ArticleBMC systems biology2016
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.
Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.
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79 citing papers in PubMed, 158 citations in OpenAlex.
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- A computational model for GPCR-ligand interaction prediction.Journal of integrative bioinformatics · 2020Article
- Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information.Molecular therapy. Nucleic acids · 2020Article
- Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian.Scientific reports · 2020Article
- Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review.Medical & biological engineering & computing · 2020Review
- Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning.Briefings in bioinformatics · 2020Article
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19 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only on amino acid sequence has significant value. TATA-binding protein (TBP) is a kind of DNA binding protein, which plays a key role in the transcription regulation. Our study proposed an automatic approach for identifying TATA-binding proteins efficiently, accurately, and conveniently. This method would guide for the special protein identification with computational intelligence strategies.
resultsFirstly, we proposed novel fingerprint features for TBP based on pseudo amino acid composition, physicochemical properties, and secondary structure. Secondly, hierarchical features dimensionality reduction strategies were employed to improve the performance furthermore. Currently, Pretata achieves 92.92% TATA-binding protein prediction accuracy, which is better than all other existing methods.
conclusionsThe experiments demonstrate that our method could greatly improve the prediction accuracy and speed, thus allowing large-scale NGS data prediction to be practical. A web server is developed to facilitate the other researchers, which can be accessed at http://server.malab.cn/preTata/ .
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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.