ABDOLLAHYAN, M

European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

SMERALDI, F

English

Queen Mary Research Online

POKer: a Partial Order Kernel for ComparingStrings with Alternative SubstringsMaryam Abdollahyan and Fabrizio SmeraldiSchool of Electronic Engineering and Computer ScienceQueen Mary University of London, Mile End Road, London E1 4NS - UKAbstract. We introduce a Partial Order Kernel (POKer) on the weightedsum of local alignment scores that can be used for comparison and clas-sification of strings containing alternative substrings of variable length.POKer is defined over the product of two directed acyclic graphs, each rep-resenting a string with alternative substrings, and is computed efficientlyusing dynamic programming. We evaluate the performance of POKer withSupport Vector Machines on a dataset of strings generated by detectingoverlapping motifs in a set of simulated DNA sequences. Compared to ageneralization of a state-of-the-art string kernel, POKer achieves a higherclassification accuracy.1 IntroductionString comparison is a fundamental operation in computer science, with applica-tions in many areas including text retrieval, data compression and computationalbiology. Particularly in the latter application, it is often necessary to handle theuncertainty in the strings to be compared. Sequence alignment algorithms basedon dynamic programming are very effective in dealing with uncertainty involv-ing single character substitutions (as in “hello” and “hullo”) [8, 10]; recentlydeveloped variations of these algorithms allow the comparison of strings con-taining alternative substrings that can be substituted for each other (henceforth,strings with alternatives). This is done by representing alternative substrings(e.g. “man” and “person” in “spokes[man|person]ship”) as paths in a directedacyclic graph (DAG). Such a graph can then be aligned to another DAG (e.g.representing “spokes[woman|people]”) to provide a measure of similarity betweentwo strings based on an alignment of best matches (likely “spokesmanship” and“spokeswoman”) [2, 4, 5]. The resulting alignment score, however, is in generalnot a metric, making it impractical for use with many learning algorithms. Thismotivated us to develop a kernel for strings with alternatives.We present a convolution kernel for the comparison of two DAGs, each rep-resenting a partial order over the characters of a string with alternatives. OurPartial Order Kernel (POKer) uses dynamic programming to efficiently computean exponentially weighted sum of the local alignment scores corresponding to allchoices of paths in the two graphs. In addition to being able to handle alterna-tive substrings of different lengths, POKer (which can be seen as a generalizationof the LA kernel [11] and a member of the family of rational kernels [3]) alsoprovides a metric interpretation of sums of partial order alignment scores [5] in263ESANN 2017 proceedings, European Symposium on Artificial Neural Networks, Computational  Intelligence and Machine Learning.  Bruges (Belgium), 26-28 April 2017, i6doc.com publ., ISBN 978-287587039-1. Available from http://www.i6doc.com/en/.A BC DE F B′ C′HF ′Fig. 1: DAG representation of AB[CD|E]FB′[H|C ′|F ′]the form of the norm induced by the kernel. This can be used in combinationwith other learning algorithms beyond kernel methods.We evaluate POKer on a dataset obtained by detecting motifs in simulatedDNA sequences, a common task in computational biology that, due to the over-lapping of motifs, naturally leads to a collection of strings with alternatives. Weuse POKer in conjunction with SVMs to classify this dataset and show thatcompared to a generalization of the popular spectrum kernel [7], POKer has abetter discrimination ability.2 DAG Representation of StringsLet x be a string with alternative substrings. For example, consider the stringx = AB[CD|E]FB′[H|C ′|F ′] where brackets indicate that, for instance, sub-string CD appears in x as an alternative to character E (we use a prime forconvenience to distinguish between multiple occurrences