2003, 2003(Special): 898-904. doi: 10.3934/proc.2003.2003.898

Learning theory applied to Sigmoid network classification of protein biological function using primary protein structure


College of Information Technology The University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, United States

Received  September 2002 Revised  March 2003 Published  April 2003

Recently, Valiant’s Probably Approximately Correct (PAC) learning theory has been extended to learning m-dependent data. With this extension, training data set size for sigmoid neural networks have been bounded without underlying assumptions for the distribution of the training data. These extensions allow learning theory to be applied to training sets which are definitely not independent samples of a complete input space. In our work, we are developing length independent measures as training data for protein classification. This paper applies these learning theory methods to the problem of training a sigmoid neural network to recognize protein biological activity classes as a function of protein primary structure. Specifically, we explore the theoretical training set sizes for classifiers using the full amino acid sequence of the protein as the training data and using length independent measures as the training data. Results show bounds for training set sizes given protein size limits for the full sequence input compared to bounds for input that is sequence length independent.
Citation: D. Warren, K Najarian. Learning theory applied to Sigmoid network classification of protein biological function using primary protein structure. Conference Publications, 2003, 2003 (Special) : 898-904. doi: 10.3934/proc.2003.2003.898

Manisha Pujari, Rushed Kanawati. Link prediction in multiplex networks. Networks & Heterogeneous Media, 2015, 10 (1) : 17-35. doi: 10.3934/nhm.2015.10.17


Serap Ergün, Osman Palanci, Sirma Zeynep Alparslan Gök, Şule Nizamoğlu, Gerhard Wilhelm Weber. Sequencing grey games. Journal of Dynamics & Games, 2020, 7 (1) : 21-35. doi: 10.3934/jdg.2020002


David Hales, Stefano Arteconi. Motifs in evolving cooperative networks look like protein structure networks. Networks & Heterogeneous Media, 2008, 3 (2) : 239-249. doi: 10.3934/nhm.2008.3.239


Chen Li, Fajie Wei, Shenghan Zhou. Prediction method based on optimization theory and its application. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1213-1221. doi: 10.3934/dcdss.2015.8.1213


Pavol Bokes. Exact and WKB-approximate distributions in a gene expression model with feedback in burst frequency, burst size, and protein stability. Discrete & Continuous Dynamical Systems - B, 2021  doi: 10.3934/dcdsb.2021126


Leonor Cruzeiro. The VES hypothesis and protein misfolding. Discrete & Continuous Dynamical Systems - S, 2011, 4 (5) : 1033-1046. doi: 10.3934/dcdss.2011.4.1033


Shui-Nee Chow, Xiaojing Ye, Hongyuan Zha, Haomin Zhou. Influence prediction for continuous-time information propagation on networks. Networks & Heterogeneous Media, 2018, 13 (4) : 567-583. doi: 10.3934/nhm.2018026


H. N. Mhaskar, T. Poggio. Function approximation by deep networks. Communications on Pure & Applied Analysis, 2020, 19 (8) : 4085-4095. doi: 10.3934/cpaa.2020181


Xilin Fu, Zhang Chen. New discrete analogue of neural networks with nonlinear amplification function and its periodic dynamic analysis. Conference Publications, 2007, 2007 (Special) : 391-398. doi: 10.3934/proc.2007.2007.391


Lambertus A. Peletier. Modeling drug-protein dynamics. Discrete & Continuous Dynamical Systems - S, 2012, 5 (1) : 191-207. doi: 10.3934/dcdss.2012.5.191


Mirosław Lachowicz, Martin Parisot, Zuzanna Szymańska. Intracellular protein dynamics as a mathematical problem. Discrete & Continuous Dynamical Systems - B, 2016, 21 (8) : 2551-2566. doi: 10.3934/dcdsb.2016060


Laltu Sardar, Sushmita Ruj. The secure link prediction problem. Advances in Mathematics of Communications, 2019, 13 (4) : 733-757. doi: 10.3934/amc.2019043


Wenjun Xia, Jinzhi Lei. Formulation of the protein synthesis rate with sequence information. Mathematical Biosciences & Engineering, 2018, 15 (2) : 507-522. doi: 10.3934/mbe.2018023


Xiaoshuang Xing, Gaofei Sun, Yong Jin, Wenyi Tang, Xiuzhen Cheng. Relay selection based on social relationship prediction and information leakage reduction for mobile social networks. Mathematical Foundations of Computing, 2018, 1 (4) : 369-382. doi: 10.3934/mfc.2018018


Fok Ricky, Lasek Agnieszka, Li Jiye, An Aijun. Modeling daily guest count prediction. Big Data & Information Analytics, 2016, 1 (4) : 299-308. doi: 10.3934/bdia.2016012


Armin Eftekhari, Michael B. Wakin, Ping Li, Paul G. Constantine. Randomized learning of the second-moment matrix of a smooth function. Foundations of Data Science, 2019, 1 (3) : 329-387. doi: 10.3934/fods.2019015


Yuantian Xia, Juxiang Zhou, Tianwei Xu, Wei Gao. An improved deep convolutional neural network model with kernel loss function in image classification. Mathematical Foundations of Computing, 2020, 3 (1) : 51-64. doi: 10.3934/mfc.2020005


Gerasimos G. Rigatos, Efthymia G. Rigatou, Jean Daniel Djida. Change detection in the dynamics of an intracellular protein synthesis model using nonlinear Kalman filtering. Mathematical Biosciences & Engineering, 2015, 12 (5) : 1017-1035. doi: 10.3934/mbe.2015.12.1017


Sunmoo Yoon, Maria Patrao, Debbie Schauer, Jose Gutierrez. Prediction models for burden of caregivers applying data mining techniques. Big Data & Information Analytics, 2017  doi: 10.3934/bdia.2017014


Martin Frank, Benjamin Seibold. Optimal prediction for radiative transfer: A new perspective on moment closure. Kinetic & Related Models, 2011, 4 (3) : 717-733. doi: 10.3934/krm.2011.4.717

 Impact Factor: 


  • PDF downloads (22)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]