The Protein Structure Prediction Module of the Prot-Grid Information System
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Summary
This work describes the protein secondary structure prediction module of a distributed bio-informatic system containing protein sequencing information and compares the results of a single classifier system based on SVMs, as well as with the version of an SVM based adaBoost algorithm and a novel fuzzy multi-SVMclassi-�er.
- Type
- article
- Published
- 2003-01-01
- Cited by
- 2
- References
- 20
- Access
- Open access
- OpenAlex
- https://openalex.org/W43900638
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2746361
Keywords
Computer science, Support vector machine, AdaBoost, Data mining, Key (lock)
References
- Multiple Classifier Systems
- Soft Margins for AdaBoost
- Prediction of protein secondary structure at better than 70% accuracy.
- Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments.
- 3Dee: a database of protein structural domains
- Evaluation and improvement of multiple sequence methods for protein secondary structure prediction
- The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks
- Pattern Recognition with Fuzzy Objective Function Algorithms
- Boosted Mixture of Experts: An Ensemble Learning Scheme
- Predicting the secondary structure of globular proteins using neural network models.
- A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach.
- Knowledge‐based protein secondary structure assignment
- An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
- Identification and application of the concepts important for accurate and reliable protein secondary structure prediction
- Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships.
- A Multi-SVM Classification System
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