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A multiple kernel learning algorithm for drug-target interaction prediction

Nascimento et al. BMC Bioinformatics (2016) 17:46 DOI 10.1186/s12859-016-0890-3 Open Access
A multiple kernel learning algorithm fordrug-target interaction predictionAndré C. A. Nascimento1,2,3*, Ricardo B. C. Prudêncio1 and Ivan G. Costa1,3,4 Background: Drug-target networks are receiving a lot of attention in late years, given its relevance for
pharmaceutical innovation and drug lead discovery. Different in silico approaches have been proposed for the
identification of new drug-target interactions, many of which are based on kernel methods. Despite technical
advances in the latest years, these methods are not able to cope with large drug-target interaction spaces and to
integrate multiple sources of biological information.
Results: We propose KronRLS-MKL, which models the drug-target interaction problem as a link prediction task on
bipartite networks. This method allows the integration of multiple heterogeneous information sources for the
identification of new interactions, and can also work with networks of arbitrary size. Moreover, it automatically selects
the more relevant kernels by returning weights indicating their importance in the drug-target prediction at hand.
Empirical analysis on four data sets using twenty distinct kernels indicates that our method has higher or comparable
predictive performance than 18 competing methods in all prediction tasks. Moreover, the predicted weights reflect
the predictive quality of each kernel on exhaustive pairwise experiments, which indicates the success of the method
to automatically reveal relevant biological sources.
Conclusions: Our analysis show that the proposed data integration strategy is able to improve the quality of the
predicted interactions, and can speed up the identification of new drug-target interactions as well as identify relevant
information for the task.
Availability: The source code and data sets are available at
Keywords: Artificial intelligence, Supervised machine learning, Kernel methods, Multiple kernel learning,
Drug discovery
interactions between these entities. Nevertheless, as the Drug-target networks are receiving a lot of attention in experimental verification of such interactions does not late years, given their relevance for pharmaceutical inno- scale with the demand for innovation, the use of computa- vation and drug repositioning purposes Although tional methods for the large scale prediction is mandatory.
the amount of known interactions between drugs and There is also a clear need for systems-based approaches to target proteins has been increasing, the number of tar- integrate these data for drug discovery and repositioning gets for approved drugs is still only a small proportion (< 10 %) from the human proteome Recent advances Recently, an increasing number of methods have been on high-throughput methods provide ways for the pro- proposed for drug-target interaction (DTI) prediction.
duction of large data sets about molecular entities as They can be categorized in ligand-based, docking-based, drugs and proteins. There is also an increase in the avail- or network-based methods The docking approach, ability of reliable databases integrating information about which can provide accurate estimates to DTIs, is com-putationally demanding and requires a 3D model of the *Correspondence: target protein. Ligand-based methods, such as the quan- 1Center of Informatics, UFPE, Recife, Brazil titative structure activity relationship (QSAR), are based Department of Statistics and Informatics, UFRPE, Recife, Brazil Full list of author information is available at the end of the article 2016 Nascimento et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
applies to the data made available in this article, unless otherwise stated.
Nascimento et al. BMC Bioinformatics (2016) 17:46 on a comparison of a candidate ligand to the known lig- These approaches use base kernels to measure the sim- ands of a biological target However, the utility of these ilarity between drugs (or targets) using distinct sources ligand-based methods is limited when there are few lig- of information (e.g., structural, pharmacophore, sequence ands for a given target Alternatively, network and function similarity). A pairwise kernel function, which based approaches use computational methods and known measures the similarity between drug-target pairs, is DTIs to predict new interactions Even though obtained by combining a drug and a protein base kernel ligand-based and docking-based methods are more pre- via kernel product.
cise when compared to network based approaches, the The majority of previous network approaches use clas- latter are more adequate for the estimation of new inter- sification methods, as Support Vector Machines (SVM), actions from complete proteomes and drugs catalogs to perform predictions over the drug-target interaction Therefore, it can indicate novel candidates to be evaluated space However, such techniques have major limi- by more accurate methods.
tations. First, they can only incorporate one pair of base Most network approaches are based on bipartite graphs, kernels at a time (one for drugs and one for proteins) in which the nodes are composed of drugs (small to perform predictions. Second, the computation of the molecules) and biological targets (proteins) Edges pairwise kernel matrix for the whole interaction space (all between drugs and targets indicate a known DTI (Fig possible drug-target pairs) is computationally unfeasible Given a known interaction network, kernel based meth- even for a moderate number of drugs and targets. More- ods can be used to predict unknown drug-target inter- over, most drug target interaction databases provide no actions A kernel can be seen as a similarity true negative interaction examples. The common solution matrix estimated on all pairs of instances. The main for these issues is to randomly sample a small proportion assumption behind network kernel methods is that simi- of unknown interactions to be used as negative examples.
lar ligands tend to bind to similar targets and vice versa.
