Abstract
Biological research is becoming increasingly database driven, motivated, in part, by the advent of large-scale functional genomics and proteomics experiments such as those comprehensively measuring gene expression. Consequently, a challenge in bioinformatics is integrating databases to connect this disparate information as well as performing large-scale studies to collectively analyze many different data sets. These composite data sets are conducive to extensive computational analysis and present new opportunities for data mining. Both supervised and unsupervised approaches can often be used to analyze the same kinds of data, depending on the desired result and the range of features available. Large-scale experiments, such as those performed with microarrays, yield large homogenous data sets that are well suited for computational analysis.
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Dash, A., Swarnkar, T., Nayak, M. (2011). Advanced Bioinformatics Approach in Machine Learning for Analyzing Genome Wide Expression Profiles and Proteomic Data Sets. In: Das, V.V., Stephen, J., Chaba, Y. (eds) Computer Networks and Information Technologies. CNC 2011. Communications in Computer and Information Science, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19542-6_54
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DOI: https://doi.org/10.1007/978-3-642-19542-6_54
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