Integrative omics for health and disease

By Konrad J. Karczewski, Michael Snyder. Nature reviews genetics, 2018

938 citations recorded by the source corpus.

Advances in omics technologies — such as genomics, transcriptomics, proteomics and metabolomics — have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bf4822220de014f4257c4e22891c60e6d79f4e7f

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Citations in the corpus

Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, background): “[65] Konrad J Karczewski and Michael P Snyder.”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “Precision medicine is progressing but with many challenges lying ahead (255), which require addition of useful analytic tools, technologies, databases and approaches (4,6) to efficiently augment networking and interoperability of clinical, laboratory and public health systems, as well as address eth”
Artificial intelligence and machine learning in precision and genomic medicine (2021, background): “While the intricacy of disorders at the interpersonal level has created it challenging to use healthcare data in therapeutic decision making, technological advancements have helped overcome some of the barriers [7].”
Multimodal biomedical AI (2022, background): “Julián N. Acosta 1 , Guido J. Falcone 1 , Pranav Rajpurkar 2,4 ✉ and Eric J. Topol 3,4 ✉ The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set…”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “As reviewed previously ( e.g. , [8, 9, 10]), a data integration approach can achieve a more thorough and informative analysis of biomedical data than an approach that uses only a single data type.”