BACKGROUND
micrornas   <cit>  are single-stranded, endogenous ~22nt small non-coding rnas  that can play important regular roles in animals and plants by targeting mrna for cleavage or post-translation repression  <cit> .
mature mirnas are derived from longer precursors , each of which can fold into a hairpin structure that contains one or two mature mirnas in either or both its arms.
accordingly, mirna biogenesis is highly regulated, controlled at both transcriptional and post-transcriptional levels  <cit> , and overexpression and underexpression of mirnas are linked to various human diseases, particularly cancers  <cit> .
mirnas are always located in the introns of protein-coding genes  <cit> , introns and exons of non-coding genes  <cit> .
in mammalian genomes, it is also possible to find mirnas in repetitive regions, and some studies suggest that transposable elements may be involved in the creation of new mirnas  <cit> .
mirna biogenesis in animals contains two steps  <cit> .
in the first step, the primary mirna , which is several hundred nucleotides long, is processed in the nucleus by a multiprotein complex containing an enzyme called drosha to produce the ~70nt long mirna stem-loop precursor , which is then exported to the cytoplasm.
in the cytoplasm, the second step takes place where the pre-mirna matures into a ~22nt long mirna:mirna* duplex, with each strand originating from opposite arms of the stem-loop  <cit> .
then, the mirna strand of the mirna:mirna* duplex is loaded into a ribonucleoprotein complex known as the mirna-induced silencing complex .
until recently, the mirna* was thought to be peeled away and degraded.
however, some studies indicate that mirna* is also sorted into argonauts and might have a regular function in drosophila melanogaster  <cit> .
identification of mirna genes is an eminent and challenging problem towards the understanding of post-transcriptional gene regulation.
the short length of mirnas and their ability to act redundantly or to have only a subtle phonotypical impact impose a limitation to the use of mutagenesis and other conventional genetics techniques  <cit> .
direct cloning is the initial choice, but only abundant mirna genes can be easily detected.
since not all mirnas are well expressed in many tissues, mirnas that have very low expression levels or that are expressed tissue-specifically possibly can not be detected, and recently research suggests that lowly expressed human mirna genes evolve rapidly  <cit> .
this situation is partially mitigated by the deep-sequencing techniques that nevertheless require extensive computational analysis to distinguish mirnas from other small non-coding rnas of the same size  <cit> .
therefore, computational approaches are essential for mirna gene finding in sequenced genomes.
in these years, large-scale computational approaches have been developed, such as filter-based approaches  <cit> , homology-based research  <cit> , mixed approaches  <cit> , and machine learning methods.
filter-based approaches , focusing on identifying high-quality sets of conserved mirna candidates, are able to recover a substantial part of the known mirnas.
however, they are critically dependent on conservation criteria to obtain reasonable specificity.
homology-based approaches  rely exclusively either on sequence conservation or structure conservation so that lineage- or species-specific mirna genes may escape the detection.
in fact, many mirna gene prediction approaches incorporate a homology search as part of their protocols, in addition to the ordinary search for orthologous.
mixed approaches  combine experimental with computational procedures in order to identify a wider range of mirnas.
as mentioned above, experimental approaches cannot easily detect low-expression or tissue-specific mirnas.
the most popular computational mirna gene finding methods are machine learning based approaches.
most of them share the same overall strategy but use different approaches to identify good stem-loop candidates, since they all try to generalize a positive set of already known mirnas and a negative set of stem-loops that are not pre-mirnas  <cit> .
several machine learning methods have been proposed to tackle the problem of identifying mirna genes.
svm is a popular framework used to learn the distinctive characteristics of mirnas.
there are other machine learning methods that employ techniques such as hmm   <cit> , random forests  <cit> , naïve bayes classifier  <cit> , and random walk algorithm  <cit>  etc.
most approaches use sets of features including sequence conservation  <cit> , topological properties  <cit> , thermodynamic stability  <cit> , and some other properties like entropy measures  <cit> .