of the same character).We denote the multiset of characters in x by X = {x1, x2, . . . , xn} and its set ofstandard strings, generated by all possible choices of alternatives, by Altx (e.g.ABEFB′C ′ ∈ Altx). Note that repeated characters count as distinct elementsin X . For any two characters xi, xj ∈ X we write xi ≺ xj if xi precedes xj insome string in Altx. The relation ≺ is a partial order on X (as for instance,A ≺ E and C ≺ D but neither H ≺ C ′ nor C ′ ≺ H).We represent (X ,≺) as a directed acyclic graph (DAG) Gx, where V (Gx) =X is the set of vertices and an edge exists between xi and xj if and only ifxi ≺ xj . Each path in Gx, from a source to a sink vertex, corresponds to a stringin Altx (there may exist multiple source/sink vertices since x can start/end withalternatives). The DAG representation of the above example string is shown inFigure 1.3 Partial Order Kernel3.1 DefinitionLet x and y be two strings with alternative substrings. The Partial Order Kernel(POKer) K(x, y) is defined as a convolution of (several copies of) two kernelsKa, Kg with respect to relations Rmx and Rmy over the vertices of paths in Gxand Gy, respectively (see [6] for a definition of convolution kernels). We define264ESANN 2017 proceedings, European Symposium on Artificial Neural Networks, Computational  Intelligence and Machine Learning.  Bruges (Belgium), 26-28 April 2017, i6doc.com publ., ISBN 978-287587039-1. Available from http://www.i6doc.com/en/.Rmx as the set of lists (X1, X2, . . . , X2m−1), Xi ⊆ V (Gx) such that1. There exists a path πx in Gx such that Xi ⊆ V (πx) ∀i and ∪iXi = V (πx);2. the Xi are consecutive, i.e. for all v ∈ Xi and u ∈ Xi+1 we have v ≺ u.Note that the Xi are disjoint, and that some of them may be empty. Rmy isdefined in a similar way. Intuitively, Rmx represents a local alignment of mcharacters along πx, possibly separated by gaps, with m characters along a pathπy in Gy. This intuition guides the definition of the two kernels to be convolved,namely the substitution kernel Ka and the gap kernel Kg. Let X ⊆ V (πx) andY ⊆ V (πy). Similarity of aligned characters is measured by the substitutionkernelKa(β)(X,Y ) ={exp(βs(X,Y )) if |X| = 1 and |Y | = 10 otherwise(1)where β ≥ 0 is a parameter and s(X,Y ) is the substitution score for the labelsof vertices in X and Y (specified by e.g. a scoring matrix). Valid values for βare those for which the kernel remains positive semi-definite. Penalty for gapsis quantified by the gap kernelKg(β)(X,Y ) = exp (βg (|X|+ |Y |)) (2)where g is a (linear) gap penalty. We convolve the above kernels to constructthe Km kernelKm(x, y) = (Ka ∗Kg)m−1 ∗Ka =∑X∈Rmx ,Y ∈Rmy[m−1∏k=1Ka(β)(X2k−1, Y2k−1)Kg(β)(X2k, Y2k)]Ka(β)(X2m−1, Y2m−1)(3)It is clear from the definition of Ka that the terms in the above sum are zerounless all the Xi and Yi with odd indexes are singletons, and thatKm(x, y) =∑X∈Rmx ,Y ∈Rmyexp(βS(X,Y )) (4)where S(X,Y ) is the score of the local alignment of m characters along πx withm characters along πy specified by (X,Y ). Note that Km is zero when m islarger than the length of the longest path in Gx or in Gy. With the abovedefinitions, we now define the POKerK(x, y) =∑m≥0Km(x, y) =∑m≥0∑X∈Rmx ,Y ∈Rmyexp(βS(X,Y )) (5)POKer is equal to an exponentially weighted sum of the scores of all the localalignments between any number of characters in x and the same number ofcharacters in y, selected from any paths in Gx and Gy, that is, from any choice ofalternatives. In the next section, we show how this (finite) sum can be computedefficiently using dynamic programming.265ESANN 2017 proceedings, European Symposium on Artificial Neural Networks, Computational  Intelligence and Machine Learning.  