Explaining Ovarian Cancer Gene Expression Profiles with Fuzzy Rules and Genetic Algorithms

Consiglio, Arianna; Casalino, Gabriella; Castellano, Giovanna; Grillo, Giorgio; Perlino, Elda; Vessio, Gennaro; Licciulli, Flavio

doi:10.3390/electronics10040375

Cited by 11 publications

(8 citation statements)

References 32 publications

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“…Inference: is a rule‐based method where the main logic from an expert is interpreted by If–then rules in linguistic mode 54 . The establishment of these rules is defined in Table 2 which relates the condition of the error and its derivative for consequent action.…”

Section: Methodsmentioning

confidence: 99%

“…Inference: is a rule-based method where the main logic from an expert is interpreted by If-then rules in linguistic mode. 54 The establishment of these rules is defined in Table 2 which relates the condition of the error and its derivative for consequent action. The values, that range between −1 and 1, are established and discretized as: negative big (NB, −1), negative medium (NM, −0.66), negative small (NS, −0.33), zero (Z, 0), positive small (PS, 0.33), positive medium (PM, 0.66) and positive big (PB, 1).…”

Section: ( ( )) }mentioning

confidence: 99%

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Experimental validation of fuzzy type‐2 against type‐1 scheme applied in DC/DC converter integrated to a PEM fuel cell system

Napole

Derbeli

Barambones

2022

Energy Science & Engineering

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This research presents and compares the outcomes of experimental implementations of different fuzzy logic control structures for a proton exchange membrane fuel cell (PEMFC). These devices are well known for their capability to transform chemical energy into electrical with low emissions. Commonly, a PEMFC has a linkage with a boost converter which allows a suitable end‐user voltage through a nonlinear control law. Hence, the contribution in this sense is the experimental comparison of two fuzzy logic strategies known as type‐1 and type‐2 that were implemented in a PEMFC system. The approaches were embedded in a control board dSPACE 1102 which also has the capability to acquire data. The contrast of results showed capabilities improvement against disturbances in terms of error reduction, control signal, and robustness.

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Section: Methodsmentioning

confidence: 99%

Section: ( ( )) }mentioning

confidence: 99%

Experimental validation of fuzzy type‐2 against type‐1 scheme applied in DC/DC converter integrated to a PEM fuel cell system

Napole

Derbeli

Barambones

2022

Energy Science & Engineering

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“…Consiglio et al used Fuzzy Rules with GA to separate ovarian cancer and other ovarian diseases [ 28 ]. Here, GA was employed for the feature selection phase with if–then rules.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Bostancı

Koçak

Ünal

et al. 2023

Sensors

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Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.

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“…Arrieta et al [36] provided an analysis of previous studies contributing to the domain of XAI. Consiglio et al [37] applied fuzzy rules to explain the gene expression of ovarian cancer. Lios et al [38] predicted surgical cytoreduction in ovarian cancer and also provided a model explanation using SHAP (Copyright 2018, Scott Lundberg, Built with Sphinx and GoogleDeepMind location: Seattle, DC, USA).…”

Section: Related Workmentioning

confidence: 99%

Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI

Abuzinadah,

Kumar Posa,

Alarfaj

et al. 2023

Cancers

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The importance of detecting and preventing ovarian cancer is of utmost significance for women’s overall health and wellness. Referred to as the “silent killer,” ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models.

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Explaining Ovarian Cancer Gene Expression Profiles with Fuzzy Rules and Genetic Algorithms

Cited by 11 publications

References 32 publications

Experimental validation of fuzzy type‐2 against type‐1 scheme applied in DC/DC converter integrated to a PEM fuel cell system

Experimental validation of fuzzy type‐2 against type‐1 scheme applied in DC/DC converter integrated to a PEM fuel cell system

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI

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