Figures
Abstract
Exploring novel computational methods in making sense of biological data has not only been a necessity, but also productive. A part of this trend is the search for more efficient in silico methods/tools for analysis of promoters, which are parts of DNA sequences that are involved in regulation of expression of genes into other functional molecules. Promoter regions vary greatly in their function based on the sequence of nucleotides and the arrangement of protein-binding short-regions called motifs. In fact, the regulatory nature of the promoters seems to be largely driven by the selective presence and/or the arrangement of these motifs. Here, we explore computational classification of promoter sequences based on the pattern of motif distributions, as such classification can pave a new way of functional analysis of promoters and to discover the functionally crucial motifs. We make use of Position Specific Motif Matrix (PSMM) features for exploring the possibility of accurately classifying promoter sequences using some of the popular classification techniques. The classification results on the complete feature set are low, perhaps due to the huge number of features. We propose two ways of reducing features. Our test results show improvement in the classification output after the reduction of features. The results also show that decision trees outperform SVM (Support Vector Machine), KNN (K Nearest Neighbor) and ensemble classifier LibD3C, particularly with reduced features. The proposed feature selection methods outperform some of the popular feature transformation methods such as PCA and SVD. Also, the methods proposed are as accurate as MRMR (feature selection method) but much faster than MRMR. Such methods could be useful to categorize new promoters and explore regulatory mechanisms of gene expressions in complex eukaryotic species.
Citation: K. K, P. G. L, Rangarajan L, K. AK (2016) Effective Feature Selection for Classification of Promoter Sequences. PLoS ONE 11(12): e0167165. https://doi.org/10.1371/journal.pone.0167165
Editor: Bin Liu, Harbin Institute of Technology Shenzhen Graduate School, CHINA
Received: July 13, 2016; Accepted: November 9, 2016; Published: December 15, 2016
Copyright: © 2016 Kouser et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work/authors received no specific funding. The private company Shodhaka Life Sciences Pvt. Ltd. provided support in the form of assistance to one of the authors [AKK] in extracting and compiling some of the data, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This commercial affiliation also does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.
Competing interests: The authors have declared that no competing interests exist, even though one of the authors (AKK) is affiliated to a private company (Shodhaka Life Sciences Pvt. Ltd) and this may be perceived by some as a potential conflict. But AKK is the founder director of this startup with focus on data analysis services for other scientists. Even though he has used the help of a staff member in extracting some of the data for the current work, the company has no commercial relevance to any part of the current study objectives or manuscript preparation.
Introduction
It is challenging to make sense out of the exponentially increasing biological data, particularly the nucleotide sequences. Efficient, robust, scalable analysis of biological data is the need of the hour as biological data is noisy and high dimension in nature [1]. Many new methods/techniques can now help in the process of extracting meaningful information from the sequences for better understanding of biomedical mechanisms [2] and to attempt solve specific biological problems. Promoter sequences consist of mainly non-coding sequences and usually have multiple transcription factor binding sites (TFBS)/motifs, which consist of specific types of patterns with 5–20 nucleotides [3]. Many researchers have earlier tried to use such features of promoters to predict and/or analyze them [4,5]. We have earlier attempted to analyze promoters using motif-frequency and alignments [6,7]. In this work, we have devised novel computational methods to analyze promoter sequences.
Exploring what constitutes a functional signal or property at the sequence level is the objective of many sequence analysis exercises. Often, classification of segments of sequences is useful for this type of analysis and thus classification techniques have become an integral part of biological data analysis [8]. The biological data is often huge in terms of dimension with comparatively less number of samples posing an inevitable challenge for classification methods to successfully identify classes. Several approaches like Decision Trees (DT), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Networks (ANN) have been found effective in the problem of classification of biological data [1]. General nucleotide feature extractions may also not help in comparing promoter sequences from complex eukaryotes. For example, repDNA [9] and repRNA [10] are useful tools for generating multiple features reflecting the physicochemical properties and sequence-order effects of nucleic acids. But, they have been neither designed to use information on TFBSs nor to compare two sets of sequences. Pse-in-One is a useful feature extraction software tool [11]. Pse DAC—General, a component of Pse-in-One, is a tool for finding various feature vectors out of a given DNA sequence. This tool takes as input, a DNA sequence and discovers features such as Kmer, RevKmer and features based on correlation between di/tri nucleotides. None of these are close to finding the features we need, which are all the motifs and their positions. Other two components, Pse RAC—General accepts RNA sequence as input and Pse AAC-General takes input of protein sequences. The method proposed analyses the sequence of motifs. Pse-in One is not designed to take this as input and hence is not suited for our type of analysis.
