Results 41 to 50 of about 24,275,785 (260)

Classification with class imbalance problem: a review [PDF]

, 2015
Most existing classification approaches assume the underlying training set is evenly distributed. In class imbalanced classification, the training set for one class (majority) far surpassed the training set of the other class (minority), in which, the ...
Ali, Aida, Ralescu, Anca L., Shamsuddin, Siti Mariyam   +2 more
core  

Tnc-Net: Automatic Classification for Thyroid Nodules Lesions Using Convolutional Neural Network

IEEE Access
Automatic and accurate classification of thyroid nodules is of great significance to doctors for clinical diagnosis and subsequent treatment recommendations.
Junyang Cao, Yangping Zhu, Xing Tian, Juan Wang   +3 more
doaj   +1 more source

Performance Improvement of Deep Learning Based Multi-Class ECG Classification Model Using Limited Medical Dataset

IEEE Access, 2023
Medical data often exhibit class imbalance, which poses a challenge in classification tasks. To solve this problem, data augmentation techniques are used to balance the data.
Sanghoon Choi, Hyo-Chang Seo, Min Soo Cho, Segyeong Joo, Gi-Byoung Nam   +4 more
doaj   +1 more source

Diversity and complexity in neural organoids

FEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley   +1 more source

Hyperosmotic stress induces PARP1‐mediated HPF1‐dependent mono(ADP‐ribosyl)ation

FEBS Letters, EarlyView.
Sorbitol‐induced hyperosmotic stress rapidly induces reversible mono(ADP‐ribosyl)ation (MARylation) on PARP1 without the signs of genotoxic signaling. We show that PARP1 autoMARylation is HPF1 dependent and forms hydroxylamine‐resistant O‐glycosidic linkages.
Anna Georgina Kopasz, Mihály Mérey, Rebeka Vásárhelyi, Ramóna Pék, Victor Imburchia, László Henn, Adrián Kószó, Nicholas D. Lakin, Ivan Ahel, Sébastien Huet, Ágnes Czibula, Gyula Timinszky   +11 more
wiley   +1 more source

Data for: Role of Allelic Imbalance in Predicting Hepatocellular Carcinoma (HCC) Recurrence Risk After Liver Transplant

, 2020
Raw data for the article: Role of Allelic Imbalance in Predicting Hepatocellular Carcinoma (HCC) Recurrence Risk After Liver Transplant.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE.
ISMETT, D (via Mendeley Data)
core   +1 more source

Support Vector Machine-Based Fault Diagnosis under Data Imbalance with Application to High-Speed Train Electric Traction Systems

Machines
The safety and reliability of high-speed train electric traction systems are crucial. However, the operating environment for China Railway High-speed (CRH) trains is challenging, with severe working conditions. Dataset imbalance further complicates fault
Yunkai Wu, Tianxiang Ji, Yang Zhou, Yijin Zhou   +3 more
doaj   +1 more source

An isoform of 14‐3‐3 protein regulates transbilayer lipid movement at the plasma membrane

FEBS Letters, EarlyView.
Loss of 14‐3‐3ζ in CHO cells confers resistance to exogenous phosphatidylserine (PS) and impairs endocytosis‐independent inward flip‐flop of fluorescent PS at the plasma membrane. RNAi‐mediated knockdown reproduces this defect, while no additive effect is seen in ATP11C‐deficient cells.
Akiko Yamaji‐Hasegawa, Akira Hattori, Masafumi Tsujimoto, Toshihide Kobayashi   +3 more
wiley   +1 more source

Organizing the interface—Plasma membrane architecture and receptor dynamics in virus‐cell interactions

FEBS Letters, EarlyView.
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

Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm [PDF]

, 2010
In classification, when the distribution of the training data among classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes.
Wong, K.W., Fung, C.C., Jeatrakul, P.
core