While this approach provides a computationally trackable Sequence kernels, chemical structures, functional annotations, Fig. 1 Overview of the proposed method. a The drug-target is a bipartite graph with drugs (left) and proteins (right). Edges between drugs and
proteins (solid line) indicates a known drug-protein interaction. The drug-protein interaction problem is defined as finding unknown edges (dashed
lines) with the assumption that similar drugs (or proteins) should share the same edges. b KronRLS-MKL uses several drugs (and protein) kernels to
solve the drug-target interaction problem. Distinct Kernels are obtained by measuring similarities of drugs (or proteins) using distinct information
sources. c KronRLS-MKL provides not only novel predicted interactions as it indicates the relevance (weights) of each kernel used in the predictions
Nascimento et al. BMC Bioinformatics (2016) 17:46 small drug-target pairwise kernel, it generates an easier competitive in the majority of evaluated scenarios. More- but unreal classification task with balanced class size over, KronRLS-MKL was able to select and also indicate An emerging machine learning (ML) discipline focused the relevance of kernels, in the form of weights, for each on the search for an optimal combination of kernels, called Multiple Kernel Learning (MKL) MKL-likemethods have been previously proposed to the prob- lem of DTI prediction and the closely related In this work, we propose an extension of the Kron- protein-protein interaction (PPI) prediction problem RLS algorithm under recent developments of the MKL This is extremely relevant, as it allows the use of framework to address the problem of link predic- distinct sources of biological information to define sim- tion on bipartite networks with multiple kernels. Before ilarities between molecular entities. However, since tra- introducing our method, we will describe the RLS and ditional MKL methods are SVM-based they are the KronRLS algorithms (for further information, see subject to memory limitations imposed by the pairwise kernel, and are not able to perform predictions in the com-plete drugs vs. protein space. Moreover, MKL approaches RLS and KronRLS
used in PPI prediction problem and protein Given a set of drugs D = {d1, . . , dn }, targets T = function prediction can not be applied to bipar- {t1, . . , tn }, and the set of training inputs x i (drug-target tite graphs, as the problem at hand. Currently, we are pairs) and their binary labels yi ∈ R (where 1 stands for a only aware of two recent works proposing MKL known interaction and 0 otherwise), with 1 < i n, n = approach to integrate similarity measures for drugs and D T (number of drug-target pairs). The RLS approach minimizes the following function Drug-target prediction fits a link prediction problem which can be solved by a Kronecker regularized least squares approach (KronRLS) . A single kernel version i f (xi))2 + of this method has been recently applied to drug-targetprediction problem A recent survey indicated where  f K is the norm of the prediction function f on that KronRLS outperforms SVM based methods in DTI the Hilbert space associated to the kernel K, and λ > 0 prediction KronRLS uses Kronecker product algebraic is a regularization parameter which determines the com- properties to be able to perform predictions on the whole promise between the prediction error and the complexity drug-target space, without the explicit calculation of the of the model. According to the representer theorem pairwise kernels. Therefore, it can cope with problems on a minimizer of the above objective function admits a dual large drugs vs. proteins spaces. However, KronRLS can representation of the following form not be used on a MKL context.
In this work, we propose a new MKL algorithm to automatically select and combine kernels on a bipar- iK (x, xi) , tite drug-protein prediction problem, the KronRLS-MKLalgorithm (Fig For this, we extend the KronRLS method where K : D T × D T → R is named the pair- to a MKL scenario. Our method uses L2 regularization wise kernel function and a is the vector of dual variables
to produce a non-sparse combination of base kernels.
corresponding to each separation constraint. The RLS The proposed method can cope with large drug vs. target algorithm obtains the minimizer of Eq. solving a sys- interaction matrices; does not requires sub-sampling of tem of linear equations defined by (K + λI)a = y, where
the drug-target network; and is also able to combine and a and y are both n-dimensional vectors consisting of the
select relevant kernels. We perform an empirical analysis parameters ai and labels yi.
using drug-target datasets previously described and a One can construct such pairwise kernel as the prod- diverse set of drug kernels (10) and protein kernels (10).