Bruges (Belgium), 26-28 April 2017, i6doc.com publ., ISBN 978-287587039-1. Available from http://www.i6doc.com/en/.3.2 ComputationWe will assume, without loss of generality, that both x and y begin with a startcharacter x0 = y0 = φ, that is, Gx and Gy each have a single source vertexlabelled φ, and s(φ, φ) = s(xi, φ) = s(φ, yj) = 0 for all i and j. POKer iscomputed efficiently using dynamic programming over the strong product graphGxy = Gx  Gy, with a complexity that is linear in the number of vertices ofGxy, here indicated simply as (i, j):K(x, y) = 1 +∑(i,j)∈V (Gxy)M(i, j) (6)where M(i, j) is computed recursively based on the following{M(i, φ) = M(φ, j) = 0N(i, φ) = N(φ, j) = 0(7)and ⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩M(i, j) = exp(βs(i, j))[1 +∑mi∈E(Gx),nj∈E(Gy)N(m,n)]N(i, j) = exp(βg)∑mi∈E(Gx)N(m, j)+ exp(βg)∑nj∈E(Gy)N(i, n)− exp(2βg) ∑mi∈E(Gx),nj∈E(Gy)N(m,n)+ M(i, j).(8)where β ≥ 0 is a parameter and g is the gap penalty.Each local alignment corresponds to a path in Gxy. POKer (Equation 6) isa sum over the exponentiated scores of all the local alignments ending at eachvertex (i, j) in Gxy (including the empty alignment). The contributions of allthe local alignments ending at (i, j), including those with the labels of i andj being the only aligned characters is accounted for by M(i, j). Penalties forinserting gaps in x or y at the end of a partial alignment are included in N(i, j).A gap in x followed by a gap in y, and a gap in y followed by a gap in x areequivalent in terms of aligned characters; this is accounted for by the negativeterm in Equation 8.4 Experimental ResultsWe tested POKer with SVMs in a multi-class classification scenario and com-pared its performance for different number of classes to that obtained by SVMsusing a generalized spectrum kernel [7].We used a standard publicly available tool (rMotifGen [9]) to generate thedataset as follows. First, we produced a dictionary consisting of 50 randomlygenerated motifs, i.e. short DNA sequences, each representing a binding site.266ESANN 2017 proceedings, European Symposium on Artificial Neural Networks, Computational  Intelligence and Machine Learning.  Bruges (Belgium), 26-28 April 2017, i6doc.com publ., ISBN 978-287587039-1. Available from http://www.i6doc.com/en/.We assigned a unique character to each motif. To generate the strings in eachclass, we randomly chose 10 motifs from the dictionary. We then generated 200random DNA sequences containing these motifs. We set the parameters so thateach motif (out of 10) appears in 70% of the sequences, and used the defaultvalues for the rest of the parameters. Finally, we scanned each sequence for theoccurrences of all 50 motifs (as motifs others than those explicitly inserted in asequence can appear in it). This yielded a string of characters corresponding tothe detected motifs. Since two or more motifs can overlap but only one protein ata time can bind to the overlapping sites, this string is a string with alternativesubstrings (similar to Figure 1) [1]. We repeated this procedure 40 times togenerate 40 classes of 200 strings with alternatives, i.e. a total of 8000 strings.To the best of our knowledge, no other kernels have been specifically designedfor strings with alternative substrings of variable length. However, the popularspectrum kernel [7] can be extended in order to deal with such strings and providea baseline for comparison. We define this generalized kernel as follows. Let x bea string with alternatives over an alphabet A, and Gx its DAG representation.We denote the set of all paths of length k in Gx, i.e. k -mers in x, by P(k,x). Wedefine the feature map indexed by the set of all k -length substrings α from AkasΦk(x) =(∣∣{π ∈ P(k,x)|(V (π)) = α}∣∣)α∈Ak (9)where (V (π)) is the sequence of labels of vertices of π. The generalized spectrumkernel is then equal toKk(x, y) = 〈Φk(x),Φk(y)〉 (10)Note that this is not equivalent to accumulating the occurrence of each k -merover all the strings in Altx, since substrings common to multiple strings in Altxwould then be counted more than once.We built an SVM classifier for each class using the one-versus-all strategy.The match score, mismatch score and gap penalty were set to 4, 0 and -2,respectively. We tested POKer with several β values, ranging from 0 to +∞,and chose the one that yielded the best performance (β=0.1). Similarly, wetested the generalized spectrum kernel with several k ∈ {2, 3, 4, 5} values andchose the one