GO has been diagnostics and drug discovery. In this paper, we further extensively used to compute the similarity between genes our previous study on gene-disease relationship (details in section 3) [19, 20]. In this work, we use the specifically with the multifunctional genes. We investigate functional annotations of a gene from the Gene Ontology the multifunctional gene-disease relationship based on the Annotation (GOA) databases to compute the shortest published molecular function annotations of genes from distance (path length) between the Molecular Function the Gene Ontology which is the most comprehensive (mf) GO terms annotating the gene.
The genome of any given individual is unique and so must be the treatment for a disease. One is not oblivious to the fact that the conventional treatment strategy using "one cure fits all" might not work for multifaceted and devastating conditions such as cancer or neurological disorders. The treatment must be tailored to suffice the differences in an individual's genetic constitution, environment, and lifestyle.
AI has embraced medical applications from its inception, and some of the earliest work in successful application of AI technology occurred in medical contexts. Medicine in the twenty-first century will be very different than medicine in the late twentieth century. Fortunately, the technical challenges to AI that emerge are similar, and the prospects for success are high. I have therefore taken the liberty of dividing the last 30 years of medical AI research into three eras: the era of diagnosis, the era of managed care, and the era of molecular medicine. A description of these eras allows me to review for you some of the early and current work in AIM and then tell you about some of the exciting opportunities now emerging. Why is AI in medicine even worth considering? In the late 1950s, medicine was already drawing the attention of computer scientists principally because it contains so many stereotypical reasoning tasks. At the same time, these tasks are fairly structured and so are ...
AI has embraced medical applications from its inception, and some of the earliest work in successful application of AI technology occurred in medical contexts. Medicine in the twenty-first century will be very different than medicine in the late twentieth century. Fortunately, the technical challenges to AI that emerge are similar, and the prospects for success are high.
The following interview is one of many included in the report. As part of our ongoing series of interviews surveying the frontiers of machine intelligence, I recently interviewed Brendan Frey. Frey is a co-founder of Deep Genomics, a professor at the University of Toronto and a co-founder of its Machine Learning Group, a senior fellow of the Neural Computation program at the Canadian Institute for Advanced Research, and a fellow of the Royal Society of Canada. His work focuses on using machine learning to understand the genome and to realize new possibilities in genomic medicine. Brendan Frey: I completed my Ph.D. with Geoff Hinton in 1997.