results 51 to 60 of about 154,181 (262)
Plasma membranes contain dynamic nanoscale domains that organize lipids and receptors. Because viruses operate at similar scales, this architecture shapes early infection steps, including attachment, receptor engagement, and entry. Using influenza A virus and HIV‐1 as examples, we highlight how receptor nanoclusters, multivalent glycan interactions ...
Jan Schlegel, Christian Sieben
wiley +1 more source
Emerging SMOTE and GAN Variants for Data Augmentation in Imbalance Machine Learning Tasks: A Review
Class imbalance is a pervasive challenge in real-world machine learning (ML) applications, where the minority class, often the class of interest, is significantly underrepresented.
Amadi G. Udu +5 more
doaj +1 more source
On the Performance of Oversampling Techniques for Class Imbalance Problems [PDF]
Although over 90 oversampling approaches have been developed in the imbalance learning domain, most of the empirical study and application work are still based on the “classical” resampling techniques. In this paper, several experiments on 19 benchmark datasets are set up to study the efficiency of six powerful oversampling approaches, including both ...
Kong, J. +4 more
openaire +2 more sources
Proteostasis and the gut microbiota play a key role in shaping host physiology. Microbiota‐derived metabolites, vitamins, and RNA modulate host proteostasis. Findings from model systems, including C. elegans, indicate microbes can either stabilize or disrupt host proteostasis.
Abhishek Anil Dubey, Maria Ermolaeva
wiley +1 more source
Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection
In education, detecting students graduating on time is difficult due to high data complexity. Researchers have employed various approaches in identifying on-time graduation with Machine Learning, but it remains a challenging task due to the class ...
Theng-Jia Law +4 more
doaj +1 more source
Design and analysis strategies for robust microbiome ageing research
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Class imbalance represents one of the most persistent and practically consequential challenges in supervised machine learning, arising whenever the minority class—typically the class of primary interest—constitutes only a small fraction of the total ...
Wenlan Wei, Zhengchun Shang
doaj +1 more source
Background Long noncoding RNAs (lncRNAs) have dense linkages with various biological processes. Identifying interacting lncRNA-protein pairs contributes to understand the functions and mechanisms of lncRNAs. Wet experiments are costly and time-consuming.
Lihong Peng +4 more
doaj +1 more source
Conserved binding mode but diverse interfaces of MreC‐PBP2 interactions
The crystal structure of abMreC reveals a conserved two β‐barrel architecture and provides structural insights into its role within the bacterial elongasome. The abMreC–abPBP2 complex model identifies the molecular basis of MreC‐mediated PBP2 recognition, contributing to the regulation of peptidoglycan synthesis.
Hyunseok Jang +4 more
wiley +1 more source
Dealing with Class Imbalance using Thresholding
We propose thresholding as an approach to deal with class imbalance. We define the concept of thresholding as a process of determining a decision boundary in the presence of a tunable parameter. The threshold is the maximum value of this tunable parameter where the conditions of a certain decision are satisfied.
Charmgil Hong +2 more
openaire +2 more sources

