μHEM for identification of differentially expressed miRNAs using hypercuboid equivalence partition matrix
TL;DR: The results on several microarray data sets demonstrate that the proposed method can bring a remarkable improvement on miRNA selection problem and is a potentially useful tool for exploration of miRNA expression data and identification of differentially expressed miRNAs worth further investigation.
Abstract: The miRNAs, a class of short approximately 22‐nucleotide non‐coding RNAs, often act post‐transcriptionally to inhibit mRNA expression. In effect, they control gene expression by targeting mRNA. They also help in carrying out normal functioning of a cell as they play an important role in various cellular processes. However, dysregulation of miRNAs is found to be a major cause of a disease. It has been demonstrated that miRNA expression is altered in many human cancers, suggesting that they may play an important role as disease biomarkers. Multiple reports have also noted the utility of miRNAs for the diagnosis of cancer. Among the large number of miRNAs present in a microarray data, a modest number might be sufficient to classify human cancers. Hence, the identification of differentially expressed miRNAs is an important problem particularly for the data sets with large number of miRNAs and small number of samples. In this regard, a new miRNA selection algorithm, called μHEM, is presented based on rough hypercuboid approach. It selects a set of miRNAs from a microarray data by maximizing both relevance and significance of the selected miRNAs. The degree of dependency of sample categories on miRNAs is defined, based on the concept of hypercuboid equivalence partition matrix, to measure both relevance and significance of miRNAs. The effectiveness of the new approach is demonstrated on six publicly available miRNA expression data sets using support vector machine. The.632+ bootstrap error estimate is used to minimize the variability and biasedness of the derived results. An important finding is that the μHEM algorithm achieves lowest B.632+ error rate of support vector machine with a reduced set of differentially expressed miRNAs on four expression data sets compare to some existing machine learning and statistical methods, while for other two data sets, the error rate of the μHEM algorithm is comparable with the existing techniques. The results on several microarray data sets demonstrate that the proposed method can bring a remarkable improvement on miRNA selection problem. The method is a potentially useful tool for exploration of miRNA expression data and identification of differentially expressed miRNAs worth further investigation.
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TL;DR: The proposed prediction model provides an effective tool for DLB classification and predicted candidate target genes from the miRNAs, including 6 functional genes included in the DHA signaling pathway associated with DLB pathology.
Abstract: Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer’s disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential biomarkers of prodromal DLB is still challenging. MicroRNAs (miRNAs) have recently received a lot of attention as a source of novel biomarkers. In this study, using serum miRNA expression of 478 Japanese individuals, we investigated potential miRNA biomarkers and constructed an optimal risk prediction model based on several machine learning methods: penalized regression, random forest, support vector machine, and gradient boosting decision tree. The final risk prediction model, constructed via a gradient boosting decision tree using 180 miRNAs and two clinical features, achieved an accuracy of 0.829 on an independent test set. We further predicted candidate target genes from the miRNAs. Gene set enrichment analysis of the miRNA target genes revealed 6 functional genes included in the DHA signaling pathway associated with DLB pathology. Two of them were further supported by gene-based association studies using a large number of single nucleotide polymorphism markers (BCL2L1: P = 0.012, PIK3R2: P = 0.021). Our proposed prediction model provides an effective tool for DLB classification. Also, a gene-based association test of rare variants revealed that BCL2L1 and PIK3R2 were statistically significantly associated with DLB.
24 citations
Cites methods from "μHEM for identification of differen..."
...This final risk prediction model using μHEM algorithm achieved an accuracy of 0.803 on an independent test set when pre-selecting the top-ranked 330 miRNAs and three clinical features....
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...Paul S, Maji P. muHEM for identification of differentially expressed miRNAs using hypercuboid equivalence partition matrix....
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...We also constructed a GBDT risk prediction model using another feature selection algorithm, μHEM [23], publicly available at http://www....
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...We also constructed a GBDT risk prediction model using another feature selection algorithm, μHEM [23], publicly available at http://www.isical.ac.in/~bibl/results/ mihem/mihem.html, and investigated whether this feature selection methodology can further improve the predictive ability of our model....
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...Hyperparameter values in the final GBDT model when using μHEM algorithm....
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TL;DR: The formulation enables the proposed method to extract required number of correlated features sequentially with lesser computational cost as compared to existing methods, and provides an efficient way to find optimum regularization parameters employed in CCA.
Abstract: One of the main problems associated with high dimensional multimodal real life data sets is how to extract relevant and significant features. In this regard, a fast and robust feature extraction algorithm, termed as FaRoC, is proposed, integrating judiciously the merits of canonical correlation analysis (CCA) and rough sets. The proposed method extracts new features sequentially from two multidimensional data sets by maximizing their relevance with respect to class label and significance with respect to already-extracted features. To generate canonical variables sequentially, an analytical formulation is introduced to establish the relation between regularization parameters and CCA. The formulation enables the proposed method to extract required number of correlated features sequentially with lesser computational cost as compared to existing methods. To compute both significance and relevance measures of a feature, the concept of hypercuboid equivalence partition matrix of rough hypercuboid approach is used. It also provides an efficient way to find optimum regularization parameters employed in CCA. The efficacy of the proposed FaRoC algorithm, along with a comparison with other existing methods, is extensively established on several real life data sets.
