Results 51 to 60 of about 3,113,449 (308)
From sequence to enzyme mechanism using multi-label machine learning [PDF]
, 2014 Background: In this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes.
De Ferrari, Luna; id_orcid +5 more
core +1 more source
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Therapeutic apheresis (TA) is an established treatment modality for hematologic, neurologic, and immunologic disorders, yet access remains severely limited in sub‐Saharan Africa. Donor apheresis, including platelet apheresis collection from healthy donors, represents an important complementary modality supporting blood product ...
Nosa Bazuaye +33 more
wiley +1 more source
Improving Multi-Label Learning by Correlation Embedding
Applied Sciences, 2021 In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other.
Jun Huang +4 more
doaj +1 more source
A new genetic algorithm for multi-label correlation-based feature selection. [PDF]
, 2015 This paper proposes a new Genetic Algorithm for Multi-Label Correlation-Based Feature Selection (GA-ML-CFS). This GA performs a global search in the space of candidate feature subset, in order to select a high-quality feature subset is used by a multi ...
Jungjit, Suwimol, Freitas, Alex A.
core +1 more source
Multi-score Learning for Affect Recognition: the Case of Body Postures [PDF]
, 2011 An important challenge in building automatic affective state recognition systems is establishing the ground truth. When the groundtruth is not available, observers are often used to label training and testing sets.
Bianchi-Berthouze, N +5 more
core +1 more source
Calpain small subunit homodimerization is robust and calcium‐independent
FEBS Letters, EarlyView.Calpains dimerize via penta‐EF‐hand (PEF) domains. Using single‐molecule force spectroscopy, we measured the strength and kinetics of PEF–PEF homodimer binding. The interaction is robust, shows a transient conformational step before dissociation, and remains largely insensitive to Ca2+.
Nesha May O. Andoy +4 more
wiley +1 more source
Multi-label Ensemble Learning [PDF]
, 2011 Multi-label learning aims at predicting potentially multiple labels for a given instance. Conventional multi-label learning approaches focus on exploiting the label correlations to improve the accuracy of the learner by building an individual multi-label learner or a combined learner based upon a group of single-label learners.
Chuan Shi 0001 +3 more
openaire +1 more source
Classifier Chains for Multi-label Classification [PDF]
Machine Learning, 2009 The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels.
Jesse Read +3 more
openaire +5 more sources
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
Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
FEBS Letters, EarlyView.Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley +1 more source