Using machine learning tools for protein database biocuration assistance

Abstract

Biocuration in the omics sciences has become paramount, as research in these fields rapidly evolves towards increasingly data-dependent models. As a result, the management of web-accessible publicly-available databases becomes a central task in biological knowledge dissemination. One relevant challenge for biocurators is the unambiguous identification of biological entities. In this study, we illustrate the adequacy of machine learning methods as biocuration assistance tools using a publicly available protein database as an example. This database contains information on G Protein-Coupled Receptors (GPCRs), which are part of eukaryotic cell membranes and relevant in cell communication as well as major drug targets in pharmacology. These receptors are characterized according to subtype labels. Previous analysis of this database provided evidence that some of the receptor sequences could be affected by a case of label noise, as they appeared to be too consistently misclassified by machine learning methods. Here, we extend our analysis to recent and quite substantially modified new versions of the database and reveal their now extremely accurate labeling using several machine learning models and different transformations of the unaligned sequences. These findings support the adequacy of our proposed method to identify problematic labeling cases as a tool for database biocuration.

Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network

“…To address these issues, automated and augmented curation systems for extracting protein functional data from scientific literature are becoming increasingly desired. In particular, Machine Learning and Natural Language Processing techniques are beginning to be employed for biocuration efforts 1 , 2 for extracting and organising unstructured biological information into a structured form that is accessible to biologists. Central to these automated systems, is the process of unambiguously extracting semantic relationships between two or more biological entities in the literature 3 .…”

Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network

“…To address these issues, automated and augmented curation systems for extracting protein functional data from scientific literature is becoming increasingly desired. In particular, Machine Learning and Natural Language Processing techniques are beginning to be employed for large scale biocuration efforts 1,2 . Biocuration refers to the process of extracting and organising unstructured biological information into a structured form that is accessible to biologists.…”

Extraction of artefactual MRS patterns from a large database using non‐negative matrix factorization

“…2 One reason for this may be the lack of the type of biocuration standards that begin to be common in other life sciences fields such as genomics and, to a lesser extent, proteomics. 3 Further reasons include the fact that MRS data in this area are scarce and fragmented. Fragmentation is both geographical and institutional, as the effort of gathering multi-center and international data is hindered by different barriers.…”