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Published on: May 5, 2017
TYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and
Jian Zhang1, Xingchen Liang1, Feng Zhou1
1School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
Abstract:
Cyclins and related cyclin-dependent kinases play vital roles in regulating the progression in the cell cycle. Understanding the intrinsic mechanisms of cyclins promises knowledge about cell uncontrolled proliferation and prevention of cancer cells. Therefore, accurate recognition of cyclins is important for the investigation of tumor cells and biomedical engineering. This study proposes a novel sequence-based predictor named TYLER (predicT cYcLin-dEpendent pRoteins) for addressing the long challenge problem of predicting cyclin-dependent proteins (CDPs). We use information theory to compute selectively enriched CDP-related motifs and build the motif-based model. For those proteins without sharing enriched motifs, we compute sequence-derived features and construct machine learning-based models. We optimize the weights of two different models to build a more accurate predictor. We estimate these two types of models by using 5-fold cross-validations on the TRAINING dataset. We prove that the combination of two models and optimization of the corresponding weights promises decent and robust results on both TRAINING and independent TEST dataset. The empirical test demonstrates that TYLER is robust predictor and statistically significantly better than current methods. The runtime assessment reveals TYLER is a high-throughput effective method. We use TYLER to make predictions on the human proteome, and use the results to hypothesize CDPs. The latest experimental verified CDPs and GO analysis proves that some of our novel predictions shall be potential CDPs. TYLER is implemented as a public user-friendly web server at http://www.inforstation.com/webservers/TYLER/. We share all data and source code that used in this research at https://github.com/biocomputinglab/TYLER.git.
Insights
This study introduces TYLER, a novel predictor for cyclin-dependent proteins (CDPs). TYLER accurately identifies CDPs, aiding cancer research and biomedical engineering by analyzing protein sequences and motifs.
Area of Science:
- Molecular Biology
- Bioinformatics
- Computational Biology
Background:
- Cyclins and cyclin-dependent kinases regulate cell cycle progression.
- Understanding these mechanisms is crucial for cancer research and biomedical applications.
- Accurate identification of cyclin-dependent proteins (CDPs) is vital for studying tumor cells.
Purpose of the Study:
- To develop a novel sequence-based predictor, TYLER, for identifying cyclin-dependent proteins (CDPs).
- To address the challenge of predicting CDPs using computational methods.
- To improve the accuracy and efficiency of CDP prediction for biological research.
Main Methods:
- Utilized information theory to identify enriched CDP-related motifs.
- Developed a motif-based model and machine learning models using sequence-derived features.
- Optimized model weights and employed 5-fold cross-validation for performance estimation.
- Combined motif-based and machine learning approaches for enhanced prediction accuracy.
Main Results:
- TYLER demonstrated robust and statistically significant performance compared to existing methods.
- The predictor achieved decent and robust results on both training and independent test datasets.
- Runtime assessment confirmed TYLER as a high-throughput and effective method.
- Predictions on the human proteome identified potential novel CDPs, supported by GO analysis.
Conclusions:
- TYLER is a robust and accurate predictor for cyclin-dependent proteins (CDPs).
- The method offers a high-throughput solution for CDP identification, benefiting cancer research.
- Novel predictions suggest potential new CDPs, warranting further experimental validation.
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