Systems biology in cardiovascular disease: a multiomics approach
Omics techniques generate large, multidimensional data that are amenable to analysis by
new informatics approaches alongside conventional statistical methods. Systems theories …
new informatics approaches alongside conventional statistical methods. Systems theories …
Similarity computation strategies in the microRNA-disease network: a survey
Various microRNAs have been demonstrated to play roles in a number of human diseases.
Several microRNA-disease network reconstruction methods have been used to describe the …
Several microRNA-disease network reconstruction methods have been used to describe the …
A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control
This paper investigates the multirate networked industrial process control problem in double-
layer architecture. First, the output tracking problem for sampled-data nonlinear plant at …
layer architecture. First, the output tracking problem for sampled-data nonlinear plant at …
Robot manipulator control using neural networks: A survey
Robot manipulators are playing increasingly significant roles in scientific researches and
engineering applications in recent years. Using manipulators to save labors and increase …
engineering applications in recent years. Using manipulators to save labors and increase …
A novel features ranking metric with application to scalable visual and bioinformatics data classification
Coming with the big data era, the filtering of uninformative data becomes emerging. To this
end, ranking the high dimensionality features plays an important role. However, most of the …
end, ranking the high dimensionality features plays an important role. However, most of the …
Deep recurrent neural networks with finite-time terminal sliding mode control for a chaotic fractional-order financial system with market confidence
Disturbances are inevitably found in almost every system and, if not rejected, they could
jeopardize the effectiveness of control methods. Thereby, employing state-of-the-art …
jeopardize the effectiveness of control methods. Thereby, employing state-of-the-art …
RNN for solving time-variant generalized Sylvester equation with applications to robots and acoustic source localization
A generalized Sylvester equation is a special formulation containing the Sylvester equation,
the Lyapunov equation and the Stein equation, which is often encountered in various fields …
the Lyapunov equation and the Stein equation, which is often encountered in various fields …
Finding the best classification threshold in imbalanced classification
Classification with imbalanced class distributions is a major problem in machine learning.
Researchers have given considerable attention to the applications in many real-world …
Researchers have given considerable attention to the applications in many real-world …
Stability analysis of quaternion-valued neural networks: decomposition and direct approaches
In this paper, we investigate the global stability of quaternion-valued neural networks
(QVNNs) with time-varying delays. On one hand, in order to avoid the noncommutativity of …
(QVNNs) with time-varying delays. On one hand, in order to avoid the noncommutativity of …
Improved and promising identification of human microRNAs by incorporating a high-quality negative set
MicroRNA (miRNA) plays an important role as a regulator in biological processes.
Identification of (pre-) miRNAs helps in understanding regulatory processes. Machine …
Identification of (pre-) miRNAs helps in understanding regulatory processes. Machine …