ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning
Motivation Recently, peptides have emerged as a promising class of pharmaceuticals for
various diseases treatment poised between traditional small molecule drugs and therapeutic …
various diseases treatment poised between traditional small molecule drugs and therapeutic …
ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism
Motivation: Peptides have recently emerged as promising therapeutic agents against
various diseases. For both research and safety regulation purposes, it is of high importance …
various diseases. For both research and safety regulation purposes, it is of high importance …
Mechanism-centric approaches for biomarker detection and precision therapeutics in cancer
CY Yu, A Mitrofanova - Frontiers in Genetics, 2021 - frontiersin.org
Biomarker discovery is at the heart of personalized treatment planning and cancer precision
therapeutics, encompassing disease classification and prognosis, prediction of treatment …
therapeutics, encompassing disease classification and prognosis, prediction of treatment …
Multi-omics clustering for cancer subtyping based on latent subspace learning
The increased availability of high-throughput technologies has enabled biomedical
researchers to learn about disease etiology across multiple omics layers, which shows …
researchers to learn about disease etiology across multiple omics layers, which shows …
Cancer subtype identification by multi-omics clustering based on interpretable feature and latent subspace learning
In recent years, multi-omics clustering has become a powerful tool in cancer research,
offering a comprehensive perspective on the diverse molecular characteristics inherent to …
offering a comprehensive perspective on the diverse molecular characteristics inherent to …
Interactive gene identification for cancer subtyping based on multi-omics clustering
Recent advances in multi-omics databases offer the opportunity to explore complex systems
of cancers across hierarchical biological levels. Some methods have been proposed to …
of cancers across hierarchical biological levels. Some methods have been proposed to …
Single-cell differential network analysis with sparse Bayesian factor models
M Sekula, J Gaskins, S Datta - Frontiers in Genetics, 2022 - frontiersin.org
Differential network analysis plays an important role in learning how gene interactions
change under different biological conditions, and the high resolution of single-cell RNA …
change under different biological conditions, and the high resolution of single-cell RNA …
Spectral clustering joint deep embedding learning by autoencoder
Spectral clustering has become one of the most popular clustering methods due to its
superior performance compared to the traditional clustering methods. However, the …
superior performance compared to the traditional clustering methods. However, the …
Distortion-free PCA on sample space for highly variable gene detection from single-cell RNA-seq data
Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which
enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation …
enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation …
Selecting interpretable features for cancer subtyping on multi-omics data
Analyzing multi-omics data is powerful in cancer research, offering essential insights for
identifying distinct cancer subtypes. Due to the significant noise and redundancy in multi …
identifying distinct cancer subtypes. Due to the significant noise and redundancy in multi …