The inherent high dimension of the data leads to the problems of difficulty in analysis and inaccuracy in the results of analysis. This is mostly due to the noise, in the form of redundant information embedded in the features. Dimensionality reduction procedures are thus an essential step in the analysis of large dimension data sets. Feature selection and feature transformation are two common methods for this step of dimensionality reduction. Selection of features is a simple and often efficient technique. Although feature selection improves the performance of the data mining algorithm, there is always a possibility of missing out some important features in the process. There are several approaches proposed in literature for feature selection which can be categorized as filter methods, wrapper methods and embedded methods. Filter methods select a subset of the features irrespective of the classification model used, whereas wrapper methods consider the model hypotheses to select a feasible subset. The embedded approach is also classifier dependent but is computationally less expensive compared to wrappers [12]. In this work, the significant features, from the view point of getting a good classification, are selected by filtering.
The sequential nature of the features imposes constraints on classification of biological sequences, hence making it a challenging task as compared to classification of feature vectors [13]. There have been a number of successful attempts in the past for finding the similarity in the coding as well as the non-coding regions of the DNA sequences. The two major tasks involved in this process are alignment and analysis. A variety of computational models exist for alignment such as Bayesian Methods [14], Scoring Matrices [15,16], Dot Matrix [17], Dynamic Programming [18] and Genetic Algorithms [19]. Nevertheless, most of these methods are based on nucleotide comparisons, which can be useful in various contexts. Motifs / transcription factor binding sites (TFBSs) are known to be important patterns within the promoter sequences. Simple alignment of nucleotides will disperse the conserved regions of motifs and hence not suited for promoter comparisons. Analyzing distribution of motifs in the promoter regions, alignment of motifs are some ways of comparing promoters [20]. Study of simple distributions, such as frequency of occurrence of each motif across the promoter or simple alignment of motif sequences as traditionally done in coding regions with nucleotides, do not utilize or keep the important information of position of motifs in the promoter sequences.
The model proposed in this work uses the sequence of motifs as well as their positional information. A promoter is reduced to a matrix called Position Specific Motif Matrix (PSMM), where rows are motifs present in the promoters and columns are positions where these motifs are present. This PSMM written as a single row (concatenation of rows of PSMM) is the feature vector of a promoter. A matrix of feature vectors of all promoters is the feature matrix of the set of promoters and the classification is performed on the feature matrix.
Materials and Methods
This section describes the proposed methods and the data sets used to test the proposed methods.
Overall schema of the proposed model
The overall schema and flow of the method is as described in Fig 1. The construction of the PSMM for a promoter has been described earlier [7,21]. Using the PSMMs of all promoters, feature matrix is identified. Classification of promoters is performed using (i) all features (ii) features with high variances (iii) features with low P values and (iv) MRMR [22]. Also, classification is performed using transformed features such as PCA [23] and SVD [24] which are frequently used transformations in literature. We carried out experiments using these transformed features as a comparative study. In this work, we have experimented with three individual classification techniques viz., KNN, SVM and Decision Trees and an ensemble classifier named LibD3C [25].
Classification algorithms
There have been several attempts in the recent past to efficiently classify biological data to aid biologists in different tasks and solve some specific biological problems [26,27]. The classification capability is greatly influenced by the method adopted and the choice of parameters [28,29]. Some popular classification techniques like Bayesian classification, Hidden Markov Models [HMMs], Support Vector Machine [SVM] and Decision Trees [DT] have been used in the recent past for biological sequence classification. SVM is used for successful classification and validation of cancer tissues using the micro array expression data [30]. Recursive feature elimination based SVM (RFE-SVM) is yet another successful example in classification of gene expression data [31]. Human DNA sequence prediction is performed using the Bayesian classification in [14,32]. Motifs-based HMMs have been successfully employed for classification of genes using the promoter regions [33]. KNN is a lazy learning method that classifies an unseen sample by vote of k-nearest training instance by using a distance metric, typically Euclidean distance [34]. The choice of the distance measure is critical to the performance of KNN classifiers [13]. KNN estimates the density function for every target instance sample locally and differentially instead of estimating once for the entire instance space [34].