uct of two base kernels, namely K ((d, t), (d, t)) = In our experiments, we considered three different sce- KD(d, d)KT (t, t), where KD and KT are the base kernels narios in the DTI prediction pair prediction, for drugs and targets, respectively. This is equivalent to where every drug and target in the training set have the Kronecker product of the two base kernels at least one known interaction; or the ‘new drug' and K = KD KT . The size of the kernel matrix makes ‘new target' setting, where some drugs and targets are the model training computationally unfeasible even for present only in the test set, respectively. A comparative moderate number of drugs and targets analysis with top performance single kernel approaches The KronRLS algorithm is a modification of RLS, and and all competing integrative approaches takes advantage of two specific algebraic properties of the demonstrates that our method is better or Kronecker product to speed up model training: the so Nascimento et al. BMC Bioinformatics (2016) 17:46 called vec trick and the relation of the eigendecom- This way, we can rewrite the classification function as position of the Kronecker product to the eigendecompo- K A K ∗ , where A = unvec(a). Using the same itera-
sition of its factors tive approach considered in previous MKL strategies Let KD = QDDQTD and KT = QTTQT be the we propose the use of a two step optimization process, in eigendecomposition of the kernel matrices KD e KT . The which the optimization of the vector a is interleaved with
solution a can be given by solving the following equation
the optimization of the kernel weights. Given two initial weight vectors, β0D and β0 , an optimal value for the vec-
a = vec(Q
tor a, using Eq. is found, and with such optimal a, we
can proceed to find optimal βD and βT . More specifically,
where vec(·) is the vectorization operator that stacks the Eq. can be redefined when a is fixed, and knowing that
columns of a matrix into a vector, and C is a matrix  f 2 = aTKa we have:
C = ( u = y
D T + λI)−1vec(QT T Y T QD) .
The KronRLS algorithm is well suited for the large pair- wise space involved on the DTI prediction problem, since
the estimation of vector a using Eqs. and is a much
J(fa) = 1  u Ka2 + 1 aT (y λa).
faster solution compared to the original RLS estimationprocess in such scenario. However, it does not support the Since the second term does not depend on K (and thus use of multiple kernels.
does not depend on the kernel weights), and, as y and a
are fixed, it can be discarded from the weights optimiza-
tion procedure. Note that we are not interested in a sparse In this work, a vector of different kernels is considered, selection of base kernels as in therefore we intro- duce a L2 regularization term to control sparsity of D = (K 1 D, K 2 D, . . , K PD and kT = (K1 , K2 , . . , KPT ,
the kernel weights, also known as a ball constraint. This D and PT indicate the number of base kernels defined over the drugs and target set, respectively. In this section, term is parameterized by the σ regularization coefficient.
we propose an extension of KronRLS to handle multiple Additionally, we can convert u to its matrix form by the
application of the unvec operator, i.e., U = unvec(u),
The kernels can be combined by a linear function, i.e., and also use a more appropriate matrix norm (Frobenius, the weighted sum of base kernels, corresponding to the  A 2≤ A F In this way, for any fixed values of optimal kernels K a and βT , the optimal value for the combination vector is
D and K ∗ : obtained by solving the optimization problem defined as: DK iD , K ∗  U mDβ
D F + σ  βD2
mD = KTA K1D
, . . , KTA KPA D, . . , βPD and βT =
, . . , βPT , correspond to the weights of drug and protein kernels, while the optimal β
In the author demonstrated that MKL can be inter- T can be found fixing the values of a
preted as a particular instance of a kernel machine with D, according to: two layers, in which the second layer is a linear function.
 U β
His work provides the theoretical basis for the develop- T mT F + σ  βT 2
ment of a MKL extension for the closely related KronRLS , ., K PT A K ∗ algorithm in our work.
The classification function of Eq. can be written in matricial form, fa = Ka and applying the well known
The optimization method used here is the interior-point property of the Kronecker product, (A B)vec(X) = optimization algorithm implemented in MATLAB vec BXAT we have: fa(X) = Ka
= K∗ ⊗ T vec QT CQT The datasets considered were first proposed by and used by most competing methods QT CQTD KD Each dataset consists of a binary matrix, containing the Nascimento et al. BMC Bioinformatics (2016) 17:46 known interactions of a determined set of drug targets, Table 2 Network entities and respective kernels considered for
namely Enzyme (E), Ion Channel (IC), GPCR and Nuclear combination purposes Receptors (NR), based on information extracted from the KEGG BRITE BRENDA SuperTarget and DrugBank databases All four datasets are extremely AERS-bit - AERS bit unbalanced, if we consider the whole drug-target inter- AERS-freq - AERS freq action space, i.e., the number of known interactions is GIP - Gaussian Interaction Profile extremely lower than the number of unknown interac- LAMBDA - Lambda-k Kernel Chem. Struct.
tions, as presented in Table In order to analyze each type of entity from differ- MARG - Marginalized Kernel Chem. Struct.
ent points of view, we extracted 20 (10 for targets and MINMAX - MinMax Kernel Chem. Struct.
10 for drugs) distinct kernels from chemical structures, SIMCOMP - Graph kernel Chem. Struct.
side-effects, amino acid sequence, biological function, PPI SIDER - Side-effects Similarity interactions and network topology (a summary of base SPEC - Spectrum Kernel Chem. Struct.
kernels is presented in Table .
TAN - Tanimoto Kernel Chem. Struct.
