神經(jīng)網(wǎng)絡(luò)在基因序列預(yù)測中的應(yīng)用研究

人類基因組計劃的開展隨之產(chǎn)生了巨量的基因組信息，區(qū)分DNA 序列上的外顯子和內(nèi)含子成為基因工程中對基因進行識別和鑒定關(guān)鍵環(huán)節(jié)之一[1]。如何建立良好的系統(tǒng)模型將基因組數(shù)據(jù)進行有效地存儲、分析和挖掘，仍是難題。本文著重研究將多層前饋神經(jīng)網(wǎng)絡(luò)應(yīng)用于基因序列的預(yù)測分析中，成功從基因序列上識別出剪接位點，進而區(qū)分內(nèi)含子和外顯子邊界。使用MATLAB 神經(jīng)網(wǎng)絡(luò)工具箱和圖形用戶界面開發(fā)技術(shù)，對UCI機器學習數(shù)據(jù)庫中的基因數(shù)據(jù)集采用二進制數(shù)字編碼，完成樣本選??；創(chuàng)建優(yōu)化算法的BP神經(jīng)網(wǎng)絡(luò)和GRNN 神經(jīng)網(wǎng)絡(luò)并加以訓(xùn)練、仿真和測試。
關(guān)鍵詞 神經(jīng)網(wǎng)絡(luò)；基因工程；內(nèi)含子；外顯子
Abstract With the development of HGP, lots of genome information is acquired. To divide
the exorns and introns in DNA sequence is one of the key points of genetic identification in
genetic engineering. How to make a system model to storage，analyze and digging the genome data is still very difficult. This paper concentrates on how to use multilayer feedforward NN to getting primate-junction from genome sequence, and then, get the boundaries between exorns and introns. By using neural network tool box of MATLAB and GUI technology, to encode the data set from UCI Machine Learning Database with binary digit code, to gain examples.The designed BPNN model and GRNN model are used to train, simulate and test the above examples, and then predict unknown gene classification data from the model.
Key word artificial neural networks; genetic engineering; exorns; introns