The Human Genome Project was a collaborative effort aiming at mapping the DNA sequence of human cells, started in 1990. The project was initially set to run for 15 years, but already in 2000 a draft of the findings was published, and in 2003 the effort was considered completed. Nowadays, the sequencing is considered a key milestone in healthcare, but research on the Genome is still undergoing and far from being concluded. First and foremost because the Genome library is considered a combined mosaic of the donors’ genetic sequence, as each individual’s DNA is - of course - unique, and therefore many questions are still unanswered.
From the Humane Genome Projects, derive progress in two main medical areas, with the potential of disrupting even further the future of healthcare: personal genomics and pharmacogenomics.
Personal Genomics is the branch of genetics, which aims at sequencing and analyzing the DNA sequence of individuals. This is a core step in predictive medicine, by providing the ability to predict the probability of a disease on the basis of specific genetic markers. This, of course, is a game changer, because it shifts the focus of medicine from treating illnesses, to preventing diseases before they manifest. And the impact in healthcare could be drastic because it could prevent diabetes, hypertension and heart attacks, as well as, provide early insights on breast cancer and cystic fibrosis. The DNA Sequencing / Test industry is in rapid expansion: alongside DNA screening testing services like 23andme, a number of genealogy based services are also emerging, like Ancestry, Family Tree DNA and MyHeritage. Moreover, companies like Helix, use the test results to generate personal insights on weight loss, improved workouts and even personal fit with wines.
In addition to that, pharmacogenomics or personalized medicine is also made possible: personal genetics is analyzed to understand individual responses to a drug and its dosages. This personalization has a dual benefit: not only it makes treatments more efficient but also reduces the occurrence of side effects. So, for example, pharmacogenomics is utilized to reduce the severe side effects of HIV related treatments, where a combination of antiretroviral is used to tackle the virus. Moreover, it is used to screen for the efficacy of an immunosuppressant in the care of rheumatoid arthritis or of autoimmune diseases, where there is the risk of leaving the patient vulnerable to life-threatening infections. Pharmacogenomics is also used to identify the right dosage of anti-coagulant for people at risk of developing a blood clot or a pulmonary embolism. Among the players in this space, Geneticure, a research collaboration from Mayo Clinic, University of Arizona and the University of Minnesota, developed a test which allows predicting how patients will respond to hypertension drugs based on functional markers in their DNA. In the space of illnesses like mental, diabetes and cardiovascular diseases, Genomas collaborates with Hartford Healthcare Institutions to offer personalized care base on DNA profiling.
Furthermore, within the pharmacogenomics umbrella, one of the most promising disciplines is oncogenomics, which offers three main benefits: 1) improving the cancer diagnosis, by using early genetic markers; 2) predicting the clinical outcome of those cancers by classifying them according to those genetic markets; 3) improving treatment by using gene mutations as a target of drug therapy.
The future of healthcare will be shaped by this emerging DNA-based discipline which allows individuals to be tested, their DNA to be sequenced and insights to be generated on their treatments efficacy and even drug dosage. As one can expect big data and Artificial Intelligence are supporting – even driving – the development of personalized medicine: for example, Canadian scale-up Deep Genomics is using big data and machine learning to help researchers interpreting genetic variations. British start-up Deskgen is using AI to harnessing CRISP libraries, supporting future gene editing methodologies. And in the traditional academic world, machine learning is becoming a key tool in pharmacogenomics research.
Source: Machine Learning in Genomics https://www.techemergence.com/machine-learning-in-genomics-applications/