GIP - Gaussian Interaction Profile Here we use the following information sources about tar- GO - Gene Ontology Semantic Similarity Func. Annot.
get proteins: amino acid sequence, functional annotationand proximity in the protein-protein network. Concern- MIS-k3m1 - Mismatch kernel (k = 3, m = 1) ing sequence information, we consider the normalizedscore of the Smith-Waterman alignment of the amino acid MIS-k4m1 - Mismatch kernel (k = 4, m = 1) sequence (SW) as well as different parametrizations MIS-k3m2 - Mismatch kernel of the Mismatch (MIS) and the Spectrum (SPEC) (k = 3, m = 2) kernels. For the Mismatch kernel, we evaluated four com- MIS-k4m2 - Mismatch kernel binations of distinct values for the k-mers length (k = 3 (k = 3, m = 2) and k = 4) and the number of maximal mismatches PPI - Proximity in protein-protein per k-mer (m = 1 and m = 2), namely MIS-k3m1, MIS-k3m2, MIS-k4m1 and MIS-k4m2; for the Spec- SPEC-k3 - Spectrum kernel (k = 3) trum kernel, we varied the k-mers length (k = 3 and k = SPEC-k4 - Spectrum kernel (k = 4) 4, SPEC-k3 and SPEC-k4, respectively). Both Mismatchand Spectrum kernels were calculated using the R package SW - Smith-Waterman aligment score The Gene Ontology semantic similarity kernel (GO) with the Resnik algorithm We also extracted a was used to encode functional information. GO terms similarity measure from the human protein-protein net- were extracted from the BioMART database and work (PPI), obtained from the BioGRID database the semantic similarity scores between the GO annota- The similarity between each pair of targets was calculated tion terms were calculated using the csbl.go R package based on the shortest distance on the corresponding PPInetwork, according to: Table 1 Number drugs, targets and positive instances (known
interactions) vs. the number of negative (or unknown)
S(p, p) = AebD(p,p), interactions on each dataset where A and b parameters were set as in (A = 0.9, b = 1), and D(p, p) is the shortest hop distance Nuclear receptors between proteins p and p.
As drug information sources, we consider 6 distinct chem- ical structure and 3 side-effects kernels. Chemical struc- ture similarity between drugs was achieved by the applica- tion of the SIMCOMP algorithm (obtained from defined as the ratio of common substructures betweentwo drugs based on the chemical graph alignment. We also computed the Lambda-k kernel (LAMBDA) the Marginalized kernel (MARG), the MINMAX kernel Nascimento et al. BMC Bioinformatics (2016) 17:46 the Spectrum kernel (SPEC) and the Tanimoto kernel of multiple kernels (respectively for drugs and targets); (TAN). These later kernels were calculated with the R (2) actual MKL methods specifically proposed for DTI Package Rchemcpp with default parameters.
Two distinct side-effects data sources were also consid- ered. The FDA adverse event reporting system (AERS), from which side effect keywords (adverse event keywords) We extend state-of-the-art methods for the similarities for drugs were first retrieved by . The DTI prediction problem for a multiple kernel context. For authors introduced two types of pharmacological profiles this, initially we average multiple kernels to produce a sin- for drugs, one based on the frequency information of side gle kernel (respectively for drugs and targets). Once we effect keywords in adverse event reports (AERS-freq) have a single average kernel (one for drug and one for and another based on the binary information (presence target), we adopt a standard kernel method for DTI pre- or absence) of a particular side-effect in adverse event diction, i.e., the base learner. In our experiments, two dis- reports (AERS-bit). Since not every drug in the Nuclear tinct previous combinations strategies are used: the mean Receptors, Ion Channel, GPCR and Enzyme datasets is of base kernels and the kernel alignment (KA) heuristic, also present on AERS-based data, we extracted the simi- previously proposed by We will briefly describe the larities of the drugs in AERS, and assigned zero similarity base learners, followed by a short overview of the two to drugs not present.
combination strategies considered.
The second side-effect resource was the SIDER The Bipartite Local Model (BLM) is a machine database1 This database contains information about learning based algorithm, where drug-target pairs are pre- commercial drugs and their recorded side effects or dicted by the construction of the so called ‘local models', adverse drug reactions. Each drug is represented by a i.e., a SVM classifier is trained for each drug in the training binary profile, in which the presence or absence of each set, and the same is done for targets. Then, the maxi- side effect keyword is coded 1 or 0, respectively. Both mum scores for drugs and targets are used to predict new AERS and SIDER based profile similarities were obtained drug-target interactions. Since BLM demonstrated supe- by the weighted cosine correlation coefficient between rior performance than Kernel Regression Method (KRM) each pair of drug profiles in previous studies we did not consider KRMin our experiments.
