ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics

Zhang, Ying; Sun, Huaicheng; Lian, Xichen; Tang, Jing; Zhu, Feng

doi:10.1002/advs.202207061

Cited by 12 publications

(4 citation statements)

References 105 publications

(214 reference statements)

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“…Overall, a total of 1.87 million ncRNA–target interaction data were collected and included in RNAenrich. These data are rich resources that are waiting for machine-learning tools ( Chen et al 2018b , Li et al 2020 , Meyer et al 2020 , Liu et al 2021 , Wang et al 2021b , Hu et al 2022a , Xia et al 2022a , b , Li et al 2023 ) to analyze, especially feature selection ( Chen et al 2018c , 2020 , 2021b , Hu et al 2021 , 2022b , Too et al 2022 , Zhang et al 2023 ) methods have great potential in this scenario.…”

Section: Methodsmentioning

confidence: 99%

RNAenrich: a web server for non-coding RNA enrichment

et al. 2023

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Motivation With the rapid advances of RNA sequencing and microarray technologies in non-coding RNA (ncRNA) research, functional tools that perform enrichment analysis for ncRNAs are needed. On the one hand, because of the rapidly growing interest in circRNAs, snoRNAs, and piRNAs, it is essential to develop tools for enrichment analysis for these newly emerged ncRNAs. On the other hand, due to the key role of ncRNAs’ interacting target in the determination of their function, the interactions between ncRNA and its corresponding target should be fully considered in functional enrichment. Based on the ncRNA-mRNA/protein-function strategy, some tools have been developed to functionally analyze a single type of ncRNA (the majority focuses on miRNA); in addition, some tools adopt predicted target data and lead to only low confidence results. Results Herein, an online tool named RNAenrich was developed to enable the comprehensive and accurate enrichment analysis of ncRNAs. It is unique in (1) realizing the enrichment analysis for various RNA types in humans and mice, such as miRNA, lncRNA, circRNA, snoRNA, piRNA and mRNA; (2) extending the analysis by introducing millions of experimentally validated data of RNA-target interactions as a built-in database; and (3) providing a comprehensive interacting network among various ncRNAs and targets to facilitate the mechanistic study of ncRNA function. Importantly, RNAenrich led to a more comprehensive and accurate enrichment analysis in a COVID-19-related miRNA case, which was largely attributed to its coverage of comprehensive ncRNA-target interactions. Availability RNAenrich is now freely accessible at https://idrblab.org/rnaenr/ Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

RNAenrich: a web server for non-coding RNA enrichment

et al. 2023

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“…However, early integration encounters two primary challenges in its application: (1) The raw high-dimensional data generated by concatenating all omics data is intricate, noisy, and redundant, leading to challenging learning processes and suboptimal model performance. , Existing methods , often employ feature selection algorithms to reduce the complexity of the composite matrix, which results in information loss as certain useful information is filtered out during the selection process . (2) Another challenge lies in the fact that sequential high-dimensional multiomics vectors can hardly reflect the intrinsic correlations of omics-features from the representational level .…”

Section: Introductionmentioning

confidence: 99%

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery

Zhang,

Mou,

et al. 2024

J. Chem. Inf. Model.

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In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.

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“…To date, various methods have been used for constructing classification models in multiclass metabolomics, such as random forests. , Due to the differences in the principles of these classification methods, conflicting outcomes are observed when different classification methods are applied, even for the same data set. , Therefore, it is highly necessary to apply an appropriate classification method with superior performance for a specific data set. To select the most suitable method, the performance of all classification methods must be assessed using well-established criteria. , Apart from classification methods, methods of identifying metabolic markers are also of importance for the performance of the classification model in multiclass metabolomics. , …”

Section: Introductionmentioning

confidence: 99%

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Yang,

Chen,

Jiang

et al. 2024

Anal. Chem.

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Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/ multiclassmetabo/.

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ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics

Cited by 12 publications

References 105 publications

RNAenrich: a web server for non-coding RNA enrichment

RNAenrich: a web server for non-coding RNA enrichment

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

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