however, there are two major drawbacks with the existing machine learning based mirnas identification approaches.
one drawback is raised by the imbalance of positive and negative examples used.
since the real number of mirnas in any given genome is still an open problem, it is assumed that there is a very few mirna precursors in any randomly chosen stem-loop extracted from the genome.
positive examples are usually selected from mirnas identified by experimental procedures or other computational methods.
and the number of positive examples we can obtain is substantially smaller than that of negative examples.
the imbalance issue between positive and negative examples can greatly degrade the performance of current machine learning approaches.
certainly, with a growing number of mirnas being identified, we can expect an increasingly better performance from these methods.
the other drawback lies in the fact that most existing machine learning based methods  <cit>  make a few structural assumptions concerning stem length, loop size and number, as well as minimum free energy .
therefore, sequences with multi-branched loops secondary structure or mfe higher than - <dig> kal/mol possibly can not be predicted by those methods, which subsequently degrade the prediction performance.
we have investigated homo sapiens mirnas in mirbase  <cit> , and found that there are an increasing number of pre-mirnas, which do not satisfy the above-mentioned assumptions .
in this paper, we still treat the mirna gene finding problem as a classification problem, and develop a powerful classification system, named mirensvm, to overcome the two drawbacks mentioned above.
on one hand, mirensvm uses ensemble learning to deal with the imbalance issue; on the other hand, in addition to the features exploited by the existing methods, mirensvm further includes the multi-loop features in its classifiers, and f-score is used to select final classification features.
as a result, mirensvm can achieve better performance than the existing methods.
in summary, mirensvm distinct itself from the existing methods at least in three aspects:  lower expression and tissue-specific mirnas can be easily identified since different types of features are use.
due to using ensemble svm classifiers, both positive and negative examples can be exploited as many as possible.
no structural assumption for mirna candidates is made.
particularly, multi-loop features are considered.
RESULTS
results of different features sets
we used  <dig> local and global features that are subsumed into three groups, which capture mirna's sequence, secondary structure and thermodynamic properties respectively.
in this section, we used single svm classifier to check how different feature sets impact classification performance.
first, we trained a single svm classifier with the entire training dataset to examine prediction performance by using each of the three features group separately.
the classification performance is evaluated by the outer 3-fold cross validation method, which has been described in the method section.
the results are listed in table  <dig>  among the three feature subsets, the base pair group gets the highest se , while the thermodynamic group archives the best sp , gm  and acc .
the triplet elements group obtains a good sp , but its se is only  <dig> %, which is much lower than that of the other two groups.
from table  <dig>  we can see that: 1) thermodynamics features are more discriminative than structure and sequence features in identifying mirna precursors.
similar result was also obtained in  <cit> .
2) base pair features are more useful in predicting real pre-mirnas, since base pair group gets the highest sensitivity.
3) the four multi-loop features introduced in mirensvm are effective in predicting pre-mirnas with multi-loops, considering that nearly 84% pseudo pre-mirnas and  <dig> % real pre-mirnas have secondary structure with multi-loops.
second, all the  <dig> features were used to train a single svm classifier with the whole training dataset, and the performance was also evaluated by the outer 3-fold cross validation method.
the results are se  and gm , which are a little better than the best results of using any individual features group.
this indicates that the combination of different kinds of features can improve classification performance.
the next step is to improve the prediction speed without degrading the accuracy rate.
we thus considered feature selection method to select the intrinsic ones from all the  <dig> features.
feature selection is often applied to high dimensional data prior to classification learning.
this procedure can reduce not only the cost of recognition by reducing the number of features that need to be collected, but in some cases it can also provide a better classification accuracy due to the finite sample size effect  <cit> .
here, we used f-score to select the best feature subset for our mirensvm.
this procedure is implemented by the libsvm's feature selection tool.
