Variations on probabilistic suffix trees: statistical modeling

  and prediction of protein families

Bejerano, Gill; Yona, Golan

doi:10.1093/bioinformatics/17.1.23

Cited by 142 publications

(131 citation statements)

References 45 publications

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“…A major advantage of PST is its capacity of extracting structural information from the sequences under analysis. Recently, an implementation of PST has been successfully used in protein classification [3], even though its performance decreases with less conserved families. Better results have been obtained using mixtures of PST models for sparse sequences [4,5].…”

Section: Introductionmentioning

confidence: 99%

Sequence Motif Identification and Protein Family Classification Using Probabilistic Trees

Leonardi

Galves

2005

Advances in Bioinformatics and Computational Biology

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Abstract. Efficient family classification of newly discovered protein sequences is a central problem in bioinformatics. We present a new algorithm, using Probabilistic Suffix Trees, which identifies equivalences between the amino acids in different positions of a motif for each family. We also show that better classification can be achieved identifying representative fingerprints in the amino acid chains.

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Section: Introductionmentioning

confidence: 99%

Sequence Motif Identification and Protein Family Classification Using Probabilistic Trees

Leonardi

Galves

2005

Advances in Bioinformatics and Computational Biology

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“…The PST method was introduced by Bejerano and Yona to model the protein families [15]. The original PST model was based on identifying significant short segments among the many input sequences, regardless of the relative position of these segments within the different proteins [16].…”

Section: A Generative Approach: Variable Length Markov Chainmentioning

confidence: 99%

Predicting Metal-Binding Sites of Protein Residues

Küçükbay

Ogul

2015

Annals of Computer Science and Information Systems

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Abstract-Metal ions in protein are critical to the function, structure and stability of protein. For this reason accurate prediction of metal binding sites in protein is very important. Here, we present our study which is performed for predicting metal binding sites for histidines (HIS) and cysteines from protein sequence. Three different methods are applied for this task: Support Vector Machine (SVM), Naive Bayes and Variable-length Markov chain. All these methods use only sequence information to classify a residue as metal binding or not. Several feature sets are employed to evaluate impact on prediction results. We predict metal binding sites for mentioned amino acids at 35% precision and 75% recall with Naive Bayes, at 25% precision and 23% recall with Support Vector Machine and at 0.05% precision and 60% recall with Variable-length Markov chain. We observe significant differences in performance depending on the selected feature set. The results show that Naive Bayes is competitive for metal binding site detection. I. INTRODUCTIONProtein plays a crucial role in all biological processes. And they consist of one or more long chains of amino acid residues. In the frame of this perspective, amino acids are important ligands with nitrogen and oxygen as the donor, constituent of many biological important molecules [1].It is estimated that approximately half of all proteins contain a metal [2]. A significant fraction (about one third) of all known proteins is believed to bind metal ions as cofactors in their native conformation [3]. The biological activities of proteins require these cofactors to assist their daily routines. For this reason, a metal ion in a protein and prediction of its binding point is very important to understand the function of proteins in biological activities. Metal ions in proteins are responsible for multiple tasks. They help stabilizing protein structure [4], induce conformational changes [5][6][7], and assist protein functions (e.g. electron transfer, nucleophilic catalysis).There are many related studies about predicting metal binding sites, however, machine learning techniques have been recently applied to predict the metal binding sites of residues. Predicting metal binding sites by using non-computational methods has some drawbacks.X-ray absorption spectroscopy (HT-XAS) has been recently proved to be capable of identifying metalloproteins with high reliability [8,9]. However, the specific ligands involved in binding metal ion(s) cannot be identified by these techniques [9]. Motif-based system has also been developed by using regular expressions but since regular expressions can be quite specific, their results have many false negatives. To overcome these drawbacks, many computational learning techniques have been developed to predict metal binding sites. Early approaches can be found in the work of Nakata et al. (1995). In this study, they focused on predicting zincfinger DNA-binding proteins with a neural network. In this approach, applicable results were generated by a method for c...

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“…Variants of the model were used in applications such as genetic text modeling (Orlov et al, 2002), classification of protein families (Bejerano and Yona, 2001), and statistical process control (Ben-Gal et al, 2003). Ziv (2001) proves that in contrast to other models the convergence of the context tree model to the 'true distribution' model is fast and does not require an infinite sequence length.…”

Section: Introduction To the Vommentioning

confidence: 99%

Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm

et al. 2008

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Universal compression algorithms can detect recurring patterns in any type of temporal data -including financial data -for the purpose of compression. The universal algorithms actually find a model of the data that can be used for either compression or prediction. We present a universal Variable Order Markov (VOM) model and use it to test of the weak form of the Efficient Market Hypothesis (EMH).The EMH is tested for 12 pairs of international intra-day currency exchange rates for one year series of 1,5,10,15,20,25 and 30 minutes. Statistically significant compression is detected in all the time-series and the high frequency series are also predictable above random. However, the predictability of the model is not sufficient to generate a profitable trading strategy, thus, Forex market turns out to be efficient, at least most of the time.

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Variations on probabilistic suffix trees: statistical modeling and prediction of protein families

Cited by 142 publications

References 45 publications

Sequence Motif Identification and Protein Family Classification Using Probabilistic Trees

Sequence Motif Identification and Protein Family Classification Using Probabilistic Trees

Predicting Metal-Binding Sites of Protein Residues

Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm

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