2018/05/08 by Xijun Liang, Zhonghang Xia, Liang, Xijun +10
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · Mathematics · #Advanced Proteomics Techniques and Applications #Artificial intelligence #Benchmark (surveying) #Chemistry #Computer science #FOS: Computer and information sciences #False discovery rate #False positive paradox #Identification (biology) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Bioinformatics #Machine learning #cs.LG #stat.ML #vaccines and immunoinformatics approaches
paper · pdf · doi:10.48550/arxiv.1805.03006
published in arXiv (Cornell University) (Cornell University) · 16 pages, 3 figures
arxiv created 2018/05/08 · openalex publication_date 2018/05/08 · arxiv updated 2018/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Motivation: Post-database searching is a key procedure in peptide dentification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of peptide identification, the challenge remains on large-scale datasets and datasets with an extremely large proportion of false positives (hard datasets). A more efficient learning strategy is required for improving the performance of peptide identification on challenging datasets. Results: In this work, we present an online learning method to conquer the challenges remained for exiting peptide identification algorithms. We propose a cost-sensitive learning model by using different loss functions for decoy and target PSMs respectively. A larger penalty for wrongly selecting decoy PSMs than that for target PSMs, and thus the new model can reduce its false discovery rate on hard datasets. Also, we design an online learning algorithm, OLCS-Ranker, to solve the proposed learning model. Rather than taking all training data samples all at once, OLCS-Ranker iteratively feeds in only one training sample into the learning model at each round. As a result, the memory requirement is significantly reduced for large-scale problems. Experimental studies show that OLCS-Ranker outperforms benchmark methods, such as CRanker and Batch-CS-Ranker, in terms of accuracy and stability. Furthermore, OLCS-Ranker is 15--85 times faster than CRanker method on large datasets. Availability and implementation: OLCS-Ranker software is available at no charge for non-commercial use at https://github.com/Isaac-QiXing/CRanker.