ArticleComputational and mathematical methods in medicine2021
iMPT-FDNPL: Identification of Membrane Protein Types with Functional Domains and a Natural Language Processing Approach.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed.
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- Spinal Cord Injury Affects Gene Expression of Transmembrane Proteins in Tissue and Release of Extracellular Vesicle in Blood: In Silico andCell journal · 2023Article
- Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications.Computational and structural biotechnology journal · 2023Review
- Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods.Frontiers in genetics · 2023Article
- Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods.Biomolecules · 2022Article
- Screening gene signatures for clinical response subtypes of lung transplantation.Molecular genetics and genomics : MGG · 2022Article
- Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method.Life (Basel, Switzerland) · 2022Article
- Detecting Blood Methylation Signatures in Response to Childhood Cancer Radiotherapy via Machine Learning Methods.Biology · 2022Article
- Identification of Cell Markers and Their Expression Patterns in Skin Based on Single-Cell RNA-Sequencing Profiles.Life (Basel, Switzerland) · 2022Article
- Predicting Heart Cell Types by Using Transcriptome Profiles and a Machine Learning Method.Life (Basel, Switzerland) · 2022Article
- Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method.Frontiers in molecular biosciences · 2022Article
- Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods.BioMed research international · 2022Article
- Identification of methylation signatures associated with CAR T cell in B-cell acute lymphoblastic leukemia and non-hodgkin's lymphoma.Frontiers in oncology · 2022Article
- Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods.Frontiers in microbiology · 2022Article
- Characterization of spleen and lymph node cell typesFrontiers in molecular neuroscience · 2022Article
- Identification of COVID-19 severity biomarkers based on feature selection on single-cell RNA-Seq data of CD8Frontiers in genetics · 2022Article
- Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer's Disease.Frontiers in neuroscience · 2022Article
- Identification of cortical interneuron cell markers in mouse embryos based on machine learning analysis of single-cell transcriptomics.Frontiers in neuroscience · 2022Article
- Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions.BioMed research international · 2022Article
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3 authors.
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Abstract
Membrane protein is an important kind of proteins. It plays essential roles in several cellular processes. Based on the intramolecular arrangements and positions in a cell, membrane proteins can be divided into several types. It is reported that the types of a membrane protein are highly related to its functions. Determination of membrane protein types is a hot topic in recent years. A plenty of computational methods have been proposed so far. Some of them used functional domain information to encode proteins. However, this procedure was still crude. In this study, we designed a novel feature extraction scheme to obtain informative features of proteins from their functional domain information. Such scheme termed domains as words and proteins, represented by its domains, as sentences. The natural language processing approach, word2vector, was applied to access the features of domains, which were further refined to protein features. Based on these features, RAndom k-labELsets with random forest as the base classifier was employed to build the multilabel classifier, namely, iMPT-FDNPL. The tenfold cross-validation results indicated the good performance of such classifier. Furthermore, such classifier was superior to other classifiers based on features derived from functional domains via one-hot scheme or derived from other properties of proteins, suggesting the effectiveness of protein features generated by the proposed scheme.
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