There are hundreds of methods for analysis of data obtained in mRNA-sequencing. The most of them are focused on small number of genes. In this study, we propose an approach that reduces the analysis of several thousand genes to analysis of several clusters. The list of genes is enriched with information from open databases. Then, the descriptions are encoded as vectors using the pretrained language model (BERT) and some text processing approaches. The encoded gene function pass through the dimensionality reduction and clusterization. Aiming to find the most efficient pipeline, 180 cases of pipeline with different methods in the major pipeline steps were analyzed. The performance was evaluated with clusterization indexes and expert review of the results.

Radiologic screening of high-risk adults reduces lung-cancer-related mortality; however, a small minority of eligible individuals undergo such screening in the United States. The availability of blood-based tests could increase screening uptake. Here researchers introduce improvements to cancer personalized profiling by deep sequencing (CAPP-Seq), a method for the analysis of circulating tumour DNA (ctDNA), to better facilitate screening applications. They show that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic. They develop and prospectively validate a machine-learning method termed'lung cancer likelihood in plasma' (Lung-CLiP), which can robustly discriminate early-stage lung cancer patients from risk-matched controls.