Improving classification of mature microRNA by solving class imbalance problem

Classification of cancer types based on microRNA expression using a hybrid radial basis function and particle swarm optimization algorithm

The diagnosis and treatment of cancer is one of the most challenging aspects of the medical profession, despite advances in disease diagnosis. MicroRNAs are small noncoding RNA molecules involved in regulating gene expression and are associated with several cancer types. Therefore, the analysis of microRNA data has become one of the most important areas of cancer research in recent years. This paper presents an improved method for cancer-type classification based on microRNA expression data using a hybrid radial basis function (RBF) and particle swarm optimization (PSO) algorithm. Two datasets containing microRNA information were used, and preprocessing and normalization operations were performed on the raw data. Feature selection was carried out by using the PSO algorithm, which can identify the most relevant and informative features in the data along with helping to prioritize them. Using a PSO algorithm for feature selection is an effective approach to microRNA analysis. This enhances the accuracy and reliability of cancer-type classifications based on microRNA expression data. In the proposed method, we, respectively, achieved an accuracy of 0.95% and 0.91% on both datasets, with an average of 0.93%, using an improved RBF neural network classifier. These results demonstrate that the proposed method outperforms previous works. RESEARCH HIGHLIGHTS: To enhance the accuracy of cancer-type classifications based on microRNA expression data. We present a minimal feature selection method using particle swarm optimization to reduce computational load & radial basis function to improve accuracy.

Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach

Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miRNA identification in the yet poorly annotated porcine genome and demonstrated the usefulness of implementing a motif search positional refinement strategy for the accurate determination of precursor miRNA boundaries. The small RNA fraction from gluteus medius skeletal muscle of 48 Duroc gilts was sequenced and used for the prediction of novel miRNA loci. Additionally, we selected the human miRNA annotation for a homology-based search of porcine miRNAs with orthologous genes in the human genome. A total of 20 novel expressed miRNAs were identified in the porcine muscle transcriptome and 27 additional novel porcine miRNAs were also detected by homology-based search using the human miRNA annotation. The existence of three selected novel miRNAs (ssc-miR-483, ssc-miR484 and ssc-miR-200a) was further confirmed by reverse transcription quantitative real-time PCR analyses in the muscle and liver tissues of Göttingen minipigs. In summary, the eMIRNA pipeline presented in the current work allowed us to expand the catalogue of porcine miRNAs and showed better performance than other commonly used miRNA prediction approaches. More importantly, the flexibility of our pipeline makes possible its application in other yet poorly annotated non-model species.

An Approach to Identify Individual Functional Single Nucleotide Polymorphisms and Isoform MicroRNAs

MicroRNAs (miRNAs) and single nucleotide polymorphisms (SNPs) play important roles in disease risk and development, especially cancer. Importantly, when SNPs are located in pre-miRNAs, they affect their splicing mechanism and change the function of miRNAs. To improve disease risk assessment, we propose an approach and developed a software tool, IsomiR_Find, to identify disease/phenotype-related SNPs and isomiRs in individuals. Our approach is based on the individual's samples, with SNP information extracted from the 1000 Genomes Project. SNPs were mapped to pre-miRNAs based on whole-genome coordinates and then SNP-pre-miRNA sequences were constructed. Moreover, we developed matpred2, a software tool to identify the four splicing sites of mature miRNAs. Using matpred2, we identified isomiRs and then verified them by searching within individual miRNA sequencing data. Our approach yielded biomarkers for biological experiments, mined functions of miRNAs and SNPs, improved disease risk assessment, and provided a way to achieve individualized precision medicine.

Expression profiles and prognostic value of miRNAs in retinoblastoma

Current cure rates for retinoblastoma (RB) are very high in developed countries. Nonetheless, in less privileged places worldwide, delayed diagnosis and refusal to adhere to treatment still endure an obstacle to improve overall patient survival. Thus, the access to consistent biomarkers for diagnosis at an earlier stage may facilitate treatment and improve outcomes. Over recent years, much attention has been focused on miRNAs, key post-transcriptional regulators that when altered, largely contribute to carcinogenesis and tumor progression. Many of the ~ 2500 microRNAs described in humans have shown differential expression profiles in tumors. In this review, we summarize current data about the roles of miRNAs in RB along with their value as diagnostic/prognostic factors using electronic databases such as PubMed. We reviewed the importance of miRNA in RB biology and discussed their implications in clinic intervention. Several miRNAs have pointed out reliable diagnostic and prognostic molecular biomarkers. The emergence of targeted therapies has significantly improved cancer treatment. In the near future, the modulation of miRNAs will represent a good treatment strategy.

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

The function of a protein is of great interest in the cutting-edge research of biological mechanisms, disease development and drug/target discovery. Besides experimental explorations, a variety of computational methods have been designed to predict protein function. Among these in silico methods, the prediction of BLAST is based on protein sequence similarity, while that of machine learning is also based on the sequence, but without the consideration of their similarity. This unique characteristic of machine learning makes it a good complement to BLAST and many other approaches in predicting the function of remotely relevant proteins and the homologous proteins of distinct function. However, the identification accuracies of these in silico methods and their false discovery rate have not yet been assessed so far, which greatly limits the usage of these algorithms. Herein, a comprehensive comparison of the performances among four popular prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these methods was systematically assessed by four standard statistical indexes based on the independent test datasets of 93 functional protein families defined by UniProtKB keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model organisms (Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae and Mycobacterium tuberculosis). As a result, the substantially higher sensitivity of SVM and BLAST was observed compared with that of PNN and KNN. However, the machine learning algorithms (PNN, KNN and SVM) were found capable of substantially reducing the false discovery rate (SVM < PNN < KNN). In sum, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.