By Stephen Wong, Stephen Wong; Chung-Sheng Li
The technology, or even paintings, of information mining has got loads of realize within the company global as how one can do really expert advertising to prior buyers. during this booklet editors Wong (Harvard scientific university) and Li (IBM) have accrued a chain of chapters at the software of knowledge mining thoughts within the box of lifestyles sciences. the actual functions displaying promise comprise: bio-surveillance illness outbreak detection excessive throughput bioimaging drug screening preidtive toxicology biosensors and extra. it is a fresh box supplying a few super possibilities to supply for locating breakthroughs within the identity of areas of difficulty in the common information being accrued for different purposes. This e-book is the 1st to debate this leading edge expertise, nonetheless within the formative phases, yet quickly stepping into the most circulate.
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From this resulting image, features can be extracted. Figure 2 shows nucleus appearances in different phases. 21 22 X. Zhou et al. Inter Phase Metaphase Prophase Anaphase Figure 2: Nucleus appearances in different phases. Once the individual nuclei have been detected by the segmentation procedure, it is then necessary to identify its cell phase. Cell phase are identified by comparing nuclei shape and intensity. To identify the shape and intensity differences between different cell phases, a set of twelve features were extracted based on the experience of expert cell biologists.
Reis, K. D. Mandl. (2004) "Use of Emergency Department Chief Complaint and Diagnostic Codes for Identifying Repiratory Illness in a Pediatric Population," Pediatric Emergency Care, vol 20, no. 6, pp. 355-360, June, 2004. Buckeridge, D. , J. K. Graham, M. J. O'Connor, M. K. Choy, S. W. Tu, M. A. Musen. (2002) Knowledge-based bioterrorism surveillance, Proceedings AMIA Symposium 2002: 76-80. Buckeridge, D. , H. Burkom, M. Campbell, W. R. Hoganb, A. W. Moore.  Algorithms for Rapid Outbreak Detection: A Research Synthesis.
We found that the traditional two-layer neural network did not work (the recognition accuracy only has about 50%). One reason may be that the data sets have a highly nonlinear property because we can see the K nearest neighbor classifier works very well. The generalized regression neural network with Gaussian kernel , on the other hand, performs better than the two-layer neural networks in cell phase identification. So here we compare the Gaussian, mixture model, K nearest neighbor classifier, regression neural networks, and decision tree.
LIFE SCIENCE DATA MINING by Stephen Wong, Stephen Wong; Chung-Sheng Li