Jeffrey Epstein VI Foundation
Supporting innovation in science and education.

Jeffrey Epstein Ideas Forum


The Jeffrey Epstein Ideas Forum addresses fundamental topics in science from medical research to artificial intelligence, neuroscience and metaphysics.


Computational Biology: pattern prediction in biology.

Computational biology is the use of quantitative tools, data-analytics, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems.  Computational biology has been very effective when collecting large data sets and has helped sequence the human genome, create accurate models of the human brain, and assist in modeling biological systems.

Computational biomodeling, which builds computer models of biological systems, often uses visual simulations to assess the complexity of biological systems.  Biomodeling specifically uses specialized algorithms and visualization software, which help with the prediction of how bio systems will react under different environments.
Subsets of computational biology includes, computational genomics, neuroscience and pharmacology. Cancer computational biology aims to determine the future mutations in cancer through an algorithmic approach to analyzing data. Research in this field has led to the use of high-throughput measurement. High throughput measurement allows for the gathering of millions of data points using robotics and other sensing devices. This data is collected from DNA, RNA, and other biological structures. Areas of focus includes determining the characteristics of tumors, analyzing molecules that are deterministic in causing cancer, and understanding the how the human genome relates to the causation of tumors and cancer.
What all of these sub fields have in common is isolating key patterns in our biology. Pattern determination, the exceptions and derivatives, all stem from linear thinking, a type of analysis that relies on cause and effect, the only form of analysis that we know, an analysis that has helped humans survive and thrive. It is the basis of artificial intelligence. But when we learn, if ever, to emerge from the world of sequences and sensitize ourselves to other forms of occurrences, artificial intelligence and computational biology will more closely mirror the undocumented quivering of nature.



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