we evaluated the effectiveness of the feature subset selected by f-score method by training a single svm classifier on the entire training set, and studying the sensitivity and the number of correctly predicted mirnas.
all the results of these experiments are summarized in table  <dig>  as shown in table  <dig>  after feature selection, the classification performance becomes better.
at last, the  <dig> features with the largest f-scores were used to train the mirensvm classifier.
this feature set contains  <dig> features from the triplet elements group,  <dig> features from the base pair group and  <dig> features from the thermodynamic group.
experimental results show that the  <dig> selected features subset not only obtains the highest classification results, but also greatly reduces the outer and inner cross-validation training time taken by svm ensembles, especially when conducting class imbalance learning experiments presented in the next section.
table  <dig> lists all features used in the final svm ensembles.
results of svm ensembles
in this section we will present the experimental results of our mirensvm approach.
two schemes, majority vote and mean distance  were applied to aggregating the results of each sub svm classifier.
since the ratio of negative samples to positive samples is  <dig> : <dig>  the cases of k= <dig>   <dig>   <dig>   <dig> or  <dig> were tested, respectively.
we found that sub svm classifiers trained with negative samples which are closer to the positive samples always achieve a lower se than the other cases.
and we called these datasets “closer set”.
with this observation, we perform majority vote when even number of sub classifiers are employed in our mirensvm.
that is, when k is even  and the test sample receives equal numbers of positive and negative votes, the latter half of sub svm classifiers takes priority over the former half trained with closer sets.
here, all experiments were conducted through the outer 3-fold cross validation method.
table  <dig> presents the average classification results of some svm ensembles experiments.
for each aggregation method, only the best two results are presented.
as shown in table  <dig>  both majority vote and mean distance get a better performance than using a single svm classifier developed with the  <dig> selected features .
compared with mean distance method, majority vote always archives higher sensitivity , but its specificity  is much lower, which impacts its overall accuracy .
if this type of classifier is used for real-life prediction, due to its lower specificity, the chance of incorrectly predicting random sequences with stem-loop like secondary structure would be quite high.
therefore, we choose the best classifier developed under the mean distance method as the final mirensvm classifier.
the mean distance method obtains the best classification results on our dataset, that is, the highest gm  with se= <dig> % and sp= <dig> %, and an acceptable acc .
there is another reason to choose mean distance, that is efficiency.
the ensemble svm classifier predicts each test sample only one time while each test sample has to be predicted k times under majority vote.
we then validated our mirensvm on the testing dataset.
this set contains  <dig> homo sapiens and  <dig> anopheles gambiae mirna precursor sequences newly published in mirbase <dig> .
the result shows that mirensvm obtains 100% accuracy.
particularly,  <dig> sequences  in the testing set whose mfe is higher than - <dig>  kal/mol are all predicted correctly by our mirensvm.
in order to further demonstarte the advantage of the mirensvm approach, we tested our mirensvm on the mirbase <dig>  and achieved a high sensitivity.
mirbase <dig>  contains  <dig> animal genomes, including  <dig> mirna precursor sequences .
mirensvm correctly classified  <dig> %  pre-mirnas.
results of comparison with existing methods
we compared our approach with three existing methods that also used machine learning techniques to predict mirna precursors  <cit>  .
these three compared methods include triplet-svm, bayesmirnafind and micropred.
the results of these methods are obtained by predicting  <dig> sequences  that have been already used in developing our mirensvm.
this dataset contains two parts: 1/ <dig> training set  and the smaller testing set .
the results of these experiments are illustrated in figure  <dig>
triplet-svm was proposed by xue et al.
<cit>  to recognize pre-mirnas based on the triplet element structure-sequence features.
the method is trained on known human pre-mirnas and obtains a high accuracy  when applied to several other species.
unlike mirensvm, triplet-svm uses only structure-sequence information, and therefore can predict mirnas quickly.
however, this method is not designed to detect mirnas with multi-loop secondary structure or mirnas with high mfe.
triplet-svm predicts only  <dig>  sequences.
although it has an acceptable sensitivity , its specificity  is not comparable to ours .
bayesmirnafind was developed by yousef et al.