SVM is another popular classification method which is proven to be effective for sequence classification [35,36,37]. The two significant challenges encountered while using SVM for sequence classification are, definition of kernel functions and computational efficiency of kernel matrices [13]. SVM performs well when a simple kernel is used for a small data. Use of more complex kernels may become necessary when datasets containing more samples become available [30]. Weston et al [38] propose a semi-supervised protein classification method by incorporating a cluster kernel into the SVM and they claim that the cluster kernel works better by adding unlabeled data than when using only the labeled data.
The other method used in this work for classification is the Decision tree. Decision tree is one of the most popular technique used by the machine learning community in general [39,40] and particularly has applications in computational biology and bioinformatics because of their capability in aggregating diverse types of data to make an accurate prediction [41]. Decision trees are sometimes more interpretable and can be trained more efficiently than other classifiers like SVM and Neural Networks because they combine simple questions about the data in an understandable way [41]. Also, decision trees suffer less from the curse of dimensionality [39, 40]. However, small changes in the input data can sometimes lead to large change in the constructed tree.
LibD3C [25] is an ensemble classifier, this approach is a hybrid model of ensemble pruning that is based on k-means clustering and the framework of dynamic selection and circulating in combination with a sequential search method. Ensemble classifier pruning becomes useful in some applications, where the number of independent classifiers that are needed to achieve reasonable accuracy is enormously large [42].
Creation of feature matrix
The PSMM of a promoter/sample is a row in the feature matrix. The successive rows of PSMM are appended to get a single row in the feature matrix. The PSMM of the promoter/sample in the Fig 1 along with the PSMM of another sample is shown in Fig 2. The feature vectors of the two promoters 1 and 2 in Fig 2 are shown in Figs 3 and 4 respectively. In the proposed promoter analysis, position and frequencies of the transcription factor binding sites (TFBSs)/motifs are the features. The design of feature matrix keeps this information intact.
Reduction of features
Applying feature selection techniques in bioinformatics has become a prerequisite for model building [12]. The major advantages of feature selection are (i) it improves the performance of the model, (ii) it provides faster and more cost effective models and (iii) it helps gain a deeper insight into the underlying processes [12]. As feature selection merely selects a subset of features, it does not change the actual representation of the features [12], hence preserves the original semantics which can be easily interpreted by a domain expert [12]. MRMR [22] is one of the most robust feature selection techniques that is useful in various applications. MRMR-MIQ features compute the significance of each feature one by one and rank the features according to their significances in the descending order [43].
Often in classification problems, features are transformed and later features are selected in the transformed space. However, there are some advantages in reduction of original features. The reduced feature set can be useful information to the biologists, since it points to key motifs and their positions that are significant in differentiating the promoter sets. In the transformed space this kind of inference is not possible. PCA and SVD are methods of this type and frequently used in literature. PCA and SVD are the basic linear transformations of the input variables [24]. PCA extracts the components by maximizing the variance of a linear combination of the original features [23]. We have experimented and compared the efficiency of the proposed feature selection methods using these popular methods. The section next gives an overview of the proposed feature selection methods. For reasons mentioned above, our selection methods do not transform features.
Variance based reduction.
If the number of promoters considered for analysis is just two, then the feature matrix is as shown in Fig 5. Features are selected based on variance. We find variance of every column (features) and those that are highly variant are selected. Total variance in all features is computed. Features are then added on to the selected set in the decreasing order of their variations until a specified threshold P% of variation is covered as described in Eq (2). Rest of the features are ignored. By doing so, we select not only motifs but also specific regions of the motif. A motif in a specific position may get selected and the same motif in some other positions may be ignored.
Suppose, v1, v2, v3, v4 ……. vn are the variances of ‘n’ features. Then, the total variance (TV)of the n features is given by,
(1)
Let j1, j2, j3, j4 ……………. jk be the features selected, where k<< n.
Then, Var j1 ≥ Var j2 ≥ Var j3 ≥ Var j4…………………≥Var jk
(2)
and
For example, in the feature matrix in Fig 5, the total variance is 8.5. Let P = 50%, then 50% of 8.5 is 4.25. Hence, only the features ‘MA0041.1’ in band 0–50, ‘MA0072.1’ in band 51–100 and ‘MA0258.1’ in band 201–250 are selected since the sum of variations is 6, which is just greater than 4.25, the threshold for selection in this case. Thus, ‘MA0041.1’is selected in the region 0–50 whereas the same motif is ignored in other regions.
