Determining Multifunctional Genes and Diseases in Human Using Gene Ontology Artificial Intelligence

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.

Can Systems Biology Aid In Personalised Medicine? – Science Trends


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.

Massive whole-genome study finds six types of liver cancer

The Japan Times

To analyze the vast genomic data -- totaling more than 300 terabytes -- the scientists used Shirokane, a supercomputer used specifically for life science research at the University of Tokyo. The study found that liver cancer is caused by mutations or abnormalities in nearly 40 genes, including more than 10 that had never before been linked to liver cancer.

Biologically-Validated A.I. Yields Breakthrough in Cardiovascular Disease


Researchers at U.S.-based WuXi NextCODE and the Yale School of Medicine published two new studies in the Journal of Experimental Medicine and Nature Metabolism on novel, artificial intelligence (A.I.) approaches that breathe new life into big data for complex diseases. A deep dive into these publications demonstrates breakthrough, biologically-validated A.I. approaches with the potential to understand virtually any disease in much greater detail using cost-effective designs of therapeutics. With precision medicine poised to transform patient care, the big data revolution in healthcare and drug development has taken center stage. Yet, the promise of precision medicine hinges on the ability to reduce the complex interconnections of large, multi-omic data sets into useful biological information. One of the long-term promises of precision medicine is to understand underlying disease mechanisms to ultimately improve our ability to diagnose, treat, and prevent a diverse array of conditions.

Deep learning meets genome biology


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.