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Predictive genomics is at the intersection of multiple disciplines: predictive medicine, personal genomics and translational bioinformatics.Specifically, predictive genomics deals with the future phenotypic outcomes via prediction in areas such as complex multifactorial diseases in humans. [1]
The goal of predictive medicine is to predict the probability of future disease so that health care professionals and the patient themselves can be proactive in instituting lifestyle modifications and increased physician surveillance, such as bi-annual full body skin exams by a dermatologist or internist if their patient is found to have an increased risk of melanoma, an EKG and cardiology ...
Note that these results use purely genetic information as input; including additional information such as age and sex often greatly improves the predictions. The coronary disease predictor and the hypothyroidism predictor above achieve AUCs of ~ 0.80 and ~0.78, respectively, when also including age and sex. [4] [11]
Scientists have long known that aging varies widely among individuals, influenced by genetics and lifestyle choices. Now, a new tool called CheekAge offers a simple, noninvasive way to predict ...
Clinicogenomics, also referred to as clinical genomics, is the study of clinical outcomes with genomic data. Genomic factors have a causal effect on clinical data. Clinicogenomics uses the entire genome of a patient in order to diagnose diseases or adjust medications exclusively for that patient.
Ab Initio gene prediction is an intrinsic method based on gene content and signal detection. Because of the inherent expense and difficulty in obtaining extrinsic evidence for many genes, it is also necessary to resort to ab initio gene finding, in which the genomic DNA sequence alone is systematically searched for certain tell-tale signs of protein-coding genes.
The UCSC Genome Browser presents a diverse collection of annotation datasets (known as "tracks" and presented graphically), including mRNA alignments, mappings of DNA repeat elements, gene predictions, gene-expression data, disease-association data (representing the relationships of genes to diseases), and mappings of commercially available ...
Matchmaker Exchange: Matchmaker Exchange is a federated network of databases whose goal is to find genetic causes of rare diseases by matching similar phenotypic and genotypic profiles. BRCA Challenge: The BRCA Challenge aims to advance understanding of the genetic basis of breast and other cancers using data from around the world.