Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer.

ABSTRACT: Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of reliable models exploiting such data to retrieve clinically useful information. Here, we introduce an original consensus clustering approach, overcoming the intrinsic instability of common clustering methods based on molecular data. This approach is applied to the case of non-small cell lung cancer (NSCLC), integrating data of an ongoing clinical study (PROMOLE) with those made available by The Cancer Genome Atlas, to define a molecular-based stratification of the patients beyond, but still preserving, hist

SUBMITTER: Manganaro L 

PROVIDER: S-EPMC10182023 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature