Solving DNA motif discovery problem using improved Clonal selection algorithm with tournament selection operator

2019 ◽  
Author(s):  
Ezgi Deniz Ülker
Keyword(s):  
Motif Discovery ◽  
Clonal Selection ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Dna Motif ◽  
Tournament Selection ◽  
Dna Motif Discovery ◽  
Selection Operator

LAMARCKIAN LEARNING IN CLONAL SELECTION ALGORITHM FOR NUMERICAL OPTIMIZATION

2010 ◽  
Vol 19 (01) ◽  
pp. 19-37 ◽  
Author(s):  
MAOGUO GONG ◽  
LICHENG JIAO ◽  
JIE YANG ◽  
FANG LIU
Keyword(s):  
Local Search ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Benchmark Problems ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Tournament Selection ◽  
Selection Operator ◽  
Lamarckian Learning

In this paper, we introduce Lamarckian learning theory into the Clonal Selection Algorithm and propose a sort of Lamarckian Clonal Selection Algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compare LCSA with the Clonal Selection Algorithm in solving twenty benchmark problems to evaluate the performance of LCSA. The results demonstrate that the Lamarckian local search makes LCSA more effective and efficient in solving numerical optimization problems.

Feature Level Fusion of Biometric Images Using Modified Clonal Selection Algorithm

2021 ◽  
Vol 8 (9) ◽  
pp. 518-526
Author(s):  
Adedeji, Oluyinka Titilayo ◽  
Amusan, Elizabeth Adedoyin ◽  
Alade, Oluwaseun. Modupe
Keyword(s):  
Recognition Accuracy ◽  
Clonal Selection ◽  
Selection Method ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Recognition Time ◽  
Feature Level Fusion ◽  
Tournament Selection ◽  
Level Fusion ◽  
Selection Phase

In feature level fusion, biometric features must be combined such that each trait is combined so as to maintain feature-balance. To achieve this, Modified Clonal Selection Algorithm was employed for feature level fusion of Face, Iris and Fingerprints. Modified Clonal Selection Algorithm (MCSA) which is characterized by feature-balance maintenance capability and low computational complexity was developed and implemented for feature level fusion. The standard Tournament Selection Method (TSM) was modified by performing tournaments among neighbours rather than by random selection to reduce the between-group selection pressure associated with the standard TSM. Clonal Selection algorithm was formulated by incorporating the Modified Tournament Selection Method (MTSM) into its selection phase. Quantitative experimental results showed that the systems fused with MCSA has a higher recognition accuracy than those fused with CSA, also with a lower recognition time. Keywords: Biometrics, Feature level Fusion, Multibiometrics, Modified Clonal Selection Algorithm, Recognition Accuracy, Recognition Time.

scholarly journals CpGmotifs: a tool to discover DNA motifs associated to CpG methylation events

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Giovanni Scala ◽  
Antonio Federico ◽  
Dario Greco
Keyword(s):  
Dna Methylation ◽  
Motif Discovery ◽  
Cpg Methylation ◽  
Functional Interpretation ◽  
Dna Motifs ◽  
Molecular Alterations ◽  
Link Type ◽  
Dna Motif ◽  
Quantitative Manner ◽  
Dna Motif Discovery

Abstract Background The investigation of molecular alterations associated with the conservation and variation of DNA methylation in eukaryotes is gaining interest in the biomedical research community. Among the different determinants of methylation stability, the DNA composition of the CpG surrounding regions has been shown to have a crucial role in the maintenance and establishment of methylation statuses. This aspect has been previously characterized in a quantitative manner by inspecting the nucleotidic composition in the region. Research in this field still lacks a qualitative perspective, linked to the identification of certain sequences (or DNA motifs) related to particular DNA methylation phenomena. Results Here we present a novel computational strategy based on short DNA motif discovery in order to characterize sequence patterns related to aberrant CpG methylation events. We provide our framework as a user-friendly, shiny-based application, CpGmotifs, to easily retrieve and characterize DNA patterns related to CpG methylation in the human genome. Our tool supports the functional interpretation of deregulated methylation events by predicting transcription factors binding sites (TFBS) encompassing the identified motifs. Conclusions CpGmotifs is an open source software. Its source code is available on GitHub https://github.com/Greco-Lab/CpGmotifs and a ready-to-use docker image is provided on DockerHub at https://hub.docker.com/r/grecolab/cpgmotifs.

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