ArticleScientific reports2015
Predicting cancerlectins by the optimal g-gap dipeptides.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Protein Representation in Metric Spaces for Protein Druggability Prediction: A Case Study on Aspirin.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Extended dipeptide composition framework for accurate identification of anticancer peptides.Scientific reports · 2024Article
- NeuroCNN_GNB: an ensemble model to predict neuropeptides based on a convolution neural network and Gaussian naive Bayes.Frontiers in genetics · 2023Article
- Article
- Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer.Journal of cellular and molecular medicine · 2020Article
- Identification of Cancerlectins Using Support Vector Machines With Fusion of G-Gap Dipeptide.Frontiers in genetics · 2020Article
- A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features.Frontiers in bioengineering and biotechnology · 2019Article
- Predicting Ion Channels Genes and Their Types With Machine Learning Techniques.Frontiers in genetics · 2019Article
- Identification of Bacteriophage Virion Proteins Using Multinomial Naïve Bayes with g-Gap Feature Tree.International journal of molecular sciences · 2018Article
- iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites.Molecular therapy. Nucleic acids · 2018Article
- A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique.BioMed research international · 2018Article
- PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.Frontiers in microbiology · 2018Article
- iRSpot-Pse6NC: Identifying recombination spots inInternational journal of biological sciences · 2018Article
- HBPred: a tool to identify growth hormone-binding proteins.International journal of biological sciences · 2018Article
- Classifying Included and Excluded Exons in Exon Skipping Event Using Histone Modifications.Frontiers in genetics · 2018Article
- SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides.BMC genomics · 2017Article
- IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types.International journal of molecular sciences · 2017Article
- Sequence-based predictive modeling to identify cancerlectins.Oncotarget · 2017Article
- Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.BioMed research international · 2017Article
- Identifying anticancer peptides by using improved hybrid compositions.Scientific reports · 2016Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The cancerlectin plays a key role in the process of tumor cell differentiation. Thus, to fully understand the function of cancerlectin is significant because it sheds light on the future direction for the cancer therapy. However, the traditional wet-experimental methods were money- and time-consuming. It is highly desirable to develop an effective and efficient computational tool to identify cancerlectins. In this study, we developed a sequence-based method to discriminate between cancerlectins and non-cancerlectins. The analysis of variance (ANOVA) was used to choose the optimal feature set derived from the g-gap dipeptide composition. The jackknife cross-validated results showed that the proposed method achieved the accuracy of 75.19%, which is superior to other published methods. For the convenience of other researchers, an online web-server CaLecPred was established and can be freely accessed from the website http://lin.uestc.edu.cn/server/CalecPred. We believe that the CaLecPred is a powerful tool to study cancerlectins and to guide the related experimental validations.
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