ArticleComputational and mathematical methods in medicine2020
A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
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- A First Computational Frame for Recognizing Heparin-Binding Protein.Diagnostics (Basel, Switzerland) · 2023Article
- Stack-VTP: prediction of vesicle transport proteins based on stacked ensemble classifier and evolutionary information.BMC bioinformatics · 2023Article
- Bitter-RF: A random forest machine model for recognizing bitter peptides.Frontiers in medicine · 2023Article
- Computational prediction of promotors inFrontiers in microbiology · 2023Article
- Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.Frontiers in genetics · 2023Article
- VARIDT 2.0: structural variability of drug transporter.Nucleic acids research · 2022Article
- Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM.Computational and mathematical methods in medicine · 2022Article
- Identification of Vesicle Transport ProteinsFrontiers in genetics · 2022Article
- MoRF-FUNCpred: Molecular Recognition Feature Function Prediction Based on Multi-Label Learning and Ensemble Learning.Frontiers in pharmacology · 2022Review
- ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition.BMC bioinformatics · 2021Article
- iTTCA-RF: a random forest predictor for tumor T cell antigens.Journal of translational medicine · 2021Article
- Immunoglobulin Classification Based on FC* and GC* Features.Frontiers in genetics · 2021Article
- A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem.Frontiers in genetics · 2021Article
- Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm.Frontiers in genetics · 2021Review
- Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network.Frontiers in cell and developmental biology · 2021Article
- Application of Multilayer Network Models in Bioinformatics.Frontiers in genetics · 2021Review
- Application of Machine Learning for Drug-Target Interaction Prediction.Frontiers in genetics · 2021Review
- FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction.Computational and structural biotechnology journal · 2021Article
- Identification of cyclin protein using gradient boost decision tree algorithm.Computational and structural biotechnology journal · 2021Article
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Authors and funding
4 authors.
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Abstract
With the development of computer technology, many machine learning algorithms have been applied to the field of biology, forming the discipline of bioinformatics. Protein function prediction is a classic research topic in this subject area. Though many scholars have made achievements in identifying protein by different algorithms, they often extract a large number of feature types and use very complex classification methods to obtain little improvement in the classification effect, and this process is very time-consuming. In this research, we attempt to utilize as few features as possible to classify vesicular transportation proteins and to simultaneously obtain a comparative satisfactory classification result. We adopt CTDC which is a submethod of the method of composition, transition, and distribution (CTD) to extract only 39 features from each sequence, and LibSVM is used as the classification method. We use the SMOTE method to deal with the problem of dataset imbalance. There are 11619 protein sequences in our dataset. We selected 4428 sequences to train our classification model and selected other 1832 sequences from our dataset to test the classification effect and finally achieved an accuracy of 71.77%. After dimension reduction by MRMD, the accuracy is 72.16%.
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