ArticleInternational journal of genomics2016
Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.
Article in International journal of genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8Frontiers in genetics · 2022Article
- Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes.Frontiers in genetics · 2021Article
- Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model.Frontiers in bioengineering and biotechnology · 2020Article
- Identification of Cancerlectins Using Support Vector Machines With Fusion of G-Gap Dipeptide.Frontiers in genetics · 2020Article
- ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation.Molecular therapy. Nucleic acids · 2019Article
- Identification of Phage Viral Proteins With Hybrid Sequence Features.Frontiers in microbiology · 2019Article
- Improved Wavelet Threshold for Image De-noising.Frontiers in neuroscience · 2019Article
- OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models.Scientific reports · 2017Article
- Sequence-based predictive modeling to identify cancerlectins.Oncotarget · 2017Article
- Relevance popularity: A term event model based feature selection scheme for text classification.PloS one · 2017Article
- Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data.PloS one · 2017Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.
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Registered trials
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.