Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis

By Zhenyu Wang, Vasile Palade. 2011

34 citations recorded by the source corpus.

BackgroundAnalysing gene expression data from microarray technologies is a very important task in biology and medicine, and particularly in cancer diagnosis. Different from most other popular methods in high dimensional bio-medical data analysis, such as microarray gene expression or proteomics mass spectroscopy data analysis, fuzzy rule-based models can not only provide good classification results, but also easily be explained and interpreted in human understandable terms, by using fuzzy rules. However, the advantages offered by fuzzy-based techniques in microarray data analysis have not yet been fully explored in the literature. Although some recently developed fuzzy-based modeling approaches can provide satisfactory classification results, the rule bases generated by most of the reported fuzzy models for gene expression data are still too large to be easily comprehensible.ResultsIn this paper, we develop some Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) methods for analysing high dimensional bio-medical data sets, such as microarray gene expression data and proteomics mass spectroscopy data. We mainly focus on evaluating our proposed models on microarray gene expression cancer data sets, i.e., the lung cancer data set and the colon cancer data set, but we extend our investigations to other type of cancer data set, such as the ovarian cancer data set. The experimental studies have shown that relatively simple and small fuzzy rule bases, with satisfactory classification performance, can be successfully obtained for challenging microarray gene expression datasets.ConclusionsWe believe that fuzzy-based techniques, and in particular the methods proposed in this paper, can be very useful tools in dealing with high dimensional cancer data. We also argue that the potential of applying fuzzy-based techniques to microarray data analysis need to be further explored.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, background): “Very useful for building explainable systems in the medical domain is generally ge-netic programming (Koza, 1994; Pena-Reyes and Sipper, 1999; Tsakonas et al., 2004), and speciﬁcally evolutionary algorithms (Wang and Palade, 2011; Holzinger et al., 2014, 2016).”
Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) [283] consists instead of a fuzzy rule-based model engineered to classify gene expression data from microarray technologies.”
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, background): “As some examples, we could mention very high resolution airbourne imagery [31], forecasting of ozone levels [125], face recognition [78], and especially medical diagnosis [11,86,91,93,132].”