By Topic

Intelligent systems in biology: why the excitement?

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Lathrop, R. ; Dept. of Inf. & Comput. Sci., California Univ., Irvine, CA, USA

Biology has rapidly become a data-rich, information-hungry science because of recent massive data generation technologies. Our biological colleagues are designing more clever and informative experiments because of recent advances in molecular science. These experiments and data hold the key to the deepest secrets of biology and medicine, but we cannot fully analyze this data due to the wealth and complexity of the information available. The result is a great need for intelligent systems in biology. There are many opportunities for intelligent systems to help produce knowledge in biology and medicine. Intelligent systems probably helped design the last drug your doctor prescribed, and they were probably involved in some aspect of the last medical care you received. Intelligent computational analysis of the human genome will drive medicine for at least the next half-century. Intelligent systems are working on gene expression data to help understand genetic regulation and ultimately the regulated control of all life processes including cancer, regeneration, and aging. Knowledge bases of metabolic pathways and other biological networks make inferences in systems biology that, for example, let a pharmaceutical program target a pathogen pathway that does not exist in humans, resulting in fewer side effects to patients. Modern intelligent analysis of biological sequences produces the most accurate picture of evolution ever achieved. Knowledge-based empirical approaches currently are the most successful method known for general protein structure prediction. Intelligent literature-access systems exploit a knowledge flow exceeding half a million biomedical articles per year. Machine learning systems exploit heterogenous online databases whose exponential growth mimics Moore's law.

Published in:

Intelligent Systems, IEEE  (Volume:16 ,  Issue: 6 )