Enumerative algorithms for biclustering: expanding and exploring their potential in bioinformatics and neuroscience

• Grant number: 17/21174-8
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: January 01, 2018
• End date: April 02, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Fernando José von Zuben
• Grantee: Rosana Veroneze
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology, AP.CEPID
• Associated scholarship(s): 20/00123-9 - An integrated and cross-disciplinary approach towards biclustering and constraint programming, BE.EP.PD

Abstract

Biclustering has proven to be a powerful data analysis technique, with great success in various application domains. Recently, in the doctorate of the candidate for the scholarship, we proposed a family of biclustering algorithms endowed with unique properties and features, which have not been properly explored in application areas of great potential of contribution. This family has algorithms able to enumerate all maximal biclusters with (i) constant values on columns, (ii) constant values on rows, and (iii) coherent values in numerical data matrices. In addition, we also extend one of these algorithms to enumerate biclusters in mixed-data matrices (which may contain numerical and/or categorical attributes). This new algorithm is endowed with additional and more general features, but retains the performance characteristics of its predecessor. To make the scope of applications of these algorithms even greater, the first two challenges of this project are to make them even more computationally efficient and implement them for parallel execution using Graphics Processing Units (GPUs). Given that enumerative algorithms may return a very large number of biclusters, and not all of them are relevant for the data analysis, the second major focus of this project is on the selection / ranking of biclusters. In this way, we will be able to provide the user with a compact bicluster set that exhibits high relevance and low redundancy at the same time. We will also explore further the connection between biclustering and frequent pattern mining for the development of rule-based classifiers, more specifically, based on associations drawn from the biclusters. Finally, the last major objective of this project is to explore the great potential of applications of this family of algorithms, focusing on the analysis of gene expression data and brain activity data. For the first case, we have a partnership with Professor Raquel Scarel-Caminaga from FOAr-UNESP. The second case is linked to the project CEPID entitled BRAINN (Brazilian Institute of Neuroscience and Neurotechnology), and there is already a recent history of partnership with other groups linked to BRAINN.

Scientific publications

(5)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DAM, KHANH HUU THE; -WILSON, THOMAS GIVEN; LEGAY, AXEL; VERONEZE, ROSANA. Packer classification based on association rule mining. APPLIED SOFT COMPUTING, v. 127, p. 21-pg., 2022-08-04. (20/00123-9, 17/21174-8)

VERONEZE, ROSANA; CRUZ TFAILE CORBI, SAMIA; ROQUE DA SILVA, BARBARA; DE S. ROCHA, CRISTIANE; V. MAURER-MORELLI, CLAUDIA; PEREZ ORRICO, SILVANA REGINA; CIRELLI, JONI A.; VON ZUBEN, FERNANDO J.; MANTUANELI SCAREL-CAMINAGA, RAQUEL. Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases. PLoS One, v. 15, n. 10, p. 22-pg., 2020-10-02. (16/25418-6, 09/16233-9, 10/10882-2, 17/21174-8, 14/16148-0, 07/08362-8)

DIERCKX, LUCILE; VERONEZE, ROSANA; NIJSSEN, SIEGFRIED. RL-Net: Interpretable Rule Learning with Neural Networks. ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2023, PT I, v. 13935, p. 13-pg., 2023-01-01. (20/00123-9, 17/21174-8)

DIERCKX, LUCILE; VERONEZE, ROSANA; NIJSSEN, SIEGFRIED. RL-Net: Interpretable Rule Learning with Neural Networks. NEURAL-SYMBOLIC LEARNING AND REASONING 2023, NESY 2023, v. N/A, p. 2-pg., 2023-01-01. (20/00123-9, 17/21174-8)

VERONEZE, ROSANA; VAN OUYTSEL, CHARLES-HENRY BERTRAND; DAM, KHANH HUU THE; LEGAY, AXEL. Feature selection for packer classification based on association rule mining. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v. 137, p. 23-pg., 2024-08-16. (20/00123-9, 17/21174-8)