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About 10,000 papers for “Clonal selection”
2025
Citation: 'clonal selection' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.13016 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for commercial usage of the compendium should be directed to IUPAC.
Cited by 0
Claude Sammut, Geoffrey I. Webb · Encyclopedia of Machine Learning · 2010
Cited by 0
A Baserga · BMJ · 1960
Cited by 31
Chris McEwan, Emma Hart · Theoretical Computer Science · 2010
Cited by 4
Jason Brownlee · Swinburne Research Bank (Swinburne University of Technology) · 2007
Inspired by Darwin’s theory of natural selection to explain the diversity and adaptability of life, Burnet’s clonal selection theory explains the diversity and learning properties of the acquired immune system of vertebrates. In a similar mirroring manner to the field of evolutionary computation that attempts to use the principles of the Darwinian theory and genetics to address practical engineering problems a new field of study called ‘Clonal Selection Algorithms’ has emerged that attempts the same task by abstracting and applying the principles of Burnet’s foundational immunological theory. This paper provides a summary of this new field of clonal selection algorithms and proposes an algorithm taxonomy a standardized nomenclature and a general model of such algorithms. Finally the field is compared and contrasted to the field of evolutionary computation and general research trends are discussed.
Cited by 41Open access
Shaun R. McCann · Bone Marrow Transplantation · 2020
Cited by 1Open access
Jason Brownlee · Swinburne Research Bank (Swinburne University of Technology) · 2005
The vertebrate immune system is a robust and powerful information process system that demonstrates features such as distributed control, parallel processing and adaptation and learning via experience. Artificial Immune Systems (AIS) are machine-learning algorithms that embody some of the principles and attempt to take advantages of the benefits of natural immune systems for use in tackling complex problem domains. The Clonal Selection Algorithm (CLONALG) is one such system inspired by the clonal selection theory of acquired immunity which has shown success on broad range of engineering problem domains. The focus of this work is the CLONALG algorithm specifically the techniques history previous research and algorithm function. In addition this work seeks to take desirable elements from CLONALG implementations and devise a clonal selection based classification algorithm. Competence with the CLONALG algorithm is demonstrated in terms of theory and application. The algorithm is analysed from the perspective of desirable features for an immune inspired classifier and a number a new clonal selection inspired technique called the Clonal Selection Classification Algorithm (CSCA) is proposed designed specified and preliminary tested. The technique is shown to be robust self-tuning (in terms of resources used) insensitive to user parameters and competitive as a classification system on common problem domains. An implementation of the CSCA is provided as a plug-in for the WEKA machinelearning workbench as is a naïve implementation of the CLONALG algorithm for classification.
Cited by 39Open access
Haifeng Du, Xiaoyi Jin, Jian Zhuang et al. · Lecture notes in computer science · 2004
Cited by 2
Jason Brownlee, Jason Brownlee · 2005
The vertebrate immune system is a robust and powerful information process system that demonstrates features such as distributed control, parallel processing and adaptation and learning via experience. Artificial Immune Systems (AIS) are machine-learning algorithms that embody some of the principles and attempt to take advantages of the benefits of natural immune systems for use in tackling complex problem domains. The Clonal Selection Algorithm (CLONALG) is one such system inspired by the clonal selection theory of acquired immunity, which has shown success on broad range of engineering problem domains. The focus of this work is the CLONALG algorithm, specifically the techniques history, previous research and algorithm function. In addition, this work seeks to take desirable elements from CLONALG implementations and devise a clonal selection based classification algorithm. Competence with the CLONALG algorithm is demonstrated in terms of theory and application. The algorithm is analysed from the perspective of desirable features for an immune inspired classifier and a number a new clonal selection inspired technique called the Clonal Selection Classification
Cited by 0
Licheng Jiao · 2008
Focusing on the time-consuming problem of wrapper feature selection when the feature subset is evaluated using high-complexity classifiers in pattern recognition,a novel parallel immune clonal selection for feature selection algorithm(PICFS) is proposed.The presented method uses an immune clonal selection for feature selection;fitness of feature subset fitness is determined by evaluating the nearest neighbor classifier with leave-one-out cross-validation in multiple computing nodes at the same time.Experimental results on several standards UCI dataset sets show that the proposed algorithm outperforms the conventional genetic algorithm and classical sequential floating forward search algorithm in terms of classification accuracy and greatly reduce the running time based on MPICH using the Linux blade cluster,we have achieved a speed-up as high as 29.57 even when up to 40 processors are used.
Cited by 2
Jean Philippe Roby, Laure de Rességuier, Cornelis van Leeuwen · HAL (Le Centre pour la Communication Scientifique Directe) · 2010
Cited by 0
Makhtar Niang (252570), Xue Yan Yam (267010), Peter Rainer Preiser (37134) · Figshare · 2015
<p>Clonal lines from <i>P. falciparum</i> 3D7 parasites were isolated by limiting dilution as described in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1000307#s4" target="_blank">Materials and Methods</a>. Whole field immunofluorescences co-staining of anti-PfSBP1 with either anti-S1 (A) or anti-S2 (B) antisera are shown for the four selected clones. Individual staining (dapi, anti-S1/S2, anti-SBP1) as well as merge images are shown for each clone.</p>
Cited by 0Open access
The Rockefeller University · Digital Commons - RU (Rockefeller University) · 2017
Abstract diagram of the clonal selection of B and T lymphocytes. Clonal selection theory is a scientific theory in immunology that explains the functions of cells (lymphocytes) of the immune system in response to specific antigens invading the body. The concept was introduced by the Australian doctor Frank Macfarlane Burnet in 1957, in an attempt to explain the formation of a diversity of antibodies during initiation of the immune response. The theory has become a widely accepted model for how the immune system responds to infection and how certain types of B and T lymphocytes are selected for a destruction of specific antigens.
Cited by 0
Kideok Jin, Saraswati Sukumar · Cancer Biology & Therapy · 2010
Commentary to:MCF-7 breast cancer cells selected for tamoxifen resistance acquire new phenotypes differing in DNA content, phospho-HER2 and PAX2 expression, and rapamycin sensitivityEuphemia Leung, Nagarajan Kannan, Geoffrey W. Krissansen, Michael P. Findlay and Bruce C. Baguley
Cited by 2
Jason Brownlee · Swinburne Research Bank (Swinburne University of Technology) · 2007
The clonal selection principle describes the selection and maturation of lymphocytes, accounting for the diversity of the antibodies they produce and the tolerance they have to selfantigens. The theory does not exist in isolation, the receptors, the host, and the antigens have a context that is commonly ignored when this principle is realised as a computational intelligence algorithm. This work attempts to provide context to the principle by introducing a spatial structure in which to house membrane bound receptors, and which simple clonal selection operations (as previously defined for the elaborated clonal selection algorithm) are applied.
Cited by 1Open access
Jason Brownlee · Swinburne Research Bank (Swinburne University of Technology) · 2007
A series of acquired immunity inspired clonal selection models have previously been proposed that includes principle model components and base (single repertoire) models. Also previously proposed is a contrived problem domain called ‘colour space’ and a number of scenarios intended for the investigation of adaptive models. This work units these two (until now) isolated streams of work by realizing the clonal selection models as clonal selection algorithms. The components and models are distilled into design principles and testable behavioural expectations. The algorithms are defined and a series of sensitivity analysis and experimental studies are proposed to preliminarily assess the proposed algorithms.
Cited by 3Open access
Yong Peng, Bao‐Liang Lu · Information Sciences · 2014
Cited by 34
Chris McEwan, Emma Hart · Lecture notes in computer science · 2010
Cited by 0Open access
P. Leclair · HAL (Le Centre pour la Communication Scientifique Directe) · 1995
*INRA Centre de Bordeaux (FRA) Diffusion du document : INRA Centre de Bordeaux (FRA)
Cited by 2
Eberhardt Weiler, Gerold Adam, W. Schüler et al. · 1976
Cited by 5