scholarly journals A Web Server for Designing Molecular Switches Composed of Two Interacting RNAs

2021 ◽  
Vol 22 (5) ◽  
pp. 2720
Author(s):  
Akito Taneda ◽  
Kengo Sato
Keyword(s):  
In Silico ◽  
Web Server ◽  
Solution Space ◽  
Rna Sequences ◽  
Design Algorithm ◽  
Multi Objective ◽  
Rna Switches ◽  
Benchmark Datasets ◽  
Switch Design ◽  
Rna Interaction

The programmability of RNA–RNA interactions through intermolecular base-pairing has been successfully exploited to design a variety of RNA devices that artificially regulate gene expression. An in silico design for interacting structured RNA sequences that satisfies multiple design criteria becomes a complex multi-objective problem. Although multi-objective optimization is a powerful technique that explores a vast solution space without empirical weights between design objectives, to date, no web service for multi-objective design of RNA switches that utilizes RNA–RNA interaction has been proposed. We developed a web server, which is based on a multi-objective design algorithm called MODENA, to design two interacting RNAs that form a complex in silico. By predicting the secondary structures with RactIP during the design process, we can design RNAs that form a joint secondary structure with an external pseudoknot. The energy barrier upon the complex formation is modeled by an interaction seed that is optimized in the design algorithm. We benchmarked the RNA switch design approaches (MODENA+RactIP and MODENA+RNAcofold) for the target structures based on natural RNA-RNA interactions. As a result, MODENA+RactIP showed high design performance for the benchmark datasets.

scholarly journals ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xujun Zhang ◽  
Chao Shen ◽  
Xueying Guo ◽  
Zhe Wang ◽  
Gaoqi Weng ◽  
...  
Keyword(s):  
High Efficiency ◽  
Low Cost ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Web Server ◽  
Scoring Functions ◽  
Protein Ligand Interactions ◽  
Prediction Module ◽  
Ligand Interactions ◽  
Benchmark Datasets

AbstractVirtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

scholarly journals Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3356
Author(s):  
Mustafa Hasan Albowarab ◽  
Nurul Azma Zakaria ◽  
Zaheera Zainal Abidin
Keyword(s):  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Mathematical Formulation ◽  
Particle Swarm ◽  
Solution Space ◽  
Particle Swarm Optimisation ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Crowding Distance ◽  
Standard Models

Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and execution cost. While past studies have evaluated load balancing from one or two aspects, none has explored the possibility of simultaneously optimising all aspects, namely, reliability, energy, cost, and execution time. For the purposes of load balancing, implementing multi-objective optimisation (MOO) based on meta-heuristic searching algorithms requires assurances that the solution space will be thoroughly explored. Optimising load balancing provides not only decision makers with optimised solutions but a rich set of candidate solutions to choose from. Therefore, the purposes of this study were (1) to propose a joint mathematical formulation to solve load balancing challenges in cloud computing and (2) to propose two multi-objective particle swarm optimisation (MP) models; distance angle multi-objective particle swarm optimization (DAMP) and angle multi-objective particle swarm optimization (AMP). Unlike existing models that only use crowding distance as a criterion for solution selection, our MP models probabilistically combine both crowding distance and crowding angle. More specifically, we only selected solutions that had more than a 0.5 probability of higher crowding distance and higher angular distribution. In addition, binary variants of the approaches were generated based on transfer function, and they were denoted by binary DAMP (BDAMP) and binary AMP (BAMP). After using MOO mathematical functions to compare our models, BDAMP and BAMP, with state of the standard models, BMP, BDMP and BPSO, they were tested using the proposed load balancing model. Both tests proved that our DAMP and AMP models were far superior to the state of the art standard models, MP, crowding distance multi-objective particle swarm optimisation (DMP), and PSO. Therefore, this study enables the incorporation of meta-heuristic in the management layer of cloud networks.

Reliability/risk centered cost effective preventive maintenance planning of generating units

2018 ◽  
Vol 35 (9) ◽  
pp. 2052-2079 ◽  
Author(s):  
Umamaheswari E. ◽  
Ganesan S. ◽  
Abirami M. ◽  
Subramanian S.
Keyword(s):  
Preventive Maintenance ◽  
Optimization Technique ◽  
Solution Space ◽  
Fuzzy Decision Making ◽  
Content Type ◽  
Multi Objective ◽  
Statistical Measures ◽  
Highly Nonlinear ◽  
Optimal Maintenance ◽  
Maintenance Schedules

Purpose Finding the optimal maintenance schedules is the primitive aim of preventive maintenance scheduling (PMS) problem dealing with the objectives of reliability, risk and cost. Most of the earlier works in the literature have focused on PMS with the objectives of leveling reserves/risk/cost independently. Nevertheless, very few publications in the current literature tackle the multi-objective PMS model with simultaneous optimization of reliability, and economic perspectives. Since, the PMS problem is highly nonlinear and complex in nature, an appropriate optimization technique is necessary to solve the problem in hand. The paper aims to discuss these issues. Design/methodology/approach The complexity of the PMS problem in power systems necessitates a simple and robust optimization tool. This paper employs the modern meta-heuristic algorithm, namely, Ant Lion Optimizer (ALO) to obtain the optimal maintenance schedules for the PMS problem. In order to extract best compromise solution in the multi-objective solution space (reliability, risk and cost), a fuzzy decision-making mechanism is incorporated with ALO (FDMALO) for solving PMS. Findings As a first attempt, the best feasible maintenance schedules are obtained for PMS problem using FDMALO in the multi-objective solution space. The statistical measures are computed for the test systems which are compared with various meta-heuristic algorithms. The applicability of the algorithm for PMS problem is validated through statistical t-test. The statistical comparison and the t-test results reveal the superiority of ALO in achieving improved solution quality. The numerical and statistical results are encouraging and indicate the viability of the proposed ALO technique. Originality/value As a maiden attempt, FDMALO is used to solve the multi-objective PMS problem. This paper fills the gap in the literature by solving the PMS problem in the multi-objective framework, with the improved quality of the statistical indices.

