Results 41 to 50 of about 1,256,985 (257)
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
, 2017 Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate.
C Varela +8 more
core +1 more source
Mapping the evolution of mitochondrial complex I through structural variation
FEBS Letters, EarlyView.Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source
A Comparison of Multi-instance Learning Algorithms [PDF]
, 2006 Motivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years. Compared with standard supervised learning, the MI learning
Dong, Lin
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
Multi-Label Learning With Label Specific Features Using Correlation Information
IEEE Access, 2019 To deal with the problem where each instance is associated with multiple labels, a lot of multi-label learning algorithms have been developed in recent years.
Huirui Han +4 more
doaj +1 more source
Semi-Supervised Multi-Label Dimensionality Reduction Learning by Instance and Label Correlations
Mathematics, 2023 The label learning mechanism is challenging to integrate into the training model of the multi-label feature space dimensionality reduction problem, making the current multi-label dimensionality reduction methods primarily supervision modes.
Runxin Li +5 more
doaj +1 more source
Molecular Oncology, EarlyView.We developed and validated a DNA methylation–based biomarker panel to distinguish pleural mesothelioma from other pleural conditions. Using the IMPRESS technology, we translated this panel into a clinically applicable assay. The resulting two classifier models demonstrated excellent performance, achieving high AUC values and strong diagnostic accuracy.
Janah Vandenhoeck +12 more
wiley +1 more source
MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
Applied SciencesMulti-instance learning (MIL) is a critical area in machine learning, particularly for applications where data points are grouped into bags. Traditional methods, however, often face challenges in accurately classifying these bags. This paper presents the
Kadriye Filiz Balbal
doaj +1 more source
Multi-task Deep Reinforcement Learning with PopArt
, 2018 The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent ...
Czarnecki, Wojciech +5 more
core +1 more source
Next‐generation proteomics improves lung cancer risk prediction
Molecular Oncology, EarlyView.This is one of very few studies that used prediagnostic blood samples from participants of two large population‐based cohorts. We identified, evaluated, and validated an innovative protein marker model that outperformed an established risk prediction model and criteria employed by low‐dose computed tomography in lung cancer screening trials.
Megha Bhardwaj +4 more
wiley +1 more source