NDLI: MIC_Locator: a novel image-based protein subcellular location multi-label prediction model based on multi-scale monogenic signal representation and intensity encoding strategy

Content Provider	Springer Nature : BioMed Central
Author	Yang, Fan Liu, Yang Wang, Yanbin Yin, Zhijian Yang, Zhen
Abstract	Background Protein subcellular localization plays a crucial role in understanding cell function. Proteins need to be in the right place at the right time, and combine with the corresponding molecules to fulfill their functions. Furthermore, prediction of protein subcellular location not only should be a guiding role in drug design and development due to potential molecular targets but also be an essential role in genome annotation. Taking the current status of image-based protein subcellular localization as an example, there are three common drawbacks, i.e., obsolete datasets without updating label information, stereotypical feature descriptor on spatial domain or grey level, and single-function prediction algorithm’s limited capacity of handling single-label database. Results In this paper, a novel human protein subcellular localization prediction model MIC_Locator is proposed. Firstly, the latest datasets are collected and collated as our benchmark dataset instead of obsolete data while training prediction model. Secondly, Fourier transformation, Riesz transformation, Log-Gabor filter and intensity coding strategy are employed to obtain frequency feature based on three components of monogenic signal with different frequency scales. Thirdly, a chained prediction model is proposed to handle multi-label instead of single-label datasets. The experiment results showed that the MIC_Locator can achieve 60.56% subset accuracy and outperform the existing majority of prediction models, and the frequency feature and intensity coding strategy can be conducive to improving the classification accuracy. Conclusions Our results demonstrate that the frequency feature is more beneficial for improving the performance of model compared to features extracted from spatial domain, and the MIC_Locator proposed in this paper can speed up validation of protein annotation, knowledge of protein function and proteomics research.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-019-3136-3.pdf
Ending Page	21
Page Count	21
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-019-3136-3
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	20
Language	English
Publisher	BioMed Central
Publisher Date	2019-10-26
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Bioimage informatics Protein subcellular localization Frequency domain feature Monogenic signal Image intensity encoding strategy Multi-label classifier chain Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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