Network topology information
The Network-based Random Walk with Restart on the We also use drug-target network structure in the form of Heterogeneous network (NRWRH) algorithm predicts a network interaction profile as a similarity measure for new interactions between drugs and targets by the simu- both proteins and drugs. The idea is to encode the con- lation of a random walk in the network of known drug- nectivity behavior of each node in the subjacent network.
target predictions as well as in the drug-drug and protein- The Gaussian Interaction Profile kernel (GIP) was protein similarity networks. LapRLS and NetLapRLS are calculated for both drugs and targets.
both proposed in Both are based on the RLS learn-ing algorithm, and perform similarity normalization by the application of the Laplacian operator. Predictions are We compare the predictive performance of the KronRLS- done for drugs and targets separately, and the final predic- MKL algorithm against other MKL approaches, as well as tion scores are obtained by averaging the prediction result in a single kernel context (one kernel for drugs, and one for from drug and target spaces.
targets). In the latter, we evaluate the performance of each As said previously, most previous SVM-based methods possible combination of base kernels (Table with the found on the literature can be reduced to the Pairwise Ker- KronRLS algorithm, recently reported as the best method nel Method (PKM) with the distinction being made for predicting drug-target pairs with single paired kernels by the kernels used and the adopted combination strat- This resulted in a total of 10 × 10 = 100 different egy. PKM starts with the construction of a pairwise kernel, combinations. The best performing pairs were then used computed from the drug and target similarities. Given two as baselines in our method evaluation, selected according drug-target pairs, (d, p) and (d, p), and the respective to two distinct criteria: the kernel pair that achieved the drug and target similarities, KD and KP, the pairwise ker- largest area under the precision recall curve (AUPR) on nel is given by K ((d, p), (d, p) = KD(d, d) × KP(p, p).
the training set, and, a more optimistic approach, which Once the pairwise matrix is computed, it is then used to considered the largest AUPR on the testing set.
train a SVM classifier.
Besides the combination of single kernels for drugs and The PKM KronRLS, BLM, NRWRH, LapRLS and targets, two different kinds of methods were adopted to NetLapRLS algorithms cannot cope with multiple kernels.
integrate multiple kernels: (1) standard non-MKL ker- For this reason, we consider two simple methods avail- nel methods for DTI prediction, trained on the average able for kernel combination: the mean of base kernels and Nascimento et al. BMC Bioinformatics (2016) 17:46 the kernel alignment (KA) heuristic The mean drug pairwise matrix is computed, it is then used to train a SVM kernel is computed as K ∗ = i=1 K iD, and the same classifier. This procedure is also known as the Pairwise can be done for targets, analogously. KA is a heuristic for Kernel Method (PKM) For this reason, we refer to the the estimation of kernel weights based on the notion of approach proposed by by PKM-MAX.
kernel alignment More specifically, the weight vector, The authors in suggest as further work a weighted βD for instance, can be obtained by:
sum approach. They suggest to learn the optimal convex combination of data sources maximizing the correlation of the obtained kernel matrix with the topology of drug- protein network. This objective can be achieved by solving a linear programming problem, as follows: where yyT stands for the ideal kernel and y being the label
D, dist) vector. The alignment A K , yyT of a given kernel K and
where K ∗ the ideal kernel yyT is defined as:
D correspond to the optimal combination of drug kernel matrices with weight vector β
D, dist is the drug- K , yyT
drug distance matrix in the DTI network, and corr rep- A K , yyT =
K, K resents the correlation coefficient. Analogously, the same can be done for targets. We call this method WANG-MKL.
where K , yyT
(K) yyT . Once such com-
i=1 j=1 binations are performed, the resulting drug and protein Previous work suggest that, in the context of kernels are then used as input to the learning algorithm.
paired input problems, one should consider separately We refer to the mean and KA heuristics appending the the experiments where the training and test sets share -MEAN and -KA, respectively, to each base learner.
common drugs or proteins. In order to achieve a clearnotion of the performance of each method, all competing Multiple kernel approaches
approaches were evaluated under 5 runs of three distinct Similarity-based Inference of drug-TARgets (SITAR) 5-fold cross-validation (CV) procedures: constructs a feature vector with the similarity values,where each feature is based on one drug-drug and one 1. ‘new drug' scenario: it simulates the task of gene-gene similarity measure, resulting in a total of PD × predicting targets for new drugs. In this scenario, the PT features. Each one is calculated by combining the drug- drugs in a dataset were divided in 5 disjoint subsets drug similarities between the query drug and other drugs (folds). Then the pairs associated to 4 folds of drugs and the gene-gene similarities between the query gene and were used to train the classifier and the remaining other target genes across all true drug-target associations.
