In this paper, we propose a hybrid neurogenetic system for stock trading. A recurrent neural network (NN) having one hidden layer is used the prediction model. The input features are generated from number of technical indicators being by financial experts. genetic algorithm (GA) optimizes NN's weights under 2-D encoding and crossover. We devised context-based ensemble method NNs which dynamically changes on basis test day's context. To reduce time in processing mass data, parallelized GA...

Abstract An incoherent feed‐forward loop (FFL) is one of the most‐frequently observed motifs in biomolecular regulatory networks. It has been thought that FFL designed simply to induce a transient response shaped by ‘fast activation and delayed inhibition’. We find dynamics various FFLs can be further classified into two types: time‐dependent biphasic responses dose‐dependent responses. Why do structurally identical play such different dynamical roles? Through computational studies, we show...

Abstract Motivation: It has been widely reported that biological networks are robust against perturbations such as mutations. On the contrary, it also known often fragile unexpected There is a growing interest in these intriguing observations and underlying design principle causes but characteristics of networks. For relatively small networks, feedback loop considered an important motif for realizing robustness. still, however, not clear how number coupled loops actually affect robustness...

Background Inferring a gene regulatory network from time-series expression data in systems biology is challenging problem. Many methods have been suggested, most of which scalability limitation due to the combinatorial cost searching set genes. In addition, they focused on accurate inference structure only. Therefore, there pressing need develop method search genes efficiently and predict dynamics accurately. Results this study, we employed Boolean model with restricted update rule scheme...

Inferring a gene regulatory network from time-series expression data is fundamental problem in systems biology, and many methods have been proposed. However, most of them were not efficient inferring relations involved by large number genes because they limited the or computed an approximated reliability multivariate relations. Therefore, improved method needed to efficiently search more generalized scalable relations.In this study, we propose genetic algorithm-based Boolean inference...

Abstract Cellular circuits have positive and negative feedback loops that allow them to respond properly noisy external stimuli. It is intriguing such exist in many cases a particular form of coupled with different time delays. As result our mathematical simulations investigations into various experimental evidences, we found can rapidly turn on reaction proper stimulus, robustly maintain its status, immediately off the when stimulus disappears. In other words, enable cellular systems...

An algorithmic approach enables the simplification of complex signaling networks and identifies potential therapeutic targets.

Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis also pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) segmentation. We first remove impulsive noise inherent MR images by utilizing vector median filter. Subsequently, Otsu thresholding used as initial coarse that finds the homogeneous regions of input image. Finally, enhanced suppressed fuzzy c-means to partition brain into multiple segments, which...

Abstract Motivation:It is widely accepted that cell signaling networks have been evolved to be robust against perturbations. To investigate the topological characteristics resulting in such robustness, we examined large-scale and found a number of feedback loops are present mostly coupled structures. In particular, coupling was made coherent way implying same types interlinked together. Results: We investigated role coherently through extensive Boolean network simulations high proportion...

Many biological networks such as protein-protein interaction networks, signaling and metabolic have topological characteristics of a scale-free degree distribution. Preferential attachment has been considered the most plausible evolutionary growth model to explain this property. Although various studies undertaken investigate structural network obtained using model, its dynamical received relatively less attention. In paper, we focus on robustness that is acquired during process. Through...

Abstract Motivation: Many studies have investigated the relationship between structural properties and dynamic behaviors in biological networks. In particular, feedback loop (FBL) feedforward (FFL) structures received a great deal of attention. One interesting common property FBL FFL is their coherency coupling. However, role coherent FFLs relation to network robustness not fully known, whereas that FBLs has been well established. Results: To establish are abundant networks, we examined gene...

It has been a challenge in systems biology to unravel relationships between structural properties and dynamic behaviors of biological networks. A Cytoscape plugin named NetDS was recently proposed analyze the robustness-related dynamics feed-forward/feedback loop structures Despite such useful function, limitations on network size that can be analyzed exist due high computational costs. In addition, cannot verify an intrinsic property which induced by observed result because it no function...

Many biological networks tend to have a high modularity structural property and the dynamic characteristic of robustness against perturbations. However, relationship between is not well understood. To investigate this relationship, we examined real signalling conducted simulations using random Boolean network model. As result, first observed that negatively correlated with modularity. In particular, negative correlation becomes more apparent as density sparser. Even interesting that, occurs...

Abstract Summary: NetDS is a novel Cytoscape plugin that conveniently simulates dynamics related to robustness, and examines structural properties with respect feedforward/feedback loops. It can evaluate how robustly network sustains stable state against mutations by employing Boolean model. In addition, the examine all loops appearing in determine whether or not pair of coupled. Random networks also be generated an interesting finding real biological significantly random. Availability:...

Abstract Motivation It is a challenging problem in systems biology to infer both the network structure and dynamics of gene regulatory from steady-state expression data. Some methods based on Boolean or differential equation models have been proposed but they were not efficient inference large-scale networks. Therefore, it necessary develop method accurately networks using expression. Results In this study, we propose novel constrained genetic algorithm-based (CGA-BNI) where canalyzing...

In this paper, we propose a neuro-genetic stock prediction system based on financial correlation between companies. A number of input variables are produced from the relatively highly correlated The genetic algorithm selects set informative features among them for recurrent neural network. It showed notable improvement over not only buy-and-hold strategy but also network using target company.

There have been many machine learning-based studies to forecast stock price trends. These attempted extract input features mostly from the information with little focus on trading volume information. In addition, modeling parameters specify a learning problem not intensively investigated. We herein develop an improved method by handling those limitations. Specifically, we generated variables considering both and even weight. also defined three parameters: target window sizes profit...

Abstract Motivation: In general, diseases are more likely to be comorbid if they share associated genes or molecular interactions in a cellular process. However, there still number of pairs which show relatively high comorbidity but do not any interactions. This observation raises the need for novel factor can explain underlying mechanism comorbidity. We here consider feedback loop (FBL) structure ubiquitously found human cell signaling network as key motif phenomenon, since it is well known...

Machinery diagnostics and prognostics usually involve the prediction process of fault-types remaining useful life (RUL) a machine, respectively. The developing data-driven method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, machine learning. In general, best performing algorithm optimal hyper-parameters suitable for each subtask are varied across characteristics datasets. Therefore, it is challenging to develop general...

Abstract Background A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain functional importance network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need investigate another measure better describe In respect, we considered feedback loop which ubiquitously found in various networks. Results We discovered...

Abstract Summary In systems biology, it is challenging to accurately infer a regulatory network from time-series gene expression data, and variety of methods have been proposed. Most them were computationally inefficient in inferring very large networks, though, because the increasing number candidate genes. Although recent approach called GABNI (genetic algorithm-based Boolean inference) was presented resolve this problem using genetic algorithm, there room for performance improvement...