Advantage of variance based reduction of features is, it is computationally simple and generally it works very well for moderately separated classes. If the data is known to have a lot of overlap of classes, T test based reduction will perform better than simple variance based reduction. This is because individual class means and variances are used in the process of reduction.
P value based reduction.
Typically, we classify two or more sets of promoters using the selected features. In biological applications P values are important and often used in variety of applications. P values of features are calculated based on t distribution. Features with lower P values are better since these indicate presence of two distinct classes. A threshold on the number of features (T %) is set. Features in the increasing order of P values are added to the list until T% is selected as described in Eq (3).
Suppose, p1, p2, p3, p4 …….. pn are the P values of ‘n’ features.
Let l1, l2, l3, l4 ……………. lk be the features selected, where k << n.
Then, pl1 ≤ pl2 ≤ pl3≤ pl4…………………≤ plk
The number of features selected ‘k’ is
(3)
For example, consider a feature matrix of 4 promoters from 2 different sets as shown in Fig 6. Suppose that the first two promoters belong to set/class 1 and next two to set/class 2. T test is performed on values of set 1 and set 2 across all the columns as shown in Fig 6. Suppose, the threshold T = 50% then the number of features selected is 10 (50% of total number (20) of features) in the increasing order of their P values. Therefore, the selected features for this particular example are motif MA0041.1 in band 51–100, 151–200 and 201–250 with P values 0.17, 0.15 and 0.04. MA0084.1 in bands 0–50, 101–150 and 151–200 with p values of 0.10, 0.10 and 0.15. MA00141.1 is selected in 4 of 5 bands except band 151–200 with P values of 0.15, 0.17, 0.15 and 0.19. Rest of the bands (also motifs—in this example MA0072.1) are ignored as they do not satisfy the threshold conditions.
Dataset description
Here we describe the origin and selection of data sets to test the proposed methods. Dataset 1 contains 6 sets (one test and 5 backgrounds) having 124 promoters in each set. Dataset 2 has 3 sets having 100 promoters in each set.
Dataset 1.
Among 176 genes listed in the supplementary notes of experiments on transcriptional regulation of HL60 neutrophil differentiation [44], 124 genes were selected with known functional genes and extracted promoter sequences for these genes using UCSC chromosomal sequences, BioMart annotations and a PERL program. Similarly, five background sets of promoters of genes, which were known to be not differentially expressed, were also obtained. Using Clover tool [45], JASPAR [46] matrices were scanned to obtain the motif information of the promoter as shown in Fig 1.
Dataset 2.
Ubiquitous and tissue-specific gene lists:
The ubiquitous gene list was obtained from an earlier report [47]. We also used the list of genes transcribed in the Testis, Uterus and Kidney from three recent bio curated mammalian gene expression databases MGEx-Tdb, MGEx-Udb and MGEx-Kdb respectively. The advantage of these databases is that the genes were assigned a reliability score based on a meta-analysis of multiple data sets such that the score for a gene indicates the consistency of its transcription status across experiments. Cumulative reliability scores from the 3 databases were used, to hierarchically list the ubiquitous genes. Thus, ubiquitous genes from the earlier report were short-listed if they were also present in 3 tissues considered, with high reliability scores, according to the MGEx-dbs.
Testis and kidney transcribed lists from MGEx-Tdb and MGEx-Kdb were also similarly used to derive a hierarchical list of tissue-specific genes. Testis and kidney specific genes were first obtained from the TiGER database [48] with EST enrichment value, Refseq IDs. Testis-specific genes from the TiGER database that were also transcribed according to the MGEx-Tdb were then short-listed. Similarly, the kidney-specific genes from TiGER database were also short-listed using MGEx-Kdb. But both EST enrichment scores (scaled 0–10) and the reliability score (scaled 0–10) were added and the sum used to sort the tissue-specific genes.
For the top 100 (ubiquitous/tissue-specific) genes, respective ensemble transcript ID was obtained using ensemble. Then, the promoter sequences (-2000 upstream and +500 downstream) corresponding to the selected genes were retrieved using the MGEx databases.
Experiments and Results
The experiments were conducted with complete feature set and also with selected features. As mentioned in the earlier section, selection of features is done using two criteria namely variances and P values. In case of variance based reduction, a threshold on total variation is set for selecting features. Features with higher variance are sequentially added to the selection list until the sum of variations of features in the selected set is just greater than the threshold. With P value selection, the threshold is chosen on the percentage (T%) of features to be selected. Features are added to the selection list in the increasing order of P value until T% is included in the list.