pairs are used to test; The method also performs a feature selection procedure 2. ‘new target' scenario: it corresponds in turn to and yields the final classification scores using a logistic predicting interacting drugs for new targets. This is analogous to the above scenario, however Gönen and Kaski proposed the Kernelized Bayesian considering 5 folds of targets; Matrix Factorization with Twin Multiple Kernel Learn- 3. pair prediction: is consists of predicting unknown ing (KBMF2MKL) algorithm, extending a previous work interactions between known drugs and targets. All to handle multiple kernels. The KBMF2MKL factor- drug-target interactionswere split in five folds, from izes the drug-target interaction matrix by projecting the which 4 were used for training and 1 for testing.
drugs and the targets into a common subspace, where the Some of the competing methods (PKM-based, projected drug and target kernels are multiplied. Normally WANG-MKL and SITAR) were trained with distributed Kernel weights for each subspace projected sub-sampled datasets, i.e., we randomly selected the kernel are then estimated without any constraints. The same number of known interactions among the product of the final combined matrices is then used to unknown interaction set, since these methods cannot make predictions.
be executed in large networks Although Wang et al. proposes to use a simple heuristic to balanced classes are unlikely in real scenarios, we also previously combine the drug and target similarities, and performed experiments in context (3), using a then use a SVM classifier to perform the predictions.
sub-sampled test set, obtained by sampling as many Only the maximum similarity values of drug and target negative examples as positive examples from kernel matrices are selected, resulting in two distinct ker- the test fold. This experiment is relevant for nels. They are then used to construct a pairwise kernel, comparison to previous work, since most previous computed from the drug and target similarities. Once the studies on drug-target prediction performed Nascimento et al. BMC Bioinformatics (2016) 17:46 under-sampling to evaluate predictive performance the interval {0, 0.25, 0.5, 0.75, 1}. The number of com- (see Additional file Table S1).2 ponents in KBMF2MKL was varied in the interval R ∈ {5, 10, . . , 40}, and for the LapRLS and NetLapRLS The hyperparameters of each competing methods were we varied βd, βt ∈ {0.25, 0.50, . . , 1}. In NetLapRLS optimized under a nested CV procedure, using the fol- we also considered two distinct values for γd2, γt2 ∈ lowing values: for the SVM-based methods (PKM, BLM {0.01, 0.1}. For NRWRH the restart probability was eval- and WANG-MKL), the SVM cost parameter was evaluated uated in the set {0.1, 0.2, . . , 0.9}. After the hyperpa- under the interval {2−1, . . , 23}; for the KronRLS-based rameters were selected for each method, the outer methods, the λ parameter was evaluated in the inter- loop evaluated the predictive performance for the test val {2−15, 2−10, . . , 230}. The σ regularization coefficient set partition with the model built using the selected of the KRONRLS-MKL algorithm was also optimized in Fig. 2 Average performance of each single kernel with the KronRLS algorithm as base learner. The boxplots shows the AUPR performance of drug
and protein kernels across different kernel combinations
Nascimento et al. BMC Bioinformatics (2016) 17:46 Table 3 Results on MKL Experiments on 5 × 5 cross-validation experiments
[SPEC-k4]-[GIP] ∗ [SPEC-k4]-[MINMAX] † Nascimento et al. BMC Bioinformatics (2016) 17:46 Table 3 Results on MKL Experiments on 5 × 5 cross-validation experiments (Continued)
Best performing methods are indicated in bold. Standart deviation is indicated in brackets. Training of the PKM, SITAR and WANG algorithms was done with the balanced training set†best on training ∗ best on testing The evaluation metric considered was the AUPR, as it According to this metric provides a better qual- allows a good quantitative estimate of the ability to sep- ity estimate for highly unbalanced data, since it punishes arate the positive interactions from the negative ones.
more heavily the existence of false positives (FP). This Nascimento et al. BMC Bioinformatics (2016) 17:46 is specially true for the datasets considered, as demon- better than all evaluated methods with the exception of strated on Table in which all datasets are extremely BLM-Mean, KBMF-MKL, KRONRLS-KA, KRONRL-MEAN and KRONRLS-MKL (α = 0.05 Additional file Inthe 'new drug' problem, KRONRLS-MKL obtained higher Results and discussion
AUPR in the NR and GPCR datasets, while BLM-KA had Paired kernel experiments
higher AUPR values in the IC and Enzyme data. Both As a base study, we evaluate the performance of KronRLS KRONRLS-MKL and BLM-KA had statistically significant on all pairs of kernels (10 × 10 pairs). The AUPR results of higher AUPR (at α = 0.05; Additional file than all other all pairs of kernels for the Nuclear Receptors, GPCR, Ion competing methods. In order to give an overview of the Channel and Enzyme datasets are show in more detail in performance of the evaluated methods, an average rank- the supplementary material (see Additional file ing of the AUPR values obtained by all methods across the The performance of KronRLS varies drastically with four datasets is presented in Table the kernel choice, as clearly demonstrated by the average Methods also displayed distinct computational require- performance of each kernel on the single kernel experi- ments. Memory usage was stable accross all methods, ments (Fig. For Nuclear Receptors, the best kernel pair except from the SVM-based algorithms, which demon- combination was SPEC-k4 and GIP, while GIP and SW strated quadratic growth of the memory used in relation performed best in all other data sets. It is also important to the size of the dataset (BLM, PKM, WANG-MKL). This to notice the impact of different parametrizations of the is in part due to the construction of the explicit pairwise Mismatch sequence kernel. Its performance decreases as kernel (see Additional file Table S3). This fact turns such more mismatches are allowed inside a k-mer. Overall, both methods inadequate for contexts in which subsampling of versions of AERS, SIMCOMP, GIP, MINIMAX and SIDER pairs is undesirable.
