Question

1. How can the significance of Bioinformatics and Biomedical be overlooked?
2. Pick two different features of Bioinformatics and Biomedical informatics. Compare and contrast the similarities and differences.
3. What challenges do health professionals face when trying to achieve significant results in genomics? What are some proposed solutions to this challenge?.

Answer 1

1.Bioinformatics is the application of computer technology to get the information that's stored in certain types of biological data.

Current biological and medical labs use methods that produce extremely large data sets, which cannot be analyzed by hand - for instance sequencing human genomes. Thus modern biological and medical research and development cannot be done without bioinformatics.

Future applications in biology, chemistry, pharmaceuticals, medicine, and agriculture.

In addition, bioinformatics plays an important role in biomedical research. Research work in the area of genetic diseases and medical genomics is rapidly increasing and the future of personalized medicine depends on bioinformatics approaches.

2.Bioinformatics and biomedical informatics both deal in biological research technology with the aim of advancing scientific knowledge, but each field has distinctive desired outcomes for their end-result applications. When faced with a choice of entering either field, it is important to become familiar with the relevant contributions of each field to know how they are unique.

Bioinformatics researchers specialize in the application of computer technology to manage, manipulate, and interpret large amounts of biological data. An emerging multidisciplinary field which has experienced accelerated growth due to the publication of the Human Genome Project, bioinformatics aims to analyze genetic data to further gene-based research and to discover medical cures. Researchers in bioinformatics use computational biology to create three-dimensional models to test the efficacy of new drugs.

Due to its multidisciplinary nature, bioinformatics attracts a wide range of scientific research professionals who hold master’s and doctoral degrees in genetics, molecular biology, neuroscience, epidemiology, and agriculture. Other professionals in this sector have advanced degrees in mathematics, computer science, and programming.

Many universities offer graduate certificates in bioinformatics; the International Society for Computational Biology has a list of bioinformatics related degree and certificate programs in the United States. Graduate-level certificate programs in bioinformatics are also available through massive open online courses (MOOCs) such as Coursera and EdX.

Biomedical informatics professionals, on the other hand, use information extracted from bioinformatics to solve problems, reduce medical errors, lower healthcare costs, and make healthcare decisions using an individual patient’s biological data. Practitioners in biomedical informatics focus on identifying trends in the data discovered through bioinformatics to analyze the health conditions of patients and the efficiency of healthcare processes.

Coming from a variety of backgrounds, biomedical informatics professionals have master’s or doctoral degrees and bring a diverse range of expertise to their research teams. Many are experts in epidemiology or licensed doctors or nurses with clinical experience. Others have professional experience in health economics, behavioral research psychology, and medical anthropology.

Clinical research teams typically have biostatisticians and computer programmers to calculate and represent data for medical professionals and patients. The American Medical Informatics Association (AMIA) has a list of featured biomedical informatics programs degree and certificate programs through universities in the United States. There are more than 150 health informatics courses available from leading university health science programs on Coursera and EdX.

3.genome annotation produces a considerable number of errors and some outright ridiculous “identifications” . These errors are highly visible, even when the error rate is quite low: because of the large numbers of genes in most genomes, the errors are also rather numerous. Some of the problems and challenges faced by genome annotation are an issue of quantity turning into quality: an analysis that can be easily and reliably done by a qualified researcher for one or ten protein sequences becomes difficult and error-prone for the same scientist and much more so for an automated tool when the task is scaled up to 10,000 sequences