23 citations
Cites methods from "μHEM for identification of differen..."
...It has been applied successfully for analyzing omics data [34], [45], [46]....
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TL;DR: Results indicate that the integrated method presented is quite promising and may become a useful tool for identifying disease genes.
Abstract: One of the most important and challenging problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new computational method to identify disease genes. It judiciously integrates the information of gene expression profiles and shortest path analysis of protein---protein interaction networks. While the $$f$$f-information based maximum relevance-maximum significance framework is used to select differentially expressed genes as disease genes using gene expression profiles, the functional protein association network is used to study the mechanism of diseases. An important finding is that some $$f$$f-information measures are shown to be effective for selecting relevant and significant genes from microarray data. Extensive experimental study on colorectal cancer establishes the fact that the genes identified by the integrated method have more colorectal cancer genes than the genes identified from the gene expression profiles alone, irrespective of any gene selection algorithm. Also, these genes have greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. The enrichment analysis of the obtained genes reveals to be associated with some of the important KEGG pathways. All these results indicate that the integrated method is quite promising and may become a useful tool for identifying disease genes.
12 citations
Cites methods from "μHEM for identification of differen..."
...The f -MRMS algorithm judiciously integrates the merits of maximum relevancemaximum significance (MRMS) criterion (Maji and Paul 2011; Paul and Maji 2013a, b) and f -information measures....
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TL;DR: This study presents an application of the RH-SAC algorithm on miRNA and mRNA expression data for identification of potential miRNA-mRNA modules and identified novel miRNA/mRNA interactions in colorectal cancer.
Abstract: Differences in the expression profiles of miRNAs and mRNAs have been reported in colorectal cancer. Nevertheless, information on important miRNA-mRNA regulatory modules in colorectal cancer is still lacking. In this regard, this study presents an application of the RH-SAC algorithm on miRNA and mRNA expression data for identification of potential miRNA-mRNA modules. First, a set of miRNA rules was generated using the RH-SAC algorithm. The mRNA targets of the selected miRNAs were identified using the miRTarBase database. Next, the expression values of target mRNAs were used to generate mRNA rules using the RH-SAC. Then all miRNA-mRNA rules have been integrated for generating networks. The RH-SAC algorithm unlike other existing methods selects a group of co-expressed miRNAs and mRNAs that are also differentially expressed. In total 17 miRNAs and 141 mRNAs were selected. The enrichment analysis of selected mRNAs revealed that our method selected mRNAs that are significantly associated with colorectal cancer. We identified novel miRNA/mRNA interactions in colorectal cancer. Through experiment, we could confirm that one of our discovered miRNAs, hsa-miR-93-5p, was significantly up-regulated in 75.8% CRC in comparison to their corresponding non-tumor samples. It could have the potential to examine colorectal cancer subtype specific unique miRNA/mRNA interactions.
9 citations
TL;DR: A novel supervised regularized canonical correlation analysis, termed as CuRSaR, to extract relevant and significant features from multimodal high dimensional omics datasets by maximizing the relevance of extracted features with respect to sample categories and significance among them.
Abstract: Objective: This paper presents a novel supervised regularized canonical correlation analysis, termed as CuRSaR, to extract relevant and significant features from multimodal high dimensional omics datasets. Methods: The proposed method extracts a new set of features from two multidimensional datasets by maximizing the relevance of extracted features with respect to sample categories and significance among them. It integrates judiciously the merits of regularized canonical correlation analysis (RCCA) and rough hypercuboid approach. An analytical formulation, based on spectral decomposition, is introduced to establish the relation between canonical correlation analysis (CCA) and RCCA. The concept of hypercuboid equivalence partition matrix of rough hypercuboid is used to compute both relevance and significance of a feature. Significance: The analytical formulation makes the computational complexity of the proposed algorithm significantly lower than existing methods. The equivalence partition matrix offers an efficient way to find optimum regularization parameters employed in CCA. Results: The superiority of the proposed algorithm over other existing methods, in terms of computational complexity and classification accuracy, is established extensively on real life data.
9 citations
Cites methods from "μHEM for identification of differen..."
...It has been applied successfully to feature selection and clustering [27] as well as to omics data analysis [26]–[30]....
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References
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46 citations
Journal Article•
TL;DR: The result shows that the more dedifferentiated PCa cells have a higher expression of miR-20a and this supports the oncogenic role of miRNAs in PCa carcinogenesis, which could yield new information about PCa pathogenesis.
