ArticleScientific reports2016
Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 20 citations in OpenAlex.
- DEPDC7 as a potential tumor suppressor in hepatocellular carcinoma: preliminary evidence for targeting the JAK1/STAT3 axis.Frontiers in oncology · 2026Article
- High Expression of DEPDC1B Predicts Poor Prognosis in Lung Adenocarcinoma.Journal of inflammation research · 2022Article
- Novel Prognostic Signatures of Hepatocellular Carcinoma Based on Metabolic Pathway Phenotypes.Frontiers in oncology · 2022Article
- Construction and investigation of a circRNA-associated ceRNA regulatory network in Tetralogy of Fallot.BMC cardiovascular disorders · 2021Article
- Research on RNA secondary structure predicting via bidirectional recurrent neural network.BMC bioinformatics · 2021Article
- Overexpressed DEPDC1B contributes to the progression of hepatocellular carcinoma by CDK1.Aging · 2021Article
- ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features.Frontiers in cell and developmental biology · 2020Article
- Article
- 70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features.BMC systems biology · 2018Article
- Article
- Predicting Amyloidogenic Proteins in the Proteomes of Plants.International journal of molecular sciences · 2017Article
- OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models.Scientific reports · 2017Article
- Geminivirus data warehouse: a database enriched with machine learning approaches.BMC bioinformatics · 2017Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Dishevelled/EGL-10/Pleckstrin (DEP) domain-containing (DEPDC) proteins have seven members. However, whether this superfamily can be distinguished from other proteins based only on the amino acid sequences, remains unknown. Here, we describe a computational method to segregate DEPDCs and non-DEPDCs. First, we examined the Pfam numbers of the known DEPDCs and used the longest sequences for each Pfam to construct a phylogenetic tree. Subsequently, we extracted 188-dimensional (188D) and 20D features of DEPDCs and non-DEPDCs and classified them with random forest classifier. We also mined the motifs of human DEPDCs to find the related domains. Finally, we designed experimental verification methods of human DEPDC expression at the mRNA level in hepatocellular carcinoma (HCC) and adjacent normal tissues. The phylogenetic analysis showed that the DEPDCs superfamily can be divided into three clusters. Moreover, the 188D and 20D features can both be used to effectively distinguish the two protein types. Motif analysis revealed that the DEP and RhoGAP domain was common in human DEPDCs, human HCC and the adjacent tissues that widely expressed DEPDCs. However, their regulation was not identical. In conclusion, we successfully constructed a binary classifier for DEPDCs and experimentally verified their expression in human HCC tissues.
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