Features selection using MRMR, PCA and SVD are also explored. For these selection methods, the available packages are used. MRMR is a selection procedure. PCA and SVD perform transformation of features and then features are selected.
Classification is performed using three classifiers (KNN, SVM, Decision Tree) and an ensemble classifier (LibD3C) for various parameter settings (such as different K for KNN, different kernels for SVM and for various learning, testing ratios). Details of the extensive experiments conducted on the two datasets are given in Table 1.
(Dataset 1 has 5 pairs of promoter sets and Dataset 2 has 3 pairs of promoter sets).
We also performed experiments using one of the popular ensemble classifier LibD3C for the best performing feature sets (P value based reduced features and MRMR reduced features) on individual classifiers.
Results of experiments on dataset 1
The classification results using different classification techniques (KNN, SVM and Decision Trees) for different learning testing ratios on this dataset are summarized in the Tables 2 to 7. The experiment was repeated 25 times for randomly selected training and testing samples and the average accuracy of these 25 experiments is quoted. We observe in Table 2 that the performance of KNN is consistent irrespective of the K values (1 to 5). SVM classification technique was performed with different kernels (Linear, Quadratic, Polynomial, RBF-Radial Basis Function and MLP-Multilayer Perceptron). It can be seen in Table 3 that Polynomial and MLP kernels do not give satisfactory classification accuracy when compared to the other three kernels when the dimension of features is very high. However, we could observe (Table 3) that their performance significantly improves when the input of the dimension of features is very low (5% of total variation).
We can see in Table 8 that PCA/SVD features perform poor when compared to original features. With PCA/SVD features, only for very large size learning set, the classification accuracy of these features is comparable to that of the original features. MRMR yields very good results for all L: T ratios and all reduction levels. However, almost same accuracy for most cases is obtained even with simple selection procedures based on variance and P values.
In Table 9 the classification performance of ensemble classifier LibD3C using the best features MRMR and P value is presented. When compared to all three individual classifiers, the overall classification accuracies are poor for all the experiments conducted. Ensemble classifiers generally perform better than individual classifiers, but could fail occasionally. This could be because of the k—means clustering algorithm used in LibD3C can be instable and because some classifiers with useful information are excluded from the ensemble without multilayer optimization [25]. Also the time taken by LibD3C is generally more than other individual classifiers.
For background 1, the summary of results after selecting features based on their variance is given in Tables 2, 3 and 4. We observe that decision trees outperform KNN and SVM particularly when the features are reduced. Tables 5, 6 and 7 show the performance of the classifiers for test v/s different backgrounds. We can notice that all the backgrounds show similar performance on all the three classification techniques except background 3. This background shows a drastic fall in number of features as well as accuracy for a threshold<30% of total variance, irrespective of the classifier used. This difference in the background 3 is perhaps due to the presence of a few highly variant features which can be observed from Table 10. Figs 7 and 8 show some plots of classification accuracies obtained for various parameters of classifiers and for different classifiers and feature selections/transformations.
Results of experiments on dataset 2
The experimental setup is same as that of Dataset 1 as described in the earlier section. Selected results are shown in Table 11. Highlights of the results obtained in this extensive experimentation are given in S3 File. The detailed results on top 100promoter sets as well as the complete results of dataset 1 are given in S1 and S2 Files.
The results with P value reduced features are consistently better in all cases, compared to variance reduced features as the number of features is significantly large in P value reduced features compared to variance reduced. MRMR performs best when compared to other feature selections/transformations even on dataset 2. The results of the ensemble classifier LibD3C on dataset 2 is detailed in Table 12. The pattern of feature reduction for different files of dataset 2 is presented in Table 13. Figs 9 and 10 are graphs showing classification accuracies of a classifier and of different classifiers.
9(a), 9(b): KNN, 9(c), 9(d): SVM.
10 (a): Decision Trees. 10 (b): different classifiers 10 (c): different feature selections/transformations.
