Formal concept analysis for knowledge discovery from biological data

Raza, Khalid

doi:10.1504/ijdmb.2017.088138

Cited by 11 publications

(6 citation statements)

References 19 publications

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“…Here, the data items may present either highly expressed or repressed genes, or any other facts that state the cellular environment of genes (e.g. diagnosis of a disease samples) (Raza, 2015). Formal Concept Analysis (FCA), introduced by R. Wille in early 1980s, is another data mining technique based on lattice theory.…”

Section: Other Ai Based Methodsmentioning

confidence: 99%

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Analysis of Microarray Data using Artificial Intelligence Based Techniques

Raza

2016

Handbook of Research on Computational Intelligence Applications in Bioinformatics

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Microarray is one of the essential technologies used by the biologist to measure genome-wide expression levels of genes in a particular organism under some particular conditions or stimuli. As microarrays technologies have become more prevalent, the challenges of analyzing these data for getting better insight about biological processes have essentially increased. Due to availability of artificial intelligence based sophisticated computational techniques, such as artificial neural networks, fuzzy logic, genetic algorithms, and many other nature-inspired algorithms, it is possible to analyse microarray gene expression data in more better way. Here, we reviewed artificial intelligence based techniques for the analysis of microarray gene expression data. Further, challenges in the field and future work direction have also been suggested.

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Section: Other Ai Based Methodsmentioning

confidence: 99%

“…Like other computational techniques, FCA has also been applied in microarray analysis, gene expression mining, gene expression clustering, finding genes in gene regulatory networks, and so on. A review FAC for the analysis and knowledge discovery from gene expression and other biological data can be found in Raza (2015).…”

Section: Other Ai Based Methodsmentioning

confidence: 99%

Analysis of Microarray Data using Artificial Intelligence Based Techniques

Raza

2016

Handbook of Research on Computational Intelligence Applications in Bioinformatics

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“…Formal Concept Analysis has been constructively applied for knowledge discovery from biological data [11][12][13].…”

Section: Formal Concept Analysismentioning

confidence: 99%

Conceptual Approach to Clustering in the Study of Gene Expression

Bogatyrev¹,

Samodurov²

2018

Proceedings of the International Conference "Mathematical Biology and Bioinformatics"

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The work contains an overview of clustering methods used in the study of gene expression and new results of application of Formal Concepts Analysis to clustering data extracted from the public functional genomics data repository GEO. Methods of Formal Concept Analysis allow clustering of multidimensional data under the single condition of partial ordering of such data sets. As a result, clusters are separate sublattices in the concept lattice, where each sublattice contains hierarchically related formal concepts. This solution of the clustering problem allows deep investigating both mutual influences of genes and their influence on other data obtained in experiments on gene expression. The paper describes a new information technology developed for the implementation of the proposed approach. The technology uses modern solutions in the field of Big Data processing, it has functions for communication with external data sources and other information systems. Preliminary results of the application of this technology to three-dimensional gene expression data obtained from the GEO system are presented.

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“…Starting with illustrative examples of application of crisp lattices given in Reference [1], there appeared many serious works in which concept lattices were used in the research of medical-related problems [16][17][18], etc. ; problems related to biology [19,20], etc. ; social type problems [21], etc.…”

Section: Introductionmentioning

confidence: 99%

Gradation of Fuzzy Preconcept Lattices

Šostak

Uljane

Krastiņš

2021

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Noticing certain limitations of concept lattices in the fuzzy context, especially in view of their practical applications, in this paper, we propose a more general approach based on what we call graded fuzzy preconcept lattices. We believe that this approach is more adequate for dealing with fuzzy information then the one based on fuzzy concept lattices. We consider two possible gradation methods of fuzzy preconcept lattice—an inner one, called D-gradation and an outer one, called M-gradation, study their properties, and illustrate by a series of examples, in particular, of practical nature.

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Formal concept analysis for knowledge discovery from biological data

Cited by 11 publications

References 19 publications

Analysis of Microarray Data using Artificial Intelligence Based Techniques

Analysis of Microarray Data using Artificial Intelligence Based Techniques

Conceptual Approach to Clustering in the Study of Gene Expression

Gradation of Fuzzy Preconcept Lattices

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