Аннотация:
We recently reviewed the current progress in the use of high-throughput molecular “omics” data for the quantitative analysis of molecular pathway activation. These quantitative metrics may be used in many ways, and we focused on their application as tumor biomarkers. Here, we provide an update of the most recent conceptual findings related to pathway analysis in tumor biology, which were not included in the previous review. The major novelties include a method enabling calculation of pathway-scale tumor mutation burden termed “Pathway Instability” and its application for scoring of anticancer target drugs. A new technique termed Shambhala emerged that enables accurate common harmonization of any number of gene expression profiles obtained using any number of experimental platforms. This may be helpful for merging various gene expression data sets and for comparing their pathway activation characteristics. Another recent bioinformatics method, termed FLOating-Window Projective Separator
Ключевые слова:
bioinformatics; cancer; machine learning; mutation profiling; signaling pathways
antineoplastic agent; algorithm; bioinformatics; cancer patient; classifier; controlled study; disease burden; drug targeting; gene activation; gene expression profiling; gene mutation; genetic database; high throughput sequencing; human; machine learning; malignant neoplasm; molecular biology; molecular dynamics; Note; personalized medicine; prescription; quantitative analysis; RNA sequence; scoring system; signal transduction; support vector machine; transcriptomics; treatment response; tumor growth
Buzdin A. A. Anton Aleksandrovich 1977-
Sorokin M. I. Maksim Igorevich 1989-
Poddubskaya E. V. Elena Vladimirovna 1965-
Borisov N. M. Nikolay Mikhaylovich 1973-
Буздин А. А. Антон Александрович 1977-
Сорокин М. И. Максим Игоревич 1989-
Поддубская Е. В. Елена Владимировна 1965-
Борисов Н. М. Николай Михайлович 1973-
High-Throughput Mutation Data Now Complement Transcriptomic Profiling: Advances in Molecular Pathway Activation Analysis Approach in Cancer Biology
Текст визуальный непосредственный
Cancer Informatics
Vol.18
2019
Комментарий
bioinformatics cancer machine learning mutation profiling signaling pathways
antineoplastic agent algorithm bioinformatics cancer patient classifier controlled study disease burden drug targeting gene activation gene expression profiling gene mutation genetic database high throughput sequencing human machine learning malignant neoplasm molecular biology molecular dynamics Note personalized medicine prescription quantitative analysis RNA sequence scoring system signal transduction support vector machine transcriptomics treatment response tumor growth
We recently reviewed the current progress in the use of high-throughput molecular “omics” data for the quantitative analysis of molecular pathway activation. These quantitative metrics may be used in many ways, and we focused on their application as tumor biomarkers. Here, we provide an update of the most recent conceptual findings related to pathway analysis in tumor biology, which were not included in the previous review. The major novelties include a method enabling calculation of pathway-scale tumor mutation burden termed “Pathway Instability” and its application for scoring of anticancer target drugs. A new technique termed Shambhala emerged that enables accurate common harmonization of any number of gene expression profiles obtained using any number of experimental platforms. This may be helpful for merging various gene expression data sets and for comparing their pathway activation characteristics. Another recent bioinformatics method, termed FLOating-Window Projective Separator