Advanced search
Start date
Betweenand
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A Multi-Learning Training Approach for Distinguishing Low and High Risk Cancer Patients

Full text
Author(s):
Povoa, Lucas Venezian [1, 2, 3, 4] ; Balan Calvi, Uriel Caire [2] ; Lorena, Ana Carolina [2] ; Costa Ribeiro, Carlos Henrique [1, 2] ; Da Silva, Israel Tojal [3]
Total Authors: 5
Affiliation:
[1] Aeronaut Inst Technol ITA, Bioengn Lab, BR-12228900 Sao Jose Dos Campos - Brazil
[2] Aeronaut Inst Technol ITA, Comp Sci Div, BR-12228900 Sao Jose Dos Campos - Brazil
[3] AC Camargo Canc Ctr ACCCC, Lab Bioinformat & Computat Biol, BR-01508010 Sao Paulo - Brazil
[4] Fed Inst Educ Sci & Technol Sao Paulo IFSP, Comp Sci Dept, BR-12322030 Jacarei - Brazil
Total Affiliations: 4
Document type: Journal article
Source: IEEE ACCESS; v. 9, p. 115453-115465, 2021.
Web of Science Citations: 0
Abstract

All cancers are caused by changes in the DNA within cells that occur over the course of an individual's lifetime. These mutations confer extensive genetic and phenotype variations within individuals, making the identification of appropriate treatments hard and costly. Moreover, cancer datasets are usually highly sparse due to the presence of few samples and many input features, making it difficult to design accurate predictors to classify patients into risk groups. Here, we report on the Multi Learning Training (MuLT) algorithm, which employs supervised, unsupervised, and self-supervised learning methods in order to take advantage of the interplay of clinical and molecular features for distinguishing low and high risk cancer patients. Our solution is evaluated using three independent and public cancer data sets considering three different performance aspects, through 5-fold cross-validation experiments. MuLT outranks other methods achieving AUCs between 0.65 and 0.77 and mean squared errors smaller than 0.24, while reducing classification complexity. These findings confirm the benefits of combining different learning algorithms and of coupling molecular and clinical data for supporting clinical decision making in Oncology. (AU)

FAPESP's process: 15/19324-6 - Retrotransposons and nucleic acid-editing enzymes: activation, somatic events and association with cancer
Grantee:Israel Tojal da Silva
Support type: Regular Research Grants