ArticleInformatics in medicine unlocked2016
World Competitive Contests (WCC) algorithm: A novel intelligent optimization algorithm for biological and non-biological problems.
Article in Informatics in medicine unlocked, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.Bioinformatics (Oxford, England) · 2026Article
- A novel early stage drip irrigation system cost estimation model based on management and environmental variables.Scientific reports · 2025Article
- A voting-based machine learning approach for classifying biological and clinical datasets.BMC bioinformatics · 2023Article
- Article
- Synthetic repurposing of drugs against hypertension: a datamining method based on association rules and a novel discrete algorithm.BMC bioinformatics · 2020Article
- Trader as a new optimization algorithm predicts drug-target interactions efficiently.Scientific reports · 2019Article
- FeatureSelect: a software for feature selection based on machine learning approaches.BMC bioinformatics · 2019Article
- Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.Iranian journal of pharmaceutical research : IJPR · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since different sciences face lots of problems which cannot be solved in reasonable time order, we need new methods and algorithms for getting acceptable answers in proper time order. In the present study, a novel intelligent optimization algorithm, known as WCC (World Competitive Contests), has been proposed and applied to find the transcriptional factor binding sites (TFBS) and eight benchmark functions discovery processes. We recognize the need to introduce an intelligent optimization algorithm because the TFBS discovery is a biological and an NP-Hard problem. Although there are some intelligent algorithms for the purpose of solving the above-mentioned problems, an optimization algorithm with good and acceptable performance, which is based on the real parameters, is essential. Like the other optimization algorithms, the proposed algorithm starts with the first population of teams. After teams are put into different groups, they will begin competing against their rival teams. The highly qualified teams will ascend to the elimination stage and will play each other in the next rounds. The other teams will wait for a new season to start. In this paper, we're going to implement our proposed algorithm and compare it with five famous optimization algorithms from the perspective of the following
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.