Implementation details
The experiments were carried out on an Intel Core i5—4460 @ 3.20 GHz machine with 4GB RAM. The code is implemented in MATLAB. It is empirically shown that, reduction in features improves classification accuracy. From Table 14, it is also evident that reduction in features results in drastic reduction in the execution time. This becomes important when we are dealing with large data sets. The time for KNN is the total time taken for K values from 1 to 5 and in the case of SVM it is the total time taken for execution of all 5 kernels. The pattern in reduction of time is same for both KNN and Decision Trees, but in the case of SVM the reduction in time is not uniform, this is due to the convergence issue that exists with some of the kernels used.
The CPU time analysis for the two datasets is shown in Tables 15 and 16. It is clear MRMR takes most time for all feature set sizes of dataset 1. On the other hand, PCA consumes more time than MRMR when the feature set size becomes smaller. However, Variance and P value based reductions are most time efficient.
Conclusion
The results obtained indicate that the variance based and P value based feature selection methods can be effectively used for classifying promoter sequences. Also, we have successfully demonstrated the effect of dimensionality reduction on some of the popular classification techniques used on biological sequences for our experiments on selected promoter sequences. KNN and SVM (particularly with Linear, Quadratic and RBF kernels) perform well even when the dimensionality is very high. Discriminative ability of SVM could be highly improved with good feature selection on Polynomial and MLP kernels. Decision trees seem to be one of the best classifier that achieves good accuracy even when the data dimension is high and the accuracy marginally improves when the dimensionality decreases [27]. We observe a significant improvement in results when compared to some recent methods [6]. This is because we retain the PSMM details in the process of differentiating the promoter sets in this work. Whereas, in [6] the summary of the PSMM is used for promoter set differentiation. The proposed methods out perform some of the popular feature transformation methods such as PCA and SVD. Also, the methods proposed are as accurate as MRMR (feature selection method) but much faster than MRMR. However, we need to further explore the efficiency of this technique for different promoter datasets. Sometimes, even after feature selection using sophisticated techniques, the dimensionality of the chosen features may still be very high [13]. Hence, we can attempt to reduce the feature set by combining different feature selection techniques using ensemble feature selection approaches based on the fact that there cannot be a single universally optimal feature selection technique [49]. Also, there is a possible existence of more than one subset of features that discriminates the data equally well [50]. A combination of different classification and feature selection techniques can both lead to different results [27].
In general, using minimal features for fast classification may help to distinguish functionally different sets of promoters. Such efforts would help scientists understand the molecular mechanisms of gene expression control, which in turn would aid research in many important biological topics.
Supporting Information
S1 File. Complete results of classification on Dataset1.
https://doi.org/10.1371/journal.pone.0167165.s001
(XLSX)
S2 File. Complete results of classification on Dataset2.
https://doi.org/10.1371/journal.pone.0167165.s002
(XLSX)
S3 File. Summary of the experimental analysis on Dataset1 and Dataset2.
https://doi.org/10.1371/journal.pone.0167165.s003
(DOCX)
Acknowledgments
The authors acknowledge the contribution of Darshan. S Chandrashekar, Research Scholar, Institute of Bioinformatics and Applied Biotechnology (IBAB) in the form of identifying the dataset for the experiments discussed. Also, we are grateful to Kavyashree Basavaraju of Shodhaka Life Sciences Pvt. Ltd. for extracting dataset2 in the specific format.
Author Contributions
- Conceptualization: KK LR.
- Data curation: AKK.
- Formal analysis: KK LPG LR AKK.
- Investigation: KK LR AKK.
- Methodology: KK LR LPG AKK.
- Project administration: LR.
- Resources: KK LPG LR AKK.
- Software: KK LPG.
- Supervision: LR AKK.
- Validation: KK LR AKK.
- Visualization: KK LR AKK.
- Writing – original draft: KK LR AKK.
- Writing – review & editing: KK LR AKK.
References
- 1. Pan F, Wang B, Hu X, Perrizo W. Comprehensive vertical sample-based KNN/LSVM classification for gene expression analysis. Journal of Biomedical Informatics. 2004 Aug 31;37(4):240–8. pmid:15465477
- 2. Qin Y, Yalamanchili HK, Qin J, Yan B, Wang J. The current status and challenges in computational analysis of genomic big data. Big Data Research. 2015 Mar 31;2(1):12–8.
- 3. Blanchette M, Tompa M. Discovery of regulatory elements by a computational method for phylogenetic footprinting. Genome research. 2002 May 1;12(5):739–48. pmid:11997340
- 4. Prestridge DS. Predicting Pol II promoter sequences using transcription factor binding sites. Journal of molecular biology. 1995 Jun 23;249(5):923–32. pmid:7791218
- 5. Wu S, Xie X, Liew AW, Yan H. Eukaryotic promoter prediction based on relative entropy and positional information. Physical Review E. 2007 Apr 12;75(4):041908.
