seekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data.

Over the past two decades, researchers have discovered a special form of alternative splicing that produces a circular form of RNA. Although these circular RNAs (circRNAs) have garnered considerable attention in the scientific community for their biogenesis and functions, the focus of current studies has been on the tissue-specific circRNAs that exist only in one tissue but not in other tissues or on the disease-specific circRNAs that exist in certain disease conditions, such as cancer, but not under normal conditions. This approach was conducted in the relative absence of methods that analyze a group of common circRNAs that exist in both conditions, but are more abundant in one condition relative to another (differentially expressed).

Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities.

Motivation: Deep learning architectures have recently demonstrated their power in predicting DNA- and RNA-binding specificity. Existing methods fall into three classes: Some are based on convolutional neural networks (CNNs), others use recurrent neural networks (RNNs) and others rely on hybrid architectures combining CNNs and RNNs. However, based on existing studies the relative merit of the various architectures remains unclear.

circDeep: deep learning approach for circular RNA classification from other long non-coding RNA.

Motivation: Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientific studies due to its major role as a microRNA (miRNA) activity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is vital to understanding their biogenesis and purpose. Prediction of circular RNA can be achieved in three steps: distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs and predicting circular RNAs from other long non-coding RNAs (lncRNAs).

Robust observer based Fault Tolerant Tracking Control for T-S uncertain systems subject to sensor and actuator faults.

This paper investigates the sensor and actuator fault (SAF) estimations and the fault tolerant tracking control (FTTC) problem for uncertain systems represented by Takagi-Sugeno (T-S) fuzzy model affected by Unknown Bounded Disturbance (UBD). A robust descriptor adaptive observer based FTTC is firstly designed to simultaneously estimate the unmeasurable system states and SAF vectors and then to track reference trajectories. Sufficient design conditions are developed in terms of Linear Matrix Inequalities (LMIs).