scholarly journals Systemic In Silico Screening in Drug Discovery for Coronavirus Disease (COVID-19) with an Online Interactive Web Server

2020 ◽  
Vol 60 (12) ◽  
pp. 5735-5745 ◽  
Author(s):  
Chi Xu ◽  
Zunhui Ke ◽  
Chuandong Liu ◽  
Zhihao Wang ◽  
Denghui Liu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Web Server ◽  
In Silico Screening

scholarly journals Cordon Pricing Considering Air Pollutants Emission

2016 ◽  
Vol 28 (2) ◽  
pp. 179-189
Author(s):  
Shahriar Afandizadeh ◽  
Seyed Ebrahim Abdolmanafi
Keyword(s):  
Optimal Design ◽  
Air Pollutants ◽  
Solution Space ◽  
Solution Algorithm ◽  
Multi Objective ◽  
Pricing Scheme ◽  
Strength Pareto Evolutionary Algorithm ◽  
Pollutants Emission ◽  
Objective Model

This paper considers the issue of air pollutants emission for the optimal and sustainable determination of cordon location, toll level, and price of park and ride (P&R). Although air pollutants emission decreases within the cordon by the implementation of cordon pricing scheme, it may increase outside the cordon and the whole network. Hence, air pollutants emission may only transfer from inside of the cordon to its outside. Therefore, in this paper, a multi-objective bi-level optimization model is developed. A solution algorithm is also presented based on the second version of strength Pareto evolutionary algorithm (SPEA2). The results reveal that this multi-objective model can be a useful tool for the sustainable and optimal design of the cordon and P&R scheme. In addition, cordon pricing is a multi-objective problem. Therefore, it is necessary to consider air pollutants emission. By choosing another non-dominated result in the solution space, air pollutants emission outside the cordon and the whole network can be reduced without a significant reduction in social welfare.

scholarly journals 3dRNA v2.0: An Updated Web Server for RNA 3D Structure Prediction

2019 ◽  
Vol 20 (17) ◽  
pp. 4116 ◽  
Author(s):  
Jun Wang ◽  
Jian Wang ◽  
Yanzhao Huang ◽  
Yi Xiao
Keyword(s):  
Structure Prediction ◽  
3D Structure ◽  
Web Server ◽  
Structure Optimization ◽  
Optimization Procedure ◽  
Biological Functions ◽  
Rna Sequences ◽  
3D Structures ◽  
3D Structure Prediction ◽  
Sampling Structure

3D structures of RNAs are the basis for understanding their biological functions. However, experimentally solved RNA 3D structures are very limited in comparison with known RNA sequences up to now. Therefore, many computational methods have been proposed to solve this problem, including our 3dRNA. In recent years, 3dRNA has been greatly improved by adding several important features, including structure sampling, structure ranking and structure optimization under residue-residue restraints. Particularly, the optimization procedure with restraints enables 3dRNA to treat pseudoknots in a new way. These new features of 3dRNA can greatly promote its performance and have been integrated into the 3dRNA v2.0 web server. Here we introduce these new features in the 3dRNA v2.0 web server for the users.

Generalized Disturbance Observer Synthesis Using a Two-Stage Heuristic Algorithm

2017 ◽  
Author(s):  
Guofei Xiang ◽  
Jianbo Su
Keyword(s):  
Disturbance Observer ◽  
Filter Design ◽  
Mimo Systems ◽  
Solution Space ◽  
The Novel ◽  
Two Stage ◽  
Design Algorithm ◽  
Reduced Order ◽  
Kyp Lemma ◽  
Low Pass

Disturbance observer (DOB) based control has been widely applied in industries due to its easy usage but powerful disturbance rejection ability. However, the existence of innate structure constraint, namely the inverse of the nominal plant, prevents its implementation on more general class of systems, such as non-minimum phase plants, MIMO systems etc.. Furthermore, additional limitations exerted on Q-filter design, i.e., unity steady state gain and low-pass nature, which narrow down its solution space largely and prevent from achieving optimal performance even if it exists. In this paper, we present a novel DOB architecture, named generalized disturbance observer (G-DOB), with the help of nontraditional use of the celebrated Youla parametrization of two degree-of-freedom controller. Rigorous analyses show that the novel G-DOB not only inherits all the merits of the conventional one, but also alleviates the limitations stated before partially. By some appropriate system manipulation, the synthesis of Q-filter has been converted to the design of reduced-order controller. Thus, a heuristic two-stage algorithm has been developed with the help of Kalman-Yakubovich-Popov (KYP) lemma: firstly design a full information controller for the augmented system and then compute a reduced-order controller. Numerical examples are presented to demonstrate the effectiveness of the proposed G-DOB structure and design algorithm.

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