<cit> , which uses a different machine learning method, naïve bayes classifier, to predict mirnas conserved between human and mouse.
yousef et al.
applied their method to the forward strand of the mouse genome sequence and present results for different score cut offs.
bayesmirnafind is trained with cross-species dataset, which contains  <dig> different organisms.
results show that our mirensvm detects more already known pre-mirnas than bayesmirnafind : of the total  <dig> real pre-mirnas, bayesmirnafind correctly predicts  <dig>  while mirensvm correctly predicts  <dig>  most of the negative training samples  used in our mirensvm are also used to train bayesmirnafind.
bayesmirnafind detects  <dig> out of  <dig> sequences in 3'-utrdb and rfam, while mirensvm finds  <dig> of the same  <dig> sequences, thus mirensvm achieves a much higher specificity.
micropred is an svm-based method designed recently by rukshan and vasile to detect human mirna gene  <cit> .
like mirensvm, micropred uses  <dig> different features for svm classification, and employs smote to deal with the class imbalance problem.
although the features used in micropred is a little different from that in mirensvm, they also cover the sequence, structure and thermodynamics aspects of mirna precursors.
also trying to improve performance with an imbalance learning method, micropred achieves a sensitivity of little higher than our method: out of the  <dig> known mirnas in mirbase <dig> , micropred detects  <dig> and we detect  <dig>  however, micropred predicts 516% more mirna candidates than mirensvm .
thus, mirensvm has a much higher specificity than micropred, although micropred specificity is estimated high.
the better performance of mirensvm is possibly due to the features used in the classification system.
considering that a large number of pseudo stem-loop sequences have secondary structure with multi-loops, micropred uses only one multi-loop relevant feature , while mirensvm uses four /n_loops, %/n_loops).
discussion
the mirensvm was first trained on homo sapiens and anopheles gambiae genomes, and got  <dig> % sensitivity,  <dig> % specificity and  <dig> % accuracy via outer 3-fold cross validation method.
we then applied it to detect new mirnas of hsa and aga genome in mirbase <dig> .
all  <dig> new pre-mirnas were correctly detected.
to further demonstrate the advantage of our approach, we tested mirensvm on  <dig> additional animal genomes registered in mirbase <dig>  out of the  <dig> animal pre-mirnas across the  <dig> other species, mirensvm correctly identified  <dig>  i.e, the recognition rate is  <dig> %.
the approach outperformed another recently published method  <cit>  in detecting mirna precursors with multi-branched loops, and obtained higher and more reliable results than the existing methods  <cit> , while there is a little overlap among sets of mirna candidates predicted by the different methods.
since the number of possible candidate hairpins within the whole genome is very large and the number of real pre-mirna is still small for some species, current specificity is still not satisfactory for multi-genomes applications and some false positive predictions can be produced.
finding more information to reduce the false positive rate should be further investigated.
however, latest reports suggested that some human mirna precursors have box h/aca snorna features  <cit> .
it might be necessary for us to reconsider those previously regarded as false-positive predictions, since our dataset contains a certain amount of hsa and aga snornas.
CONCLUSIONS
in this study, we presented mirensvm, a svm-based computational approach that detects real mirna precursors from pseudo ones with their intrinsic features.
mirensvm uses both global and local intrinsic features of known mirnas as its input.
several machine learning technologies including feature selection, imbalance learning and multi-classification were applied.
our approach is more general than the existing methods, since it is not sensitive to pre-mirna's structure and thermodynamic characteristics.
and it can achieve better prediction performance than the existing methods.