- 6.
Kouser K, Rangarajan L, Chandrashekar DS, Kshitish KA, Abraham EM. Alignment Free Frequency Based Distance Measures for Promoter Sequence Comparison. In International Conference on Bioinformatics and Biomedical Engineering 2015 Apr 15 (pp. 183–193). Springer International Publishing.
- 7. Kouser K, Rangarajan L. Promoter Sequence Analysis through No Gap Multiple Sequence Alignment of Motif Pairs. Procedia Computer Science. 2015 Dec 31; 58:356–62.
- 8. Kamath U, De Jong K, Shehu A. Effective automated feature construction and selection for classification of biological sequences. PloS one. 2014 Jul 17;9(7): e99982. pmid:25033270
- 9. Liu B, Liu F, Fang L, Wang X, Chou K C. repDNA—a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics.2015, 31(8):1307–1309. pmid:25504848
- 10. Liu B, Liu F, Fang L, Wang X, Chou K C. repRNA—a web server for generating various feature vectors of RNA sequences. Molecular Genetics and Genomics. 2016, 291(1): 473–481. pmid:26085220
- 11. Liu B, Liu F, Wang X, Chen J, Fang L, Chou K C.Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res. 2015 Jul 1;43(W1): W65–71. pmid:25958395
- 12. Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007 Oct 1;23(19):2507–17. pmid:17720704
- 13. Xing Z, Pei J, Keogh E. A brief survey on sequence classification. ACM SIGKDD Explorations Newsletter. 2010 Nov 9;12(1):40–8.
- 14. Liang KC, Wang X, Anastassiou D. Bayesian basecalling for DNA sequence analysis using hidden Markov models. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). 2007 Jul 1;4(3):430–40.
- 15.
Leung HC, Chin FY. Discovering DNA Motifs with Nucleotide Dependency. InBIBE 2006 Oct 16 (pp. 70–80).
- 16. Alexandrov NN, Mironov AA. Application of a new method of pattern recognition in DNA sequence analysis: a study of E. coli promoters. Nucleic acids research. 1990 Apr 11;18(7):1847–52. pmid:2186368
- 17.
Dong X, Sung SY, Sung WK, Tan CL. In Bioinformatics and Bioengineering, 2004. BIBE 2004. Proceedings. Fourth IEEE Symposium on 2004 May 19 (pp. 483–490). IEEE.
- 18.
Meera A. Computational Models for DNA Sequence Alignment-Some New Approaches (Doctoral dissertation, Doctoral Thesis. University of Mysore).
- 19.
Chan TM, Leung KS, Lee KH. Generic spaced DNA motif discovery using Genetic Algorithm. In IEEE Congress on Evolutionary Computation 2010 Jul 18 (pp. 1–8). IEEE.
- 20. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012 Sep 6;489(7414):57–74. pmid:22955616
- 21.
Kouser K, Rangarajan L. Similarity analysis of position specific motif matrices using lacunarity for promoter sequences. In Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing 2014 Oct 10 (p. 37). ACM.
- 22. Ding C, Peng H.Minimum redundancy feature selection from microarray gene expression data.J Bioinform Comput Biol. 2005 Apr;3(2):185–205. pmid:15852500
- 23. Li GZ, Bu HL, Yang MQ, Zeng XQ, Yang JY. Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis. BMC genomics. 2008 Sep 16;9(2):1.
- 24. Guyon I, Elisseeff A. An introduction to variable and feature selection. Journal of machine learning research. 2003;3(Mar):1157–82.
- 25. Lin C, Chen W, Qiu C, Wu Y, Krishnan S, Zou Q. LibD3C: ensemble classifiers with a clustering and dynamic selection strategy. Neurocomputing. 2014 Jan 10; 123:424–35.
- 26. Murakami K, Imanishi T, Gojobori T, Nakai K. Two different classes of co-occurring motif pairs found by a novel visualization method in human promoter regions. BMC genomics. 2008 Mar 1; 9(1):1.
