Applying interpretable machine learning in computational biology—pitfalls, recommendations and opportunities for new developments
Recent advances in machine learning have enabled the development of next-generation
predictive models for complex computational biology problems, thereby spurring the use of …
predictive models for complex computational biology problems, thereby spurring the use of …
Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data
Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims
to unravel the complex relationships between genes and their regulators. Deciphering these …
to unravel the complex relationships between genes and their regulators. Deciphering these …
Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis
Background Feature selection is an essential task in single-cell RNA-seq (scRNA-seq) data
analysis and can be critical for gene dimension reduction and downstream analyses, such …
analysis and can be critical for gene dimension reduction and downstream analyses, such …
scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis
W Lan, T Ling, Q Chen, R Zheng, M Li… - PLOS Computational …, 2024 - journals.plos.org
With the rapidly development of biotechnology, it is now possible to obtain single-cell multi-
omics data in the same cell. However, how to integrate and analyze these single-cell multi …
omics data in the same cell. However, how to integrate and analyze these single-cell multi …
scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data
W Li, B ** and selecting plant varieties with improved agronomic
traits. Modern molecular techniques, such as genome editing, enable more efficient …
traits. Modern molecular techniques, such as genome editing, enable more efficient …
scDRMAE: integrating masked autoencoder with residual attention networks to leverage omics feature dependencies for accurate cell clustering
T Zhang, H Zhang, J Ren, Z Wu, Z Zhao… - Bioinformatics, 2024 - academic.oup.com
Motivation Cell clustering is foundational for analyzing the heterogeneity of biological
tissues using single-cell sequencing data. With the maturation of single-cell multi-omics …
tissues using single-cell sequencing data. With the maturation of single-cell multi-omics …
A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches
The rapid advancement of high-throughput sequencing and other assay technologies has
resulted in the generation of large and complex multi-omics datasets, offering …
resulted in the generation of large and complex multi-omics datasets, offering …