ArticleFrontiers in genetics2020
Predicting ATP-Binding Cassette Transporters Using the Random Forest Method.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Genomic structure of yellow lupin (Lupinus luteus): genome organization, evolution, gene family expansion, metabolites and protein synthesis.BMC genomics · 2025Article
- Structural and biochemical insights of xylose MFS and SWEET transporters in microbial cell factories: challenges to lignocellulosic hydrolysates fermentation.Frontiers in microbiology · 2024Review
- Aptamers Targeting Membrane Proteins for Sensor and Diagnostic Applications.Molecules (Basel, Switzerland) · 2023Review
- Impact of Non-Coding RNAs on Chemotherapeutic Resistance in Oral Cancer.Biomolecules · 2022Review
- A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.Frontiers in genetics · 2022Article
- Significance of a PTEN Mutational Status-Associated Gene Signature in the Progression and Prognosis of Endometrial Carcinoma.Oxidative medicine and cellular longevity · 2022Article
- Prediction of prokaryotic transposases from protein features with machine learning approaches.Microbial genomics · 2021Article
- DeepRTCP: Predicting ATP-Binding Cassette Transporters Based on 1-Dimensional Convolutional Network.Frontiers in cell and developmental biology · 2020Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
ATP-binding cassette (ABC) proteins play important roles in a wide variety of species. These proteins are involved in absorbing nutrients, exporting toxic substances, and regulating potassium channels, and they contribute to drug resistance in cancer cells. Therefore, the identification of ABC transporters is an urgent task. The present study used 188D as the feature extraction method, which is based on sequence information and physicochemical properties. We also visualized the feature extracted by t-Distributed Stochastic Neighbor Embedding (t-SNE). The sample based on the features extracted by 188D may be separated. Further, random forest (RF) is an efficient classifier to identify proteins. Under the 10-fold cross-validation of the model proposed here for a training set, the average accuracy rate of 10 training sets was 89.54%. We obtained values of 0.87 for specificity, 0.92 for sensitivity, and 0.79 for MCC. In the testing set, the accuracy achieved was 89%. These results suggest that the model combining 188D with RF is an optimal tool to identify ABC transporters.
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Registered trials
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