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- W1650390 abstract "Scientists, biologists and medical practitioners are always enthusiastic to know the human unconscious control of body activity. A simple question of: how the cells talk? can answer several mysteries of cognitive science and life control of species. As the nature manages and organize an organism’s perception, circulation, respiration, digestion, excretion and action, if we can ably simulate the language of cells, consequently establishing the associated biomolecular interaction networks, it can yield miraculous results procuring digital control on our body with a profound impact on biotechnological and pharmaceutical fields. Studying the properties of protein and gene interaction networks promises to yield prediction of cell languages which can shed new light on the evolution of species and also improve our understanding of the underlying biological phenomena. Since a large part of these complex biological networks is governed by probabilistic theory and undefined rules that comes from the incompleteness of detailed knowledge on systems biology, the modern machine learning algorithms using probability and heuristics are the major techniques for their simulation. Several distinct and complementary learning algorithms are being explored to infer networks in biology. In this paper, we highlight application of various machine learning techniques to study biological networks inferring organized complexity of the languages of cells and providing a context for readers new to the field." @default.
- W1650390 created "2016-06-24" @default.
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- W1650390 date "2012-05-22" @default.
- W1650390 modified "2023-09-24" @default.
- W1650390 title "Application of Machine Learning Techniques to Study Biological Networks" @default.
- W1650390 hasPublicationYear "2012" @default.
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