Making it personal: translational bioinformatics is a research paper published in Journal of the American Medical Informatics Association (2013). On theSindex it has a DataRank of 3.6. It has been cited 51 times, with 50 citing works in its 1-hop citation network.
One of the most exciting research areas in Translational Bioinformatics1 ,2 is related to the redefinition of fundamental notions of what constitutes a ‘disease.’ Nosology, the systematic classification of diseases, dates back to Carl Linnaeus, with the Genera Morborum 3 Today, the improvement in our abilities to make molecular measurements related to health and disease has largely driven the revolution towards personalized medicine. For example, in diseases like non-small cell lung cancer or breast cancer, standard-of-care is now including sequencing of genes such as EGFR or quantitating panels of RNA such as those included in Oncotype DX, respectively, to drive therapeutic decisions for new subtypes of patients. While experts, including those at the National Research Council, are seeing the potential of scaling beyond these early case examples towards redefining our entire nosology,4 it is in the field of cancer where personalized or precision medicine has had best traction. It is no coincidence that many contributions to this special issue of JAMIA focus on cancer. Personalized medicine, also known as precision medicine, has often been equated with the use of molecular measurements to characterize disease. The special feature in this issue of JAMIA challenges this limited view. Personalized medicine starts even before a disease is manifested in an individual, many times at a point when the disease or condition is preventable. Researchers use data from different sources to develop preventive models. For example, smoking is still the strongest preventable risk factor for many cancers, most notably lung cancer, yet it is hard to extract this information from …
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.593
From this paper's citation signal
Citation Network Contribution
3.0
From 42 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 16% comes from its base citations and 84% from the citation network (42 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
Click a node to highlight its connections. Use scroll to zoom. Drag to pan.