Research & Development
PNN Technologies is conducting research to investigate new and more powerful statistical science in neural computational and information sciences, creating a model that can mimic the brain’s learning and cognitive processes. PNN is continuing its research in bridging the gaps between many interdisciplinary areas: neural computation, statistical inference, fuzzy sets theory, machine learning, pattern recognition, information theory, and dynamic systems.
PNN Technologies is interested in applying PNN to the benefit of the medical field. In the past years PNN Technologies has acted as a consultant for several bioinformatics projects at the research institution, Academia Sinica in Taiwan.
Future PNN Technologies research will be conducted on implementing PNN into hardware analog VLSI chips. PNN Technologies is looking for future collaborators on these projects.
Bioinformatics – Microarray, proteomic, and gene networks analysis. Gene-disease association, early cancer detection tool.
Intelligent self-organized system – Machines capable of learning from data information and extracting patterns through queries as an expert system.
Publications
Lin RJ, Lin YC, Chen J, Kuo HH, Chen YY, Diccianni MB, Chang JCH, London W, Yu AL (2010). MicroRNA signature and expression of dicer and drosha can delineate clinical risk groups in neuroblastoma. Cancer Research, 2010
Chang, W. C., Hsu, P. I., Chen, Y.Y. , Hsiao, M., Lu, P.J., and Chen, Chung-Hsuan (2008). Biological Observation of peptide differences between cancer and control in gastric juice, Proteomics-Clinical Applications, Volume 2, Issue 1, 55-62
Li, K. Chang, D. J., Rouchka, E. and Chen, Y. Y. and Chen, J. J. (2007). Biological Sequence Mining Using Plausible Neural Network and its Application to Exon/intron Boundaries Prediction, 2007 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. 165-169
Li, K. Chang, D. J. and Chen, Y. Y. (2006). Hi-speed Bidirectional Function Approximation using Plausible Neural Networks International Joint Conference on Neural Networks 1085 – 1090
Li, K. Chang, D. J. (2006). Fuzzy Membership Function Elicitation using Plausible Neural Network.Proceedings of the 2006 International Conference on Artificial intelligence.141-147
Chen, Y. Y. and Chen, J. J. (2004). Neural Networks and Belief Logic, Proceedings of the Fourth International Conference on Hybrid Intelligent Systems (HIS04), IEEE. 460-461.
Chen, Y. Y. (2002). Plausible Neural Networks. Advance in Neural Networks World. Ed. Grmela, A. and Mastorakis, N. E. WSEAS Press, 180-185.
Chen, Y. Y. (2000). Fuzzy Analysis of Statistical Evidence. IEEE Trans. Fuzzy Systems, 8, 796-799.
Chen, Y. Y. (1995). Statistical Inference based on the Possibility and Belief measures. Trans. Amer. Math.Soc. 347 1855-1863.
Chen, Y. Y. (1993). Bernoulli trials: from a fuzzy measure point of view. J. Math. Anal. Appl. 175, 392-404.
Lectures
BCIG Seminar at the National Institutes of Health (NIH) (2007).
Chen, Y. Y. (2005). Plausible Neural Networks – An Intelligent Self-organized Network System.
Chen, Y. Y. (2005). Logic, Science and Complex Systems – a Synthesis View