 micrornas are single-stranded, endogenous ~22nt small non-coding rnas that can play important regular roles in animals and plants by targeting mrna for cleavage or post-translation repression. mature mirnas are derived from longer precursors, each of which can fold into a hairpin structure that contains one or two mature mirnas in either or both its arms. accordingly, mirna biogenesis is highly regulated, controlled at both transcriptional and post-transcriptional levels, and overexpression and underexpression of mirnas are linked to various human diseases, particularly cancers. mirnas are always located in the introns of protein-coding genes, introns and exons of non-coding genes. in mammalian genomes, it is also possible to find mirnas in repetitive regions, and some studies suggest that transposable elements may be involved in the creation of new mirnas. mirna biogenesis in animals contains two steps. in the first step, the primary mirna, which is several hundred nucleotides long, is processed in the nucleus by a multiprotein complex containing an enzyme called drosha to produce the ~70nt long mirna stem-loop precursor, which is then exported to the cytoplasm. in the cytoplasm, the second step takes place where the pre-mirna matures into a ~22nt long mirna:mirna* duplex, with each strand originating from opposite arms of the stem-loop. then, the mirna strand of the mirna:mirna* duplex is loaded into a ribonucleoprotein complex known as the mirna-induced silencing complex. until recently, the mirna* was thought to be peeled away and degraded. however, some studies indicate that mirna* is also sorted into argonauts and might have a regular function in drosophila melanogaster. identification of mirna genes is an eminent and challenging problem towards the understanding of post-transcriptional gene regulation. the short length of mirnas and their ability to act redundantly or to have only a subtle phonotypical impact impose a limitation to the use of mutagenesis and other conventional genetics techniques. direct cloning is the initial choice, but only abundant mirna genes can be easily detected. since not all mirnas are well expressed in many tissues, mirnas that have very low expression levels or that are expressed tissue-specifically possibly can not be detected, and recently research suggests that lowly expressed human mirna genes evolve rapidly. this situation is partially mitigated by the deep-sequencing techniques that nevertheless require extensive computational analysis to distinguish mirnas from other small non-coding rnas of the same size. therefore, computational approaches are essential for mirna gene finding in sequenced genomes. in these years, large-scale computational approaches have been developed, such as filter-based approaches, homology-based research, mixed approaches, and machine learning methods. filter-based approaches, focusing on identifying high-quality sets of conserved mirna candidates, are able to recover a substantial part of the known mirnas. however, they are critically dependent on conservation criteria to obtain reasonable specificity. homology-based approaches rely exclusively either on sequence conservation or structure conservation so that lineage- or species-specific mirna genes may escape the detection. in fact, many mirna gene prediction approaches incorporate a homology search as part of their protocols, in addition to the ordinary search for orthologous. mixed approaches combine experimental with computational procedures in order to identify a wider range of mirnas. as mentioned above, experimental approaches cannot easily detect low-expression or tissue-specific mirnas. the most popular computational mirna gene finding methods are machine learning based approaches. most of them share the same overall strategy but use different approaches to identify good stem-loop candidates, since they all try to generalize a positive set of already known mirnas and a negative set of stem-loops that are not pre-mirnas. several machine learning methods have been proposed to tackle the problem of identifying mirna genes. svm is a popular framework used to learn the distinctive characteristics of mirnas. there are other machine learning methods that employ techniques such as hmm, random forests, naïve bayes classifier, and random walk algorithm etc. most approaches use sets of features including sequence conservation, topological properties, thermodynamic stability, and some other properties like entropy measures. however, there are two major drawbacks with the existing machine learning based mirnas identification approaches. one drawback is raised by the imbalance of positive and negative examples used. since the real number of mirnas in any given genome is still an open problem, it is assumed that there is a very few mirna precursors in any randomly chosen stem-loop extracted from the genome. positive examples are usually selected from mirnas identified by experimental procedures or other computational methods. and the number of positive examples we can obtain is substantially smaller than that of negative examples. the imbalance issue between positive and negative examples can greatly degrade the