Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

Background: Genome-wide identification of specific oligonucleotides (oligos) is a computationallyintensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos. Results: We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Chun-Chi Liu, Chin-Chung Lin, Ker-Chau Li, Wen-Shyen E. Chen, Jiun-Ching Chen, Ming-Te Yang, Pan-Chyr Yang, Pei-Chun Chang, Jeremy J. W. Chen
• Keywords: BMCBI 2007, IAB Algorithm, Specific Oligos, Unique Marker Database