Results 61 to 70 of about 4,317,504 (301)
IEEE Access, 2020 Excessive time complexity has severely restricted the application of support vector machine (SVM) in large-scale multi-label classification. Thus, this paper proposes an efficient multi-label SVM classification algorithm by combining approximate extreme ...
Zhongwei Sun +4 more
doaj +1 more source
From mice to humans—divergent strategies for intestinal homeostasis and regeneration
FEBS Letters, EarlyView.Recent advances such as organoid genome editing, xenotransplantation, imaging, and whole‐genome sequencing have enabled direct studies of human intestinal stem cells (ISCs). These studies reveal species‐specific features, including slower ISC proliferation, distinct injury responses, slower somatic mutation accumulation in humans, and an inverse ...
Keiko Ishikawa +2 more
wiley +1 more source
EnzML : multi-label prediction of enzyme classes using InterPro signatures [PDF]
, 2012 LDF is funded by ONDEX DTG, BBSRC TPS Grant BB/F529038/1 of the Centre for Systems Biology at Edinburgh and the University of Newcastle. SA is supported by by a Wellcome Trust Value In People award and, together with IG, the Centre for Systems Biology at
Goryanin Igor +14 more
core +1 more source
Addressing Imbalance Problem for Multi Label Classification of Scholarly Articles
IEEE Access, 2023 Scientific document classification is an important field of machine learning. Currently, scientific document category identification is done manually.
Aiman Hafeez +6 more
doaj +1 more source
A TDIDT technique for multi-label classification
2010 10th International Conference on Intelligent Systems Design and Applications, 2010 There are numerous problems of increasing significance where a pattern can have several classes simultaneously associated. This kind of problems, usually called multi-label problems, should be tackled with specific techniques in order to generate models more accurate than those obtained with classical classification algorithms.
Eva Lucrecia Gibaja Galindo +3 more
openaire +2 more sources
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.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
A review of multi-instance learning assumptions [PDF]
, 2010 Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector.
Frank, Eibe, Foulds, James Richard
core +1 more source
Deep active learning for multi label text classification
Scientific ReportsGiven a set of labels, multi-label text classification (MLTC) aims to assign multiple relevant labels for a text. Recently, deep learning models get inspiring results in MLTC.
Qunbo Wang +5 more
doaj +1 more source
An Analysis of Chaining in Multi-Label Classification
, 2012 The idea of classifier chains has recently been introduced as a promising technique for multi-label classification. However, despite being intuitively appealing and showing strong performance in empirical studies, still very little is known about the main principles underlying this type of method.
Krzysztof Dembczynski +2 more
openaire +2 more sources
Structure‐forward targeting of claudins with synthetic binders
FEBS Letters, EarlyView.Claudins form the paracellular barriers between epithelial and endothelial tissues at tight junctions and are targets for molecular binders with the goal of modulating barrier permeability. Claudin‐binding molecules are relevant in drug delivery or in altering claudin interactions with disease‐causing proteins.
Alex J. Vecchio
wiley +1 more source