Biomarkers are specific molecules, patterns or indicators, that coincide with a certain state of health or disease. The presence of such biomarkers can aid diagnosis, estimate disease progression or treatment efficacy. It could encompass, for instance, presence of metabolites in the blood, a genetic mutation or a body function.
A network is called federated when different partners in the interconnected ecosystem agree upon a uniform strategy, in this context, for use, processing and analysis of data. The specific advantage of having such as federated network, is that it allows for full preservation of patient privacy and security.
Refers to high frequency/tendency in genomic alterations/changes (e.g. mutations or chromosomal aberrations). This leads to alterations in the DNA sequence and, often, to phenotypic changes, such as onset of disease. It is often a hallmark of cancer and leads to the need for specific diagnosis and treatment.
Genomics is one of the omics technologies that specifically focusses on the genome structure and evolution as well as on gene sequence and function. The genome is the complete ensemble of genetic information of a certain cell or organism. By studying the genome, information relevant for health and disease can be deduced.
When giving a computer enough relevant input data and concomitant output results, it will become able to process or analyze future information independently. It does so by recognizing patterns and structures in the data and linking it with the appropriate result. In the field of personalized medicine, machine learning can link biomarkers to disease progression, outcome and treatment potential.
Omics is a term denoting the comprehensive characterization of biological molecules. Depending on the prefix, a different type of biological molecules is investigated. Relevant examples are genomics, investigating the genes and genome; proteomics, analyzing proteins; and metabolomics, studying the metabolites of a cell. Multi-omics is the combination or integration of different omic layers together to obtain better understanding of biological processes or diseace mechanisms.
When diagnosis, care and therapy are all optimized for the particular situation of a patient, one can use the term personalized medicine. The opposite is true when a generalized care procedure is used for all patient, regardless of their characteristics. Examples of patient traits that can influence their most optimal care solution are age, sex, weight and biomarkers in general.
Real-world data is data (such as patient characteristics, omics, etc.) coming from patients in a real clinical setting. This can be opposed to data obtained from conventional clinical trials, which result from a select group of patients (non-representative of the population) in a somewhat artificial setting.
Real-world evidence is clinical proof resulting from analysis of real-world data. For instance, when real-world data indicates that elderly patients receiving a certain type of drug do not recover as well as patients prescribed a different drug, there is real-world evidence that the first drug type is non-optimal for elderly patients. This is evidence that can guide healthcare professionals in establishing the correct therapy for a specific patient.
Transcriptomics is one of the omics technologies that specifically focusses on the structure, function, quantity and modifications of RNA molecules. The DNA inside the cell contains the genetic code that determines, to a large extent, how the organism functions. This DNA is transcribed into RNA, which is used by the cell to produce proteins, which in their turn bring about a certain phenomenon. By studying the transcriptome, information relevant for health and disease can be deduced.
EM-103424 | DATE OF PREPARATION: JUNE 2022