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The concept of biological computation proposes that living organisms perform computations, and that as such, abstract ideas of information and computation may be key to understanding biology.
General topics that appear in recent conferences include: (1) personal genomics and genomic infrastructure, (2) drug and gene research for adverse events, interactions and repurposing of drugs, (3) biomarkers and phenotype representation, (4) sequencing, science and systems medicine, (5) computational and analytical methodologies for TBI, and ...
[1] Image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics, it aids in sequencing and annotating genomes and their observed mutations. Bioinformatics includes text mining of biological literature and the development of biological and gene ontologies to organize and query ...
[1] While each field is distinct, there may be significant overlap at their interface, [1] so much so that to many, bioinformatics and computational biology are terms that are used interchangeably. The terms computational biology and evolutionary computation have a similar name, but are not to be confused. Unlike computational biology ...
It usually lasts three days in September, and traditionally includes some special sessions about the application of computational intelligence to specific aspects of biology (for example, the "Special session on machine learning in health informatics and biological systems" at CIBB 2018, [2]) and occasionally some tutorials.
It is a community-driven 501(c)(3) non-profit organization [1] that aims to establish a worldwide network that is open to anyone interested in bioinformatics irrespective of academic background and to provide bioinformatics training, mentorship and the opportunity to collaborate on exciting research projects. [2]
Modelling biological systems is a significant task of systems biology and mathematical biology. [a] Computational systems biology [b] [1] aims to develop and use efficient algorithms, data structures, visualization and communication tools with the goal of computer modelling of biological systems.
[2] [3] Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems such as protein structure prediction, this proved difficult. [4] Machine learning techniques such as deep learning can learn features of data sets rather than requiring the programmer to define them individually.