performance of current machine learning approaches. certainly, with a growing number of mirnas being identified, we can expect an increasingly better performance from these methods. the other drawback lies in the fact that most existing machine learning based methods make a few structural assumptions concerning stem length, loop size and number, as well as minimum free energy. therefore, sequences with multi-branched loops secondary structure or mfe higher than - kal/mol possibly can not be predicted by those methods, which subsequently degrade the prediction performance. we have investigated homo sapiens mirnas in mirbase, and found that there are an increasing number of pre-mirnas, which do not satisfy the above-mentioned assumptions. in this paper, we still treat the mirna gene finding problem as a classification problem, and develop a powerful classification system, named mirensvm, to overcome the two drawbacks mentioned above. on one hand, mirensvm uses ensemble learning to deal with the imbalance issue; on the other hand, in addition to the features exploited by the existing methods, mirensvm further includes the multi-loop features in its classifiers, and f-score is used to select final classification features. as a result, mirensvm can achieve better performance than the existing methods. in summary, mirensvm distinct itself from the existing methods at least in three aspects: lower expression and tissue-specific mirnas can be easily identified since different types of features are use. due to using ensemble svm classifiers, both positive and negative examples can be exploited as many as possible. no structural assumption for mirna candidates is made. particularly, multi-loop features are considered.  results of different features sets we used local and global features that are subsumed into three groups, which capture mirna's sequence, secondary structure and thermodynamic properties respectively. in this section, we used single svm classifier to check how different feature sets impact classification performance. first, we trained a single svm classifier with the entire training dataset to examine prediction performance by using each of the three features group separately. the classification performance is evaluated by the outer 3-fold cross validation method, which has been described in the method section. the results are listed in table among the three feature subsets, the base pair group gets the highest se, while the thermodynamic group archives the best sp, gm and acc. the triplet elements group obtains a good sp, but its se is only %, which is much lower than that of the other two groups. from table we can see that: 1) thermodynamics features are more discriminative than structure and sequence features in identifying mirna precursors. similar result was also obtained in. 2) base pair features are more useful in predicting real pre-mirnas, since base pair group gets the highest sensitivity. 3) the four multi-loop features introduced in mirensvm are effective in predicting pre-mirnas with multi-loops, considering that nearly 84% pseudo pre-mirnas and % real pre-mirnas have secondary structure with multi-loops. second, all the features were used to train a single svm classifier with the whole training dataset, and the performance was also evaluated by the outer 3-fold cross validation method. the results are se and gm, which are a little better than the best results of using any individual features group. this indicates that the combination of different kinds of features can improve classification performance. the next step is to improve the prediction speed without degrading the accuracy rate. we thus considered feature selection method to select the intrinsic ones from all the features. feature selection is often applied to high dimensional data prior to classification learning. this procedure can reduce not only the cost of recognition by reducing the number of features that need to be collected, but in some cases it can also provide a better classification accuracy due to the finite sample size effect. here, we used f-score to select the best feature subset for our mirensvm. this procedure is implemented by the libsvm's feature selection tool. we evaluated the effectiveness of the feature subset selected by f-score method by training a single svm classifier on the entire training set, and studying the sensitivity and the number of correctly predicted mirnas. all the results of these experiments are summarized in table as shown in table after feature selection, the classification performance becomes better. at last, the features with the largest f-scores were used to train the mirensvm classifier. this feature set contains features from the triplet elements group, features from the base pair group and features from the thermodynamic group. experimental results show that the selected features subset not only obtains the highest classification results, but also greatly reduces the outer and inner cross-validation training time taken by svm ensembles, especially when conducting class imbalance learning experiments presented in the next section. table lists all features used in the final svm ensembles. results of svm