The rise of nonnegative matrix factorization: algorithms and applications
YT Guo, QQ Li, CS Liang - Information Systems, 2024 - Elsevier
Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …
methods result in misleading results and waste of computing resources due to lack of timely …
Network learning for biomarker discovery
Everything is connected and thus networks are instrumental in not only modeling complex
systems with many components, but also accommodating knowledge about their …
systems with many components, but also accommodating knowledge about their …
DAESTB: inferring associations of small molecule–miRNA via a scalable tree boosting model based on deep autoencoder
MicroRNAs (miRNAs) are closely related to a variety of human diseases, not only regulating
gene expression, but also having an important role in human life activities and being viable …
gene expression, but also having an important role in human life activities and being viable …
Benchmarking of computational methods for predicting circRNA-disease associations
Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in
human diseases. Identification of circRNA-disease associations can help for the diagnosis of …
human diseases. Identification of circRNA-disease associations can help for the diagnosis of …
Deciphering ligand–receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic …
Background: Cell–cell communication in a tumor microenvironment is vital to tumorigenesis,
tumor progression and therapy. Intercellular communication inference helps understand …
tumor progression and therapy. Intercellular communication inference helps understand …
MPCLCDA: predicting circRNA–disease associations by using automatically selected meta-path and contrastive learning
W Liu, T Tang, X Lu, X Fu, Y Yang… - Briefings in …, 2023 - academic.oup.com
Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying
the associations between human diseases and circRNA can help in disease prevention …
the associations between human diseases and circRNA can help in disease prevention …
Predicting CircRNA-disease associations via feature convolution learning with heterogeneous graph attention network
L Peng, C Yang, Y Chen, W Liu - IEEE Journal of Biomedical …, 2023 - ieeexplore.ieee.org
Exploring the relationship between circular RNA (circRNA) and disease is beneficial for
revealing the mechanisms of disease pathogenesis. However, a blind search for all possible …
revealing the mechanisms of disease pathogenesis. However, a blind search for all possible …
scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network
J Wang, J Xia, H Wang, Y Su… - Briefings in …, 2023 - academic.oup.com
The advances in single-cell ribonucleic acid sequencing (scRNA-seq) allow researchers to
explore cellular heterogeneity and human diseases at cell resolution. Cell clustering is a …
explore cellular heterogeneity and human diseases at cell resolution. Cell clustering is a …
Predicting lncRNA–disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation
GB Xie, RB Chen, ZY Lin, GS Gu, JR Yu… - Briefings in …, 2023 - academic.oup.com
Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to
several human diseases, providing new opportunities for their use in detection and therapy …
several human diseases, providing new opportunities for their use in detection and therapy …
A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation
Abstract Background Circular RNAs (circRNAs) have been confirmed to play a vital role in
the occurrence and development of diseases. Exploring the relationship between circRNAs …
the occurrence and development of diseases. Exploring the relationship between circRNAs …