Abstract: Background: MicroRNAs (miRNAs), which are endogenously expressed regulatory noncoding RNAs, have an altered expression in tumor tissues. MiRNAs regulate cancer- related processes such as cell growth and tissue differentiation, and therefore, might function as oncogenes or tumor-suppressor genes. The aim of our study was to assess the expression of mir-20a, let-7a, miR-15a and miR- 16 in prostate cancer (PCa) and benign prostatic hyperplasia (BPH) tissue and to investigate the relation between the expression of miRNAs and the clinico- pathological features of PCa. Patients and Methods: The study group comprised 138 patients: 85 patients with BPH and 53 patients with PCa. The total RNA was isolated from the tissue specimen core and miRNA expressions were quantified using a real-time RT-PCR method (TaqMan MicroRNA Assays). U6snRNA was used for the normalization of the miRNA expression. Results: miR-20a expression was significantly higher in the group of patients with a Gleason score of 7-10 in comparison with the group of patients with a Gleason score of 0-6 (p=0.0082). We found no statistical differences in the miRNA expressions (mir-20a, let-7a, miR-15a and miR-16) in the PCa tissue samples in comparison with the BPH tissue samples. Conclusion: Our result shows that the more dedifferentiated PCa cells have a higher expression of miR-20a and this supports the
43 citations
"μHEM for identification of differen..." refers background in this paper
...In [54], it has been shown that the hsa-miR-20a is over expressed in prostate cancer....
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TL;DR: Prolonged thalidomide therapy enhanced survival rate and less frequently developed serious toxicity in non-transplant multiple myeloma patients.
Abstract: Thalidomide based regimen is an effective and well tolerated therapy in multiple myeloma (MM) patients, however, there were a small number of studies written about the results of thalidomide therapy in non-transplant MM patients. We therefore conducted a retrospective study of 42 consecutive patients with newly diagnosed and relapsed/refractory MM treated with thalidomide- based induction regimens followed by thalidomide maintenance therapy. Induction regimens with thalidomide and dexamethasone, and the oral combination of melphalan, prednisolone and thalidomide were administrated in 22 and 16 patients, respectively. The remaining 4 patients received other thalidomide- containing regimens. Twenty-nine patients received thalidomide as a salvage regimen. Twenty-three out of 26 patients achieving complete remission (CR) and very good partial remission (VGPR) received thalidomide maintenance. Of the 41 evaluable patients, median time of treatment was 21 months (3- 45 months), ORR was 92.7% with a 63.4% CR/VGPR. With a median follow up of 23 months, 3-year- PFS and 3-year-OS were 58.6 and 72.6%, respectively. Median time to progression was 42 months. While 3-year-PFS and 3-year-OS in non-transplant patients receiving thalidomide maintenance therapy were 67 and 80%, respectively. Prolonged thalidomide therapy enhanced survival rate and less frequently developed serious toxicity in non-transplant multiple myeloma patients.
42 citations
TL;DR: Native polyacrylamide gel electrophoresis results demonstrated that rhEPO was successfully encapsulated into the micelles, which was stable in phosphate buffered saline with different pHs and concentrations of NaCl, therefore, PEG–PLA micells can be a potential protein drug delivery system.
Abstract: To improve the pharmacokinetics and stability of recombinant human erythropoietin (rhEPO), rhEPO was successfully formulated into poly(ethylene glycol)–poly(d,l-lactide) (PEG–PLA) di-block copolymeric micelles at diameters ranging from 60 to 200 nm with narrow polydispersity indices (PDIs; PDI < 0.3) and trace amount of protein aggregation. The zeta potential of the spherical micelles was in the range of −3.78 to 4.65 mV and the highest encapsulation efficiency of rhEPO in the PEG–PLA micelles was about 80%. In vitro release profiles indicated that the stability of rhEPO in the micelles was improved significantly and only a trace amount of aggregate was found. Pharmacokinetic studies in rats showed highly enhanced plasma retention time of the rhEPO-loaded PEG-PLA micelles in comparison with the native rhEPO group. Increased hemoglobin concentrations were also found in the rat study. Native polyacrylamide gel electrophoresis results demonstrated that rhEPO was successfully encapsulated into the micelles, which was stable in phosphate buffered saline with different pHs and concentrations of NaCl. Therefore, PEG–PLA micelles can be a potential protein drug delivery system.
33 citations
14 May 2007
TL;DR: This work transforms the training data using a novel way of non-parametric discretization, called roughfication, and applies standard rough set attribute reduction and then classify the testing data by voting among the obtained decision rules.
Abstract: We extend the standard rough set-based approach to be able to deal with huge amounts of numeric attributes versus small amount of available objects. We transform the training data using a novel way of non-parametric discretization, called roughfication (in contrast to fuzzification known from fuzzy logic). Given roughfied data, we apply standard rough set attribute reduction and then classify the testing data by voting among the obtained decision rules. Roughfication enables to search for reducts and rules in the tables with the original number of attributes and far larger number of objects. It does not require expert knowledge or any kind of parameter tuning or learning. We illustrate it by the analysis of the gene expression data, where the number of genes (attributes) is enormously large with respect to the number of experiments (objects).
32 citations
"μHEM for identification of differen..." refers methods in this paper
...The theory of rough sets has also been successfully applied to microarray data analysis in [9,24-35]....
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