drug kernels showed better performance, while LAMBDA, We now discuss about computational time in the pair MARG, SPEC and TAN performed worse. For targets, GIP, prediction scenario. The precomputed kernels approaches GO, MIS-k4m1, SPEC and SW kernels performed better (MEAN and KA) were overall the fastest on average, than other target kernels.
with PKM-based methods requiring less time to train and Table 4 Average ranking over all four datasets
In this section, we compare the competing methods interms of AUPR for all datasets. Concerning KronRLS, we will use the best kernel pair (Best Pair) with largest AUPR as described in the previous section. This will serve as a baseline to evaluate the MKL approaches. Results are presented in Table In the pair prediction scenario, KRONRLS-MKL obtained highest AUPR in all datasets. Itsresults are even superior than the performance in compar- ison to the best kernel pair under the optimistic selection.
The results of KRONRLS-MKL in pair prediction are statis- tically significant against all other methods (at α = 0.05), except from KRONRLS-KA and KRONRLS-MEAN, accord- ing to the Wilcoxon rank sum test (Additional file Con- cerning the subsampled pair prediction, KRONRLS-MKLachieved highest AUPR in the NR and IC data sets, and SITAR performed best in the GPCR and Enzyme data. There it performed second, just after SITAR (see Additional file Table S1). The highest AUPR values obtained in the subsampled data sets in comparison to the unbalanced data sets clearly indicate that performing predictions in the complete data is a more difficult task.
Moreover, the number of positive examples was negatively correlated to the dataset size for the complete datasets.
In the 'new target' scenario, BLM-KA performed best in 3 of 4 datasets, followed closely by BLM-mean and KRONRLS-MKL, demonstrating that the local SVM model † best on training is more effective in such scenario. BLM-KA performed ∗ best on testing Nascimento et al. BMC Bioinformatics (2016) 17:46 test the models (∼1 min), followed by KronRLS-based datasets were generated almost eight years ago, new and LapRLS-based algorithms(∼20 and 27 min, respec- interactions included in these databases will serve as a tively). KBMF2MKL and BLM were the slowest, requiring external validation set. We exclude interactions already more than 100 min on average at the same task. The present in the training data.
lower computation time of the heuristic-based methods is We trained all methods with all interactions present explained by the absence of complex optimization proce- in the original datasets. In the specific case of BLM dures to find the kernel weights. KronRLS-MKL took a lit- and NRWRH, one model for drugs and another for tar- tle less time than KBMF2MKL, taking an average over the gets was trained, and then the maximum score for each four datasets of 74 min. (see Additional file Table S4).
DT pair was considered for prediction. Then, we cal-culated the AUPR for each dataset separately, discard- Predictions on new drug-target interactions
ing already known interactions (see Additional file In order to evaluate the quality of final predictions in Table S2). The low AUPR values of all methods indi- a more realistic scenario, we performed an experiment cate the difficulty in performing predictions in such large similar to that described by We estimate the search space. An average ranking (Fig. of each method most highly ranked drug–target pairs as most likely true across all databases indicates that KronRLS methods as interactions, and performed a search on the current best performing algorithms followed by single kernel release of four major databases (DrugBank MATA- approaches. It is also important to highlight the poor DOR KEGG and ChEMBL As the training performance of BLM-KA and BLM-MEAN in this task.
Fig. 3 Mean AUPR ranking of each method when compared to the new interactions found on updated databases. The KronRLS-based methods
achieved superior performance when compared to other integration strategies
Nascimento et al. BMC Bioinformatics (2016) 17:46 This indicates a poor generalization capacity of the BLM (SW mean value is 0.1563). In addition, one of the targets framework to the drug-target prediction problem (see RORa is NR0B1 (nuclear receptor subfamily 0, group B, member 1). This protein is very close to RORa in the PPI Next, a more practical assessment of the predicting network (similarity score of 0.90).
power of KRONRLS-MKL is done, by looking to the top Concerning Ion Channel models, prediction ranked 2 5 ranked interactions predicted by our method (Table and 3 indicate the interaction of Verapamil and Diazox- We observe that the great majority of interactions (14 out ide with ATP-binding cassete sub-family C (ABBCC8).
