Introducing a Stable Bootstrap Validation Framework for Reliable Genomic Signature Extraction

Abstract

The application of machine learning methods for the identification of candidate genes responsible for phenotypes of interest, such as cancer, is a major challenge in the field of bioinformatics. These lists of genes are often called genomic signatures and their linkage to phenotype associations may form a significant step in discovering the causation between genotypes and phenotypes. Traditional methods that produce genomic signatures from DNA Microarray data tend to extract significantly different lists under relatively small variations of the training data. That instability hinders the validity of research findings and raises skepticism about the reliability of such methods. In this study, a complete framework for the extraction of stable and reliable lists of candidate genes is presented. The proposed methodology enforces stability of results at the validation step and as a result, it is independent of the feature selection and classification methods used. Furthermore, two different statistical tests are performed in order to assess the statistical significance of the observed results. Moreover, the consistency of the signatures extracted by independent executions of the proposed method is also evaluated. The results of this study highlight the importance of stability issues in genomic signatures, beyond their prediction capabilities.

Identification and validation of an individualized autophagy-clinical prognostic index in gastric cancer patients

“…More importantly, in two independent geo gastric cancer datasets within the STAD, the prognostic power of the 4-gene signature was verified. Gene signature is often applied to forcast the prognosis of a variety of tumors in the past few years [22], which is even better than TNM staging and histopathological diagnosis in some extent [23]. Gene signatures based on ATGs have been reported in a variety of cancers, such as serous ovarian cancer, breast cancer, colon cancer and glioma [24][25][26][27].…”

Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

“…More importantly, in two independent geo gastric cancer datasets within the STAD, the prognostic power of the 4-gene signature was verified. Gene signature is often applied to forcast the prognosis of a variety of tumors in the past few years [22], which is even better than TNM staging and histopathological diagnosis in some extent [23]. Gene signatures based on ATGs have been reported in a variety of cancers, such as serous ovarian cancer, breast cancer, colon cancer and glioma [24][25][26][27].…”

Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

“…More importantly, in two independent geo gastric cancer datasets within the STAD, the prognostic power of the 4-gene signature was veri ed. Gene signature is often applied to forcast the prognosis of a variety of tumors in the past few years [22], which is even better than TNM staging and histopathological diagnosis in some extent [23]. Gene signatures based on ATGs have been reported in a variety of cancers, such as serous ovarian cancer, breast cancer, colon cancer and glioma [24][25][26][27].…”