- 27. Huang J, Fang H, Fan X. Decision forest for classification of gene expression data. Computers in biology and medicine. 2010 Aug 31; 40(8):698–704. pmid:20591424
- 28. Simon R. Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data. British journal of cancer. 2003 Nov 3; 89(9):1599–604. pmid:14583755
- 29. Jeffery IB, Higgins DG, Culhane AC. Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC bioinformatics. 2006 Jul 26;7(1):359.
- 30. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics. 2000 Oct 1;16(10):906–14. pmid:11120680
- 31. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Machine learning. 2002 Jan 1;46(1–3):389–422.
- 32.
Bercher JF, Jardin P, Duriez B. Bayesian classification and entropy for promoter prediction in human DNA sequences. In 26th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt'06) 2006 Feb 27 (Vol. 872, No. 1, pp. 235–242).
- 33.
Pavlidis P, Furey TS, Liberto M, Haussler D, Grundy WN. Promoter region-based classification of genes. In Pacific symposium on biocomputing 2001 (Vol. 6, pp. 151–164).
- 34.
Mitchell TM. Machine learning. McGraw-Hill, New York, NY, 1997.
- 35.
Leslie CS, Eskin E, Noble WS. The spectrum kernel: A string kernel for SVM protein classification. In Pacific symposium on biocomputing 2002 Jan 2 (Vol. 7, No. 7, pp. 566–575).
- 36. Rätsch G, Sonnenburg S, Schäfer C. Learning interpretable SVMs for biological sequence classification. BMC bioinformatics. 2006 Mar 20;7(Suppl 1): S9.
- 37.
Deshpande M, Karypis G. Evaluation of techniques for classifying biological sequences. In Pacific-Asia Conference on Knowledge Discovery and Data Mining 2002 May 6 (pp. 417–431). Springer Berlin Heidelberg.
- 38. Weston J, Leslie C, Ie E, Zhou D, Elisseeff A, Noble WS. Semi-supervised protein classification using cluster kernels. Bioinformatics. 2005 Aug 1;21(15):3241–7. pmid:15905279
- 39.
Brieman L. Classification and regression trees. Chapman and Hall/CRC,1998.
- 40.
Quinlan JR. C4. 5: Programming for machine learning. Morgan Kauffmann. 1993 Jan:38.
- 41. Kingsford C, Salzberg SL. What are decision trees? Nature biotechnology. 2008 Sep 1;26(9):1011–3. pmid:18779814
- 42.
Lazarevic A, Obradovic Z. Effective pruning of neural network classifier ensembles. In Neural Networks, 2001. Proceedings. IJCNN'01. International Joint Conference on 2001 (Vol. 2, pp. 796–801). IEEE.
- 43. Hu Q, Zhang L, Zhang D, Pan W, An S, Pedrycz W. Measuring relevance between discrete and continuous features based on neighborhood mutual information. Expert Systems with Applications. 2011 Sep 30;38(9):10737–50.
- 44. Huang AC, Hu L, Kauffman SA, Zhang W, Shmulevich I. Using cell fate attractors to uncover transcriptional regulation of HL60 neutrophil differentiation. BMC systems biology. 2009 Feb 18;3(1):1.
- 45. Frith MC, Fu Y, Yu L, Chen JF, Hansen U, Weng Z. Detection of functional DNA motifs via statistical over‐representation. Nucleic acids research. 2004 Mar 15;32(4):1372–81. pmid:14988425
- 46. Mathelier A, Zhao X, Zhang AW, Parcy F, Worsley-Hunt R, Arenillas DJ, Buchman S, Chen CY, Chou A, Ienasescu H, Lim J. JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic acids research. 2013 Nov 4: gkt997.
- 47. Chang CW, Cheng WC, Chen CR, Shu WY, Tsai ML, Huang CL, Hsu IC. Identification of human housekeeping genes and tissue-selective genes by microarray meta-analysis. PloS one. 2011 Jul 27;6(7): e22859. pmid:21818400
- 48. Liu X, Yu X, Zack DJ, Zhu H, Qian J. TiGER: a database for tissue-specific gene expression and regulation. BMC bioinformatics. 2008 Jun 9;9(1):271.
- 49. Yang YH, Xiao Y, Segal MR. Identifying differentially expressed genes from microarray experiments via statistic synthesis. Bioinformatics. 2005 Apr 1;21(7):1084–93. pmid:15513985
- 50. Yeung KY, Bumgarner RE, Raftery AE. Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data. Bioinformatics. 2005 May 15;21(10):2394–402. pmid:15713736