for which the kernel performed best (k=3). We use ROC scores(area under the ROC curve), averaged over the number of classes, to compare theperformance of two kernels. The results are shown in Table 1. As can be seen,POKer outperforms the generalized spectrum kernel in all cases, its performancedecreasing only marginally as the number of classes increases. Moreover, POKeris fairly robust to the choice of β; in the case of 40 classes, the best meanROC score is 96.4% when β=0.1 while β=0.01 and β=1 yield 95.8% and 91.2%,respectively.5 ConclusionsIn this work, we presented POKer, a convolution kernel on the weighted sum oflocal alignment scores for comparing strings containing alternative substrings.267ESANN 2017 proceedings, European Symposium on Artificial Neural Networks, Computational  Intelligence and Machine Learning.  Bruges (Belgium), 26-28 April 2017, i6doc.com publ., ISBN 978-287587039-1. Available from http://www.i6doc.com/en/.Mean ROC scoreNo. of classes POKer Generalized spectrum kernel5 98.4% 93.4%10 98.1% 90.1%20 97.8% 86.4%40 96.4% 82.0%Table 1: Mean ROC scores obtained by POKer and the generalized spectrumkernel for different number of classesPOKer can handle alternative substrings of different length, is computed effi-ciently via dynamic programming and has an intuitive interpretation in termsof the sum of local alignment scores for all alternatives. In our experiments wefound POKer to be very effective in discriminating between classes of stringswith alternative substrings, compared to a generalization of the popular spec-trum kernel. With richer mathematical properties than (non-metric) alignmentscores, low computational complexity and classification effectiveness, POKer canbe a powerful tool in the analysis of strings with alternatives.References[1] M. Abdollahyan, F. Smeraldi, B. Noyvert, and G. Elgar, Transcription factor binding site-based alignment of conserved non-coding elements. Poster presented at the European Con-ference on Computational Biology (ECCB 2016). doi:10.7490/f1000research.1113029.1[2] L. Arvestad, Algorithms for biological sequence alignment. 1999.[3] C. Cortes, P. Haffner and M. Mohri, Rational kernels: Theory and algorithms. Journalof Machine Learning Research, 5:1035–1062, 2004.[4] M. Gı̂rdea, L. Noé and G. Kucherov, Back-translation for discovering distant proteinhomologies. Algorithms in Bioinformatics, 108–120, Springer, 2009.[5] C. Grasso and C. Lee, Combining partial order alignment and progressive multiple se-quence alignment increases alignment speed and scalability to very large alignment prob-lems. Bioinformatics, 20(10):1546–1556, 2004.[6] D. Haussler, Convolution kernels on discrete structures. Technical report, Department ofComputer Science, University of California at Santa Cruz, 1999.[7] C. S. Leslie, E. Eskin and W. S. Noble, The spectrum kernel: A string kernel for SVMprotein classification. Pacific symposium on biocomputing. Vol. 7, No. 7, pages 566–575,2002.[8] S. B. Needleman and C. D. Wunsch, A general method applicable to the search forsimilarities in the amino acid sequence of two proteins. Journal of molecular biology,48(3):443–453, 1970.[9] E. C. Rouchka and C. T. Hardin, rMotifGen: random motif generator for DNA andprotein sequences. BMC bioinformatics, 8(1), 2007.[10] T. F. Smith and M. S. Waterman, Identification of common molecular subsequences.Journal of molecular biology, 147(1):195–197, 1981.[11] J. P. Vert, H. Saigo and T. Akutsu, Convolution and local alignment kernels. Kernelmethods in computational biology, pages 131–154, MIT press, 2004.268ESANN 2017 proceedings, European Symposium on Artificial Neural Networks, Computational  Intelligence and Machine Learning.  Bruges (Belgium), 26-28 April 2017, i6doc.com publ., ISBN 978-287587039-1. Available from http://www.i6doc.com/en/.

POKer: a Partial Order Kernel for Comparing Strings with Alternative Substrings

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POKer: a Partial Order Kernel for Comparing Strings with Alternative Substrings

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