of 20) have been already described in ChEMBL, Drug- ABBCC8 is one of the proteins encoding the sulfonylurea Bank or Matador. We focus our discussion in selected receptor (SUR1) and is associated to calcium regulation novel interactions. For example, in the Nuclear Receptor and diabetes type I Interestingly, there are positive database, the 5th ranked prediction indicates the asso- reports of Diazoxide treatments to prevent diabetes in rats ciation of Tretinoin with the nuclear factor RAR-related orphan receptor A (RORa). Tretinoin is a drug currentlyused to treatment of acnes Interestingly, its molec- Evaluation of kernel weigths
ular activity is associated with the activation of nuclear The kernel weights given by KBMF2MKL, KRONRLS-MKL receptors of the closelly related RAR family.
and WANG-MKL, as well as the KA heuristic, can be This is also a good example to illustrate the benefits for used to analyze the ability of such methods to identify incorporation of multiple sources of data. Both RORa and the most relevant information sources. As there is no Tretinoin do not share nodes in the training set. All tar- guideline or gold standard for this, we resort to a sim- gets of Tretinoin have a high GO similarity to RORa (mean ple approach: compare the kernel weights (Fig. with value of 0.8368) despite of theirr low sequence similarity the average performance of each kernel on the single Table 5 Top five predicted interactions by KRONRLS-MKL
Nuclear Receptors estrogen receptor 1 estrogen receptor 1 estrogen receptor 1 RAR-related orphan receptor A adrenoceptor beta 2, surface dopamine receptor D3 adenosine A2a receptor adenosine A1 receptor adrenoceptor beta 3 glutamate receptor, ionotropic, kainate 2 ATP-binding cassette, sub-family C (CFTR ATP-binding cassette, sub-family C (CFTR potassium channel, two pore domain subfamily K, member 12 cholinergic receptor, nicotinic, alpha 1 (muscle) cytochrome P450, family 2, subfamily E, polypeptide 1 cytochrome P450, family 2, subfamily C, polypeptide 9 cytochrome P450, family 2, subfamily A, polypeptide 7 cytochrome P450, family 4, subfamily A, polypeptide 11 cytochrome P450, family 1, subfamily A, polypeptide 1 Interactions found in KEGG, DrugBank, ChEMBL and Matador are marked as K, D, C and M respectively Nascimento et al. BMC Bioinformatics (2016) 17:46 Fig. 4 Comparison of the average final weights obtained by the Kernel Alignment (KA) heuristic, KBMF2MKL, KronRLS-MKL and WANG-MKL
algorithms. As one can note, the KA heuristic demonstrated close to mean weights, while KRONRLS-MKL and WANG-MKL effectively discarded
the most irrelevant kernels
kernel experiments (Fig. First, it is noticeable that for the lower quality MIS-k3m2 in three of the four the KA weights are very similar to the average selec- tion (0.10). This indicates that no clear kernel selectionis performed. WANG-MKL and KRONRLS-MKL give low weights to drug kernels LAMBDA, MARG, MINIMAX, SPEC We have presented a new Multiple Kernel Learning algo- and TAN and protein kernel MIS-k3m2. These kernels rithm for the bipartite link prediction problem, which is have overall worst AUPR in the single kernel experiments, able to identify and select the most relevant information which indicates an agreement with both selection pro- sources for DTI prediction. Most previous MKL methods cedures. Although the weights assigned by KBMF2MKL mainly solve the problem of MKL when kernels are built are not subject to convex constraints, as indicated by over the same set of entities, which is not the case for the larger weights assigned to all kernels, they also pro- the bipartite link prediction problem, e.g. drug-target net- vide a notion of quality of base kernels. We can observe works. Regarding predictions in drug-target networks, the a stronger preference to the GIP kernel, in all datasets, sampling of negative/unknown examples, as a way to cope even though the algorithm assigned a high weight with large data sets, is a clear limitation . Our method Nascimento et al. BMC Bioinformatics (2016) 17:46 takes advantage of the KronRLS framework to efficiently perform link prediction on data with arbitrary size.
Center of Informatics, UFPE, Recife, Brazil. 2Department of Statistics and Informatics, UFRPE, Recife, Brazil. 3IZKF Computational Biology Research Group, In our experiments, the KronRLS-MKL algorithm Institute for Biomedical Engineering, RWTH Aachen University Medical School, demonstrated an interesting balance between accuracy Aachen, Germany. 4Aachen Institute for Advanced Study in Computational and computational cost in relation to other approaches.
Engineering Science (AICES), RWTH Aachen University, Aachen, Germany.
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Subantimicrobial-dose doxycycline modulates gingival crevicular fluid biomarkers of periodontitis in postmenopausal osteopenic women

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