Results 41 to 50 of about 106,507 (280)

Organ‐specific redox imbalances in spinal muscular atrophy mice are partially rescued by SMN antisense oligonucleotides

FEBS Letters, EarlyView.
We identified a systemic, progressive loss of protein S‐glutathionylation—detected by nonreducing western blotting—alongside dysregulation of glutathione‐cycle enzymes in both neuronal and peripheral tissues of Taiwanese SMA mice. These alterations were partially rescued by SMN antisense oligonucleotide therapy, revealing persistent redox imbalance as ...
Sofia Vrettou, Brunhilde Wirth
wiley   +1 more source

Research on intrusion detection method of marine meteorological sensor network based on anomalous behaviors

Tongxin xuebao, 2023
To deal with the abnormal data flow attacks faced by the marine meteorological sensor network (MMSN), analyze the security mechanism, and aim at the complex and huge network structure and the extremely imbalanced data flow in the nodes, the intrusion ...
Xin SU, Tian TIAN, Gong Ziyang, Yiqing ZHOU   +3 more
doaj   +2 more sources

A weighted pattern matching approach for classification of imbalanced data with a fireworks-based algorithm for feature selection

Connection Science, 2019
Learning a classifier from imbalanced data is a challenging problem in Machine learning. A dataset is said to be imbalanced when the number of instances belonging to one class is much less than the number of instances belonging to the other class ...
N. K. Sreeja
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

RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification

Journal of King Saud University: Computer and Information Sciences, 2022
Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot of the real-world data sets are naturally imbalanced. So, imbalanced classification is a serious problem in machine learning.
Ahmed Arafa, Nawal El-Fishawy, Mohammed Badawy, Marwa Radad   +3 more
doaj   +1 more source

Network divergence analysis identifies adaptive gene modules and two orthogonal vulnerability axes in pancreatic cancer

Molecular Oncology, EarlyView.
Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson, Lyanne Delgado‐Coka, Natalia Marchenko, Luisa F. Escobar‐Hoyos, Kenneth R. Shroyer, Alisa Yurovsky, Trey Ideker, Gábor Balázsi, Thomas MacCarthy, Scott Powers   +9 more
wiley   +1 more source

Assessing model performance in Alzheimer's disease classification: The impact of data imbalance on fine-tuned vision transformers and CNN architectures

Journal of Intelligent Systems
Problem: Data imbalance in medical datasets poses significant challenges for the performance of machine learning models, particularly in classifying Alzheimer’s disease (AD).
Almalki Hassan, Khadidos Alaa O., Alhebaishi Nawaf   +2 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

Large-Scale Visual Relationship Understanding

, 2019
Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are
Elgammal, Ahmed, Elhoseiny, Mohamed, Kalantidis, Yannis, Paluri, Manohar, Rohrbach, Marcus, Zhang, Ji   +5 more
core   +1 more source

Is Diabetic Retinopathy Grading Biased by Imbalanced Datasets?

, 2022
Diabetic retinopathy (DR) is one of the most severe complications of diabetes and the leading cause of vision loss and even blindness. Retinal screening contributes to early detection and treatment of diabetic retinopathy. This eye disease has five stages, namely normal, mild, moderate, severe and proliferative diabetic retinopathy.
Monteiro, Fernando C., Rufino, José
openaire   +3 more sources