ArticleOncotarget2017
Sequence-based predictive modeling to identify cancerlectins.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- IDDLncLoc: Subcellular Localization of LncRNAs Based on a Framework for Imbalanced Data Distributions.Interdisciplinary sciences, computational life sciences · 2022Article
- A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.Frontiers in genetics · 2022Article
- mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net.BMC bioinformatics · 2021Article
- LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion.International journal of molecular sciences · 2020Article
- miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides.Scientific reports · 2020Article
- Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer.Journal of cellular and molecular medicine · 2020Article
- PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron.Computational and mathematical methods in medicine · 2020Article
- Identification of Cancerlectins Using Support Vector Machines With Fusion of G-Gap Dipeptide.Frontiers in genetics · 2020Article
- iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC.PloS one · 2020Article
- A Linear Regression Predictor for Identifying NMolecular therapy. Nucleic acids · 2019Article
- SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome.Molecular therapy. Nucleic acids · 2019Article
- Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation.Molecular therapy. Nucleic acids · 2019Article
- Identification of D Modification Sites by Integrating Heterogeneous Features inMolecules (Basel, Switzerland) · 2019Article
- Predicting Ion Channels Genes and Their Types With Machine Learning Techniques.Frontiers in genetics · 2019Article
- An Efficient Classifier for Alzheimer's Disease Genes Identification.Molecules (Basel, Switzerland) · 2018Article
- M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning.Molecular therapy. Nucleic acids · 2018Article
- Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods.Molecules (Basel, Switzerland) · 2018Article
- RFAmyloid: A Web Server for Predicting Amyloid Proteins.International journal of molecular sciences · 2018Article
- SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins.International journal of molecular sciences · 2018Article
- PrESOgenesis: A two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lectins are a diverse type of glycoproteins or carbohydrate-binding proteins that have a wide distribution to various species. They can specially identify and exclusively bind to a certain kind of saccharide groups. Cancerlectins are a group of lectins that are closely related to cancer and play a major role in the initiation, survival, growth, metastasis and spread of tumor. Several computational methods have emerged to discriminate cancerlectins from non-cancerlectins, which promote the study on pathogenic mechanisms and clinical treatment of cancer. However, the predictive accuracies of most of these techniques are very limited. In this work, by constructing a benchmark dataset based on the CancerLectinDB database, a new amino acid sequence-based strategy for feature description was developed, and then the binomial distribution was applied to screen the optimal feature set. Ultimately, an SVM-based predictor was performed to distinguish cancerlectins from non-cancerlectins, and achieved an accuracy of 77.48% with AUC of 85.52% in jackknife cross-validation. The results revealed that our prediction model could perform better comparing with published predictive tools.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.