ensembles in this section we will present the experimental results of our mirensvm approach. two schemes, majority vote and mean distance were applied to aggregating the results of each sub svm classifier. since the ratio of negative samples to positive samples is: the cases of k= or were tested, respectively. we found that sub svm classifiers trained with negative samples which are closer to the positive samples always achieve a lower se than the other cases. and we called these datasets “closer set”. with this observation, we perform majority vote when even number of sub classifiers are employed in our mirensvm. that is, when k is even and the test sample receives equal numbers of positive and negative votes, the latter half of sub svm classifiers takes priority over the former half trained with closer sets. here, all experiments were conducted through the outer 3-fold cross validation method. table presents the average classification results of some svm ensembles experiments. for each aggregation method, only the best two results are presented. as shown in table both majority vote and mean distance get a better performance than using a single svm classifier developed with the selected features. compared with mean distance method, majority vote always archives higher sensitivity, but its specificity is much lower, which impacts its overall accuracy. if this type of classifier is used for real-life prediction, due to its lower specificity, the chance of incorrectly predicting random sequences with stem-loop like secondary structure would be quite high. therefore, we choose the best classifier developed under the mean distance method as the final mirensvm classifier. the mean distance method obtains the best classification results on our dataset, that is, the highest gm with se= % and sp= %, and an acceptable acc. there is another reason to choose mean distance, that is efficiency. the ensemble svm classifier predicts each test sample only one time while each test sample has to be predicted k times under majority vote. we then validated our mirensvm on the testing dataset. this set contains homo sapiens and anopheles gambiae mirna precursor sequences newly published in mirbase. the result shows that mirensvm obtains 100% accuracy. particularly, sequences in the testing set whose mfe is higher than - kal/mol are all predicted correctly by our mirensvm. in order to further demonstarte the advantage of the mirensvm approach, we tested our mirensvm on the mirbase and achieved a high sensitivity. mirbase contains animal genomes, including mirna precursor sequences. mirensvm correctly classified % pre-mirnas. results of comparison with existing methods we compared our approach with three existing methods that also used machine learning techniques to predict mirna precursors. these three compared methods include triplet-svm, bayesmirnafind and micropred. the results of these methods are obtained by predicting sequences that have been already used in developing our mirensvm. this dataset contains two parts: 1/ training set and the smaller testing set. the results of these experiments are illustrated in figure triplet-svm was proposed by xue et al. to recognize pre-mirnas based on the triplet element structure-sequence features. the method is trained on known human pre-mirnas and obtains a high accuracy when applied to several other species. unlike mirensvm, triplet-svm uses only structure-sequence information, and therefore can predict mirnas quickly. however, this method is not designed to detect mirnas with multi-loop secondary structure or mirnas with high mfe. triplet-svm predicts only sequences. although it has an acceptable sensitivity, its specificity is not comparable to ours. bayesmirnafind was developed by yousef et al., which uses a different machine learning method, naïve bayes classifier, to predict mirnas conserved between human and mouse. yousef et al. applied their method to the forward strand of the mouse genome sequence and present results for different score cut offs. bayesmirnafind is trained with cross-species dataset, which contains different organisms. results show that our mirensvm detects more already known pre-mirnas than bayesmirnafind: of the total real pre-mirnas, bayesmirnafind correctly predicts while mirensvm correctly predicts most of the negative training samples used in our mirensvm are also used to train bayesmirnafind. bayesmirnafind detects out of sequences in 3'-utrdb and rfam, while mirensvm finds of the same sequences, thus mirensvm achieves a much higher specificity. micropred is an svm-based method designed recently by rukshan and vasile to detect human mirna gene. like mirensvm, micropred uses different features for svm classification, and employs smote to deal with the class imbalance problem. although the features used in micropred is a little different from that in mirensvm, they also cover the sequence, structure and thermodynamics aspects of mirna precursors. also trying to improve performance with an imbalance learning method, micropred achieves a sensitivity of little higher than our method: out of the known mirnas in mirbase, micropred detects and we detect however, micropred predicts 516% more