2015/06/24 by Kévin Vervier, Pierre Mahé, Vervier, Kévin +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bacterial Identification and Susceptibility Testing #FOS: Biological sciences #FOS: Computer and information sciences #Genomics and Phylogenetic Studies #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Metabolomics and Mass Spectrometry Studies #Quantitative Methods (q-bio.QM) #cs.LG #q-bio.QM #stat.ML
paper · pdf · doi:10.48550/arxiv.1506.07251
arxiv created 2015/06/24 · openalex publication_date 2015/06/24 · arxiv updated 2015/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Microbial identification is a central issue in microbiology, in particular in the fields of infectious diseases diagnosis and industrial quality control. The concept of species is tightly linked to the concept of biological and clinical classification where the proximity between species is generally measured in terms of evolutionary distances and/or clinical phenotypes. Surprisingly, the information provided by this well-known hierarchical structure is rarely used by machine learning-based automatic microbial identification systems. Structured machine learning methods were recently proposed for taking into account the structure embedded in a hierarchy and using it as additional a priori information, and could therefore allow to improve microbial identification systems. We test and compare several state-of-the-art machine learning methods for microbial identification on a new Matrix-Assisted Laser Desorption/Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) dataset. We include in the benchmark standard and structured methods, that leverage the knowledge of the underlying hierarchical structure in the learning process. Our results show that although some methods perform better than others, structured methods do not consistently perform better than their "flat" counterparts. We postulate that this is partly due to the fact that standard methods already reach a high level of accuracy in this context, and that they mainly confuse species close to each other in the tree, a case where using the known hierarchy is not helpful.