ArticleBMC biology2024
Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data.
Article in BMC biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 40 papers.
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40 citing papers in PubMed.
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- HMA-GCA: hybrid manifold augmentation and gated cross-attention for circRNA-miRNA interaction prediction.Bioinformatics (Oxford, England) · 2026Article
- EssTFNet: integration of adaptive time-frequency and DNA language models for interpretable human essential gene prediction.Briefings in bioinformatics · 2026Article
- Comprehensive identification of three key genes and three key DNA methylation CpG sites associated with prognosis and prediction in colon adenocarcinoma.Discover oncology · 2026Article
- SpaMFG: a spatial multi-omics integration method based on feature grouping.Bioinformatics (Oxford, England) · 2026Article
- AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA-drug sensitivity association prediction.BMC biology · 2026Article
- MVFAN-Kcr: A Multi-View Feature Fusion and Attention-Based Network for Lysine Crotonylation Site Identification.Interdisciplinary sciences, computational life sciences · 2026Article
- KmalPred: a deep learning framework for lysine malonylation site prediction using protein language model representations.BMC biology · 2026Article
- CataCon: a contrastive graph representation learning framework for catalyst prediction.Journal of cheminformatics · 2026Article
- ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026Review
- MsgaBpred: A B-cell epitope predictor integrating AlphaFold3-predicted structures with multi-scale GCNs and pre-trained language model ESM-C.PLoS computational biology · 2026Article
- TF-loop: deciphering the transcription factor regulatory language for CTCF-mediated chromatin loop based on BERT.Briefings in bioinformatics · 2026Article
- CFGSCDSA: Predicting circRNA-drug sensitivity associations based on collaborative feature learning and graph structure learning.PLoS computational biology · 2026Article
- PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.PLoS computational biology · 2026Article
- DeepSGE: predicting spatial gene expression using residual network with efficient channel attention and dynamic graph attention network.BMC genomics · 2026Article
- UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction.Genome biology · 2026Article
- FKSUDDAPre: A drug-disease association prediction framework based on F-TEST feature selection and AMDKSU resampling with interpretability analysis.PLoS computational biology · 2026Article
- Aegis: a transformer-based deep learning framework for the accurate identification of anticancer peptides.BMC biology · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA velocity, as an extension of trajectory inference, is an effective method for understanding cell development using single-cell RNA sequencing (scRNA-seq) experiments. However, existing RNA velocity methods are limited by the batch effect because they cannot directly correct for batch effects in the input data, which comprises spliced and unspliced matrices in a proportional relationship. This limitation can lead to an incorrect velocity stream. This paper introduces VeloVGI, which addresses this issue innovatively in two key ways. Firstly, it employs an optimal transport (OT) and mutual nearest neighbor (MNN) approach to construct neighbors in batch data. This strategy overcomes the limitations of existing methods that are affected by the batch effect. Secondly, VeloVGI improves upon VeloVI's velocity estimation by incorporating the graph structure into the encoder for more effective feature extraction. The effectiveness of VeloVGI is demonstrated in various scenarios, including the mouse spinal cord and olfactory bulb tissue, as well as on several public datasets. The results show that VeloVGI outperformed other methods in terms of metric performance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.