mirna candidates than mirensvm. thus, mirensvm has a much higher specificity than micropred, although micropred specificity is estimated high. the better performance of mirensvm is possibly due to the features used in the classification system. considering that a large number of pseudo stem-loop sequences have secondary structure with multi-loops, micropred uses only one multi-loop relevant feature, while mirensvm uses four /n_loops, %/n_loops). discussion the mirensvm was first trained on homo sapiens and anopheles gambiae genomes, and got % sensitivity, % specificity and % accuracy via outer 3-fold cross validation method. we then applied it to detect new mirnas of hsa and aga genome in mirbase. all new pre-mirnas were correctly detected. to further demonstrate the advantage of our approach, we tested mirensvm on additional animal genomes registered in mirbase out of the animal pre-mirnas across the other species, mirensvm correctly identified i.e, the recognition rate is %. the approach outperformed another recently published method in detecting mirna precursors with multi-branched loops, and obtained higher and more reliable results than the existing methods, while there is a little overlap among sets of mirna candidates predicted by the different methods. since the number of possible candidate hairpins within the whole genome is very large and the number of real pre-mirna is still small for some species, current specificity is still not satisfactory for multi-genomes applications and some false positive predictions can be produced. finding more information to reduce the false positive rate should be further investigated. however, latest reports suggested that some human mirna precursors have box h/aca snorna features. it might be necessary for us to reconsider those previously regarded as false-positive predictions, since our dataset contains a certain amount of hsa and aga snornas.  in this study, we presented mirensvm, a svm-based computational approach that detects real mirna precursors from pseudo ones with their intrinsic features. mirensvm uses both global and local intrinsic features of known mirnas as its input. several machine learning technologies including feature selection, imbalance learning and multi-classification were applied. our approach is more general than the existing methods, since it is not sensitive to pre-mirna's structure and thermodynamic characteristics. and it can achieve better prediction performance than the existing methods. 
a number of computational methods for mirna genes finding have been proposed in the past decade, yet the problem is far from being tackled, especially when considering the imbalance issue of known mirnas and unidentified mirnas, and the pre-mirnas with multi-loops or higher minimum free energy. aiming at better prediction performance, an ensemble support vector machine classifier is established to deal with the imbalance issue, and multi-loop features are included for identifying those pre-mirnas with multi-loops. we collected a representative dataset, which contains real mirna precursors identified by experimental procedure and other computational methods, and pseudo ones from several datasets. mirensvm is an ensemble support vector machine classification system for better detecting mirna genes, especially those with multi-loop secondary structure. compared with the state-of-the-art approaches, mirensvm obtained better prediction results. this paper presents a new computational approach, mirensvm, for finding mirna genes.
 micrornas are derived from larger hairpin rna precursors and play essential regular roles in both animals and plants. a number of computational methods for mirna genes finding have been proposed in the past decade, yet the problem is far from being tackled, especially when considering the imbalance issue of known mirnas and unidentified mirnas, and the pre-mirnas with multi-loops or higher minimum free energy. this paper presents a new computational approach, mirensvm, for finding mirna genes. aiming at better prediction performance, an ensemble support vector machine classifier is established to deal with the imbalance issue, and multi-loop features are included for identifying those pre-mirnas with multi-loops.  we collected a representative dataset, which contains real mirna precursors identified by experimental procedure and other computational methods, and pseudo ones from several datasets. experiments showed that our mirensvm achieved a % specificity and a % sensitivity on the dataset. compared with the state-of-the-art approaches, mirensvm obtained better prediction results. we also applied our method to predict homo sapiens pre-mirnas and anopheles gambiae pre-mirnas that first appeared in mirbase, mirensvm got a 100% prediction rate. furthermore, performance evaluation was conducted over additional species in mirbase, and % animal pre-mirnas were correctly identified by mirensvm.  mirensvm is an ensemble support vector machine classification system for better detecting mirna genes, especially those with multi-loop secondary structure. 16– december the 21st international conference on genome informatics hangzhou, people’s republic of china 