Biology subjects

You, R.

Publications and source records attributed to You, R..

22 records · Page 2Linked to original sources

bioRxiv2020

BERTMeSH: Deep Contextual Representation Learning for Large-scale High-performance MeSH Indexing with Full Text

Motivation With the rapid increase of biomedical articles, large-scale automatic Medical Subject Headings (MeSH) indexing has become increasingly important. FullMeSH, the only method for large-scale MeSH indexing with full text, suffers from three major drawbacks: FullMeSH 1) uses Learning To Rank (LTR), which is time-consuming, 2) can capture some pre-defined sections only in full text, and 3) ignores the whole MEDLINE database.Results We propose a computationally lighter, full-text and deep learning based MeSH indexing method, BERTMeSH, which is flexible for section organization in full text. BERTMeSH has two technologies: 1) the state-of-the-art pre-trained deep contextual representation, BERT (Bidirectional Encoder Representations from Transformers), which makes BERTMeSH capture deep semantics of full text. 2) a transfer learning strategy for using both full text in PubMed Central (PMC) and title and abstract (only and no full text) in MEDLINE, to take advantages of both. In our experiments, BERTMeSH was pre-trained with 3 million MEDLINE citations and trained on approximately 1.5 million full text in PMC. BERTMeSH outperformed various cutting edge baselines. For example, for 20K test articles of PMC, BERTMeSH achieved a Micro F-measure of 69.2%, which was 6.3% higher than FullMeSH with the difference being statistically significant. Also prediction of 20K test articles needed 5 minutes by BERTMeSH, while it took more than 10 hours by FullMeSH, proving the computational efficiency of BERTMeSH.Contact zhusf{at}fudan.edu.cnCompeting Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2019

Fibroblast growth factor-2 prevents synaptic pathology in minimal hepatic encephalopathy via