MicroRNA–mRNA interaction network using TSK-type recurrent neural fuzzy network

Using machine learning approaches for multi-omics data analysis: A review

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

PHLPP2 is regulated by competing endogenous RNA network in pathogenesis of colon cancer

Recently, homologous pleckstrin-homology (PH)-domain leucine-rich-repeat protein phosphatases (PHLPP2) has been reported as a tumor suppressor in colon cancer. This study aimed to unravel the possible involvement of long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) regulating PHLPP2 in colon cancer. Expressions of candidate lncRNAs and miRNAs were verified by the RT-qPCR and Western blot analyses in colon cancer. The roles of candidate genes in colon cancer were investigated in HT-29 cells in vitro and in mouse tumor xenograft model in vivo. PHLPP2, a target of miR-141 and miR-424, was downregulated in colon cancer. PHLPP2 upregulation and miR-141 and miR-424 downregulation suppressed the colon cancer cell proliferation, migration, invasion, and epithelial-mesenchymal transition, and promote cell apoptosis, which also resulted in suppression of tumor metastasis and formation. Furthermore, LINC00402, LINC00461, and SFTA1P were identified as the targets of miR-141 and miR-424 and acted as competitive endogenous RNAs (ceRNAs) of PHLPP2. The upregulation of LINC00402, LINC00461, and SFTA1P was verified to enhance the suppressive effects of PHLPP2 in the pathogenesis of colon cancer. Conjointly, our results demonstrated the suppressive effects of PHLPP2 in colon cancer and proved that LINC00402, LINC00461, and SFTA1P acted as ceRNAs of PHLPP2 by competitive binding to miR-141 and miR-424.

Backstepping-Based Recurrent Type-2 Fuzzy Sliding Mode Control for MIMO Systems (MEMS Triaxial Gyroscope Case Study)

This paper presents a novel type-2 fuzzy sliding mode control with nonlinear consequent part in fuzzy rules for control of Micro-Electro-Mechanical Systems (MEMS) gyroscope. The MEMS gyroscope consists of the basic mechanical structure, an electronic transducer to excite the system as well as an electronic sensor to detect the change in the mechanical structures modal shape. A nonlinear consequent part recurrent type-2 fuzzy system is used to approximate the conventional sliding mode control (SMC) law. A supervisory compensator is introduced to eliminate the effect of the approximation error. The adaptive adjustment algorithms for type-2 fuzzy parameters are derived in the sense of projection algorithm and Lyapunov stability theorem. The proposed type-2 fuzzy system has simple structure with six layers. Recurrent feedbacks at the fifth layer uses delayed outputs for improve the performance of type-2 fuzzy system. Finally the proposed type-2 fuzzy sliding mode control system is used to tracking control design with regard to uncertainty in MEMS gyroscope system. Combination of backstepping method and sliding mode control helps to compensate the control signal and get a better performance. The backstepping method is used to improve the global ultimate asymptotic stability and applying the sliding mode control to obtain high response and invariability to uncertainties. Simulation results show the proposed type-2 fuzzy system has better performance than ANFIS-based sliding mode control.

Revisiting Chaos Theorem to Understand the Nature of miRNAs in Response to Drugs of Abuse

Just like Matryoshka dolls, biological systems follow a hierarchical order that is based on dynamic bidirectional communication among its components. In addition to the convoluted inter-relationships, the complexity of each component spans several folds. Therefore, it becomes rather challenging to investigate phenotypes resulting from these networks as it requires the integration of reductionistic and holistic approaches. One dynamic system is the transcriptome which comprises a variety of RNA species. Some, like microRNAs, have recently received a lot of attention. miRNAs are very pleiotropic and have been considered as therapeutic and diagnostic candidates in the biomedical fields. In this review, we survey miRNA profiles in response to drugs of abuse (DA) using 118 studies. After providing a summary of miRNAs related to substance use disorders (SUD), general patterns of miRNA signatures are compared among studies for single or multiple drugs of abuse. Then, current challenges and drawbacks in the field are discussed. Finally, we provide support for considering miRNAs as a chaotic system in normal versus disrupted states particularly in SUD and propose an integrative approach for studying and analyzing miRNA data.

Pathway and network approaches for identification of cancer signature markers from omics data

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.