Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion

MSCNE:Predict miRNA-Disease Associations Using Neural Network Based on Multi-Source Biological Information

The important role of microRNA (miRNA) in human diseases has been confirmed by some studies. However, only using biological experiments has greater blindness, leading to higher experimental costs. In this paper a high-efficiency algorithm based on a variety of biological source information and applying a combination of a convolutional neural network (CNN) feature extractor and an extreme learning machine (ELM) classifier is proposed. Specifically, the semantic similarity of diseases, the gaussian interaction profile kernel similarity of the four biological information of miRNA, disease, long non-coding RNA (lncRNA) and environmental factors (EFs), and the similarities of miRNAs are fused together. Among them, miRNAs similarity is composed of miRNA target information, sequence information, family information, and function information. Then, the dimensionality of the data set is reduced by the autoencoder (AE). Finally, deep features are extracted through CNN, and then the association between miRNA and disease is predicted by ELM. The experimental results show that the average AUC value based on the multi-biological source information (MSCNE) model is 0.9630, which can reach higher performance than the other classic classifier, feature extractor mentioned and the other existing algorithms. The results show the MSCNE algorithm is effective to predict the correlation of miRNA-disease.

ILDMSF: Inferring Associations Between Long Non-Coding RNA and Disease Based on Multi-Similarity Fusion

The dysregulation and mutation of long non-coding RNAs (lncRNAs) have been proved to result in a variety of human diseases. Identifying potential disease-related lncRNAs may benefit disease diagnosis, treatment and prognosis. A number of methods have been proposed to predict the potential lncRNA-disease relationships. However, most of them may give rise to incorrect results due to relying on single similarity measure. This article proposes a novel framework (ILDMSF) by fusing the lncRNA similarities and disease similarities, which are measured by lncRNA-related gene and known lncRNA-disease interaction and disease semantic interaction, and known lncRNA-disease interaction, respectively. Further, the support vector machine is employed to identify the potential lncRNA-disease associations based on the integrated similarity. The leave-one-out cross validation is performed to compare ILDMSF with other state of the art methods. The experimental results demonstrate our method is prospective in exploring potential correlations between lncRNA and disease.

GBDTL2E: Predicting lncRNA-EF Associations Using Diffusion and HeteSim Features Based on a Heterogeneous Network

Interactions between genetic factors and environmental factors (EFs) play an important role in many diseases. Many diseases result from the interaction between genetics and EFs. The long non-coding RNA (lncRNA) is an important non-coding RNA that regulates life processes. The ability to predict the associations between lncRNAs and EFs is of important practical significance. However, the recent methods for predicting lncRNA-EF associations rarely use the topological information of heterogenous biological networks or simply treat all objects as the same type without considering the different and subtle semantic meanings of various paths in the heterogeneous network. In order to address this issue, a method based on the Gradient Boosting Decision Tree (GBDT) to predict the association between lncRNAs and EFs (GBDTL2E) is proposed in this paper. The innovation of the GBDTL2E integrates the structural information and heterogenous networks, combines the Hetesim features and the diffusion features based on multi-feature fusion, and uses the machine learning algorithm GBDT to predict the association between lncRNAs and EFs based on heterogeneous networks. The experimental results demonstrate that the proposed algorithm achieves a high performance.

Transcription Factors Targeted by miRNAs Regulating Smooth Muscle Cell Growth and Intimal Thickening after Vascular Injury

Neointima formation after percutaneous coronary intervention (PCI) is a manifestation of “phenotype switching” by vascular smooth muscle cells (SMC), a process that involves de-differentiation from a contractile quiescent phenotype to one that is richly synthetic. In response to injury, SMCs migrate, proliferate, down-regulate SMC-specific differentiation genes, and later, can revert to the contractile phenotype. The vascular response to injury is regulated by microRNAs (or miRNAs), small non-coding RNAs that control gene expression. Interactions between miRNAs and transcription factors impact gene regulatory networks. This article briefly reviews the roles of a range of miRNAs in molecular and cellular processes that control intimal thickening, focusing mainly on transcription factors, some of which are encoded by immediate-early genes. Examples include Egr-1, junB, KLF4, KLF5, Elk-1, Ets-1, HMGB1, Smad1, Smad3, FoxO4, SRF, Rb, Sp1 and c-Myb. Such mechanistic information could inform the development of strategies that block SMC growth, neointima formation, and potentially overcome limitations of lasting efficacy following PCI.

Predicting miRNA-disease interaction based on recommend method

Purpose

MicroRNAs (miRNAs) have been proved to be a significant type of non-coding RNAs related to various human diseases. This paper aims to identify the potential miRNA–disease interactions.

Design/methodology/approach

A computational framework, MDIRM is presented to predict miRNAs-disease interactions. Unlike traditional approaches, the miRNA function similarity is calculated by miRNA–disease interactions. The k-mean method is further used to cluster miRNA similarity network. For miRNAs in the same cluster, their similarities are enhanced, as the miRNAs from the same cluster may be reliable. Further, the potential miRNA–disease association is predicted by using recommend method.

Findings

To evaluate the performance of our model, the fivefold cross validation is implemented to compare with two state-of-the-art methods. The experimental results indicate that MDIRM achieves an AUC of 0.926, which outperforms other methods.

Originality/value

This paper proposes a novel computational method for miRNA–disease interaction prediction based on recommend method. Identifying the relationship between miRNAs and diseases not only helps us better understand the disease occurrence and mechanism through the perspective of miRNA but also promotes disease diagnosis and treatment.

Molecular Computing and Bioinformatics

Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of-and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions-even trillions-of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.

Impact of Polypharmacy on Candidate Biomarker miRNomes for the Diagnosis of Fibromyalgia and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Striking Back on Treatments

Fibromyalgia (FM) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are diseases of unknown etiology presenting complex and often overlapping symptomatology. Despite promising advances on the study of miRNomes of these diseases, no validated molecular diagnostic biomarker yet exists. Since FM and ME/CFS patient treatments commonly include polypharmacy, it is of concern that biomarker miRNAs are masked by drug interactions. Aiming at discriminating between drug-effects and true disease-associated differential miRNA expression, we evaluated the potential impact of commonly prescribed drugs on disease miRNomes, as reported by the literature. By using the web search tools SM2miR, Pharmaco-miR, and repoDB, we found a list of commonly prescribed drugs that impact FM and ME/CFS miRNomes and therefore could be interfering in the process of biomarker discovery. On another end, disease-associated miRNomes may incline a patient's response to treatment and toxicity. Here, we explored treatments for diseases in general that could be affected by FM and ME/CFS miRNomes, finding a long list of them, including treatments for lymphoma, a type of cancer affecting ME/CFS patients at a higher rate than healthy population. We conclude that FM and ME/CFS miRNomes could help refine pharmacogenomic/pharmacoepigenomic analysis to elevate future personalized medicine and precision medicine programs in the clinic.