Clustering and Classification Techniques for Gene Expression Profile Pattern Analysis

Weitschek, Emanuel; Fiscon, Giulia; Fustaino, Valentina; Felici, G.; Bertolazzi, Paola

doi:10.1002/9781119078845.ch19

Cited by 4 publications

(3 citation statements)

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“…In the literature, works [18][19][20][21][22][23][24] have shown that, despite the high number of dimensions in RNA sequencing, DNA and Gene Expression Profiles datasets, an accurate classification is feasible if class imbalance is treated appropriately.…”

Section: Dataset Descriptionmentioning

confidence: 99%

LICIC: Less Important Components for Imbalanced Multiclass Classification

2018

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Multiclass classification in cancer diagnostics, using DNA or Gene Expression Signatures, but also classification of bacteria species fingerprints in MALDI-TOF mass spectrometry data, is challenging because of imbalanced data and the high number of dimensions with respect to the number of instances. In this study, a new oversampling technique called LICIC will be presented as a valuable instrument in countering both class imbalance, and the famous “curse of dimensionality” problem. The method enables preservation of non-linearities within the dataset, while creating new instances without adding noise. The method will be compared with other oversampling methods, such as Random Oversampling, SMOTE, Borderline-SMOTE, and ADASYN. F1 scores show the validity of this new technique when used with imbalanced, multiclass, and high-dimensional datasets.

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Section: Dataset Descriptionmentioning

confidence: 99%

LICIC: Less Important Components for Imbalanced Multiclass Classification

2018

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“…In this work, we focus on the RNA sequencing (RNA-seq) NSG experiment [ 8 ] for quantifying the gene expression across the transcriptome in a given tissue [ 9 , 10 ]. Indeed, studying the quantification of the transcriptome enables to understand which genes are activated at different phases of the cell cycle or in the development of pathological conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Several rule-based machine learning methods are available for the analysis of gene expression data, e.g., [ 21 – 24 ]. The reader may find a more detailed survey of these methods in [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

CamurWeb: a classification software and a large knowledge base for gene expression data of cancer

et al. 2018

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BackgroundThe high growth of Next Generation Sequencing data currently demands new knowledge extraction methods. In particular, the RNA sequencing gene expression experimental technique stands out for case-control studies on cancer, which can be addressed with supervised machine learning techniques able to extract human interpretable models composed of genes, and their relation to the investigated disease. State of the art rule-based classifiers are designed to extract a single classification model, possibly composed of few relevant genes. Conversely, we aim to create a large knowledge base composed of many rule-based models, and thus determine which genes could be potentially involved in the analyzed tumor. This comprehensive and open access knowledge base is required to disseminate novel insights about cancer.ResultsWe propose CamurWeb, a new method and web-based software that is able to extract multiple and equivalent classification models in form of logic formulas (“if then” rules) and to create a knowledge base of these rules that can be queried and analyzed. The method is based on an iterative classification procedure and an adaptive feature elimination technique that enables the computation of many rule-based models related to the cancer under study. Additionally, CamurWeb includes a user friendly interface for running the software, querying the results, and managing the performed experiments. The user can create her profile, upload her gene expression data, run the classification analyses, and interpret the results with predefined queries. In order to validate the software we apply it to all public available RNA sequencing datasets from The Cancer Genome Atlas database obtaining a large open access knowledge base about cancer. CamurWeb is available at http://bioinformatics.iasi.cnr.it/camurweb.ConclusionsThe experiments prove the validity of CamurWeb, obtaining many classification models and thus several genes that are associated to 21 different cancer types. Finally, the comprehensive knowledge base about cancer and the software tool are released online; interested researchers have free access to them for further studies and to design biological experiments in cancer research.

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