PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods

Xia, Weiqi; Zheng, Lingyan; Fang, Jiebin; Li, Fengcheng; Zhou, Ying; Zeng, Zhenyu; Zhang, Bing; Li, Zhaorong; Li, Honglin; Zhu, Feng

doi:10.1016/j.compbiomed.2022.105465

Cited by 50 publications

(24 citation statements)

References 109 publications

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“…Machine Learning models can be instrumental also in the analysis of simulation results . One prominent application in the macromolecular domain is the analysis of protein conformations from molecular simulations, , and the use of unsupervised ML methods, such as Principal Component Analysis, for dimensionality reduction and the extraction of the “essential coordinates” that can describe the motion of the macromolecule . Increasing effort is also being devoted to the design interpretable ML frameworks rather than “black box” ones.…”

Section: Intersections Between Polymer Informatics Molecular Simulati...mentioning

confidence: 99%

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Ricci

Vergadou

2023

J. Phys. Chem. B

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Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.

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Section: Intersections Between Polymer Informatics Molecular Simulati...mentioning

confidence: 99%

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Ricci

Vergadou

2023

J. Phys. Chem. B

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“…DL methods use interconnected layers of nonlinear transformation units to learn from data without labor-intensive feature engineering. , DL models such as deep neural network (DNN) and CNN have been employed to perform cell segmentation, image representation and protein localization prediction, and their principles were described elsewhere. ,,,− Different from supervised learning, self-supervised learning is a mode that generates labels from data itself for training. AE is a type of semi-supervised learning method which embeds high-dimensional data into a low-dimensional latent space while preserving the original information on inputs .…”

Section: How ML Is Integrated Into Spatial Proteomicsmentioning

confidence: 99%

Application of Machine Learning in Spatial Proteomics

Mou

Pan

et al. 2022

J. Chem. Inf. Model.

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Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

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“…In metabolomics, the detection of multiclass biosamples is often required for disease diagnoses and clinical applications . There are an increasing number of multiclass ( N > 2) problems analyzed using metabolomics. , For example, a multiclass metabolomic study has been applied to reveal the level of bile acids in different cancerous sites, differentiate the presence of succinate in diverse adipose tissues, and discover variations in amino acids of different cell lines .…”

Section: Introductionmentioning

confidence: 99%

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

2023

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Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.

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PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods

Cited by 50 publications

References 109 publications

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Application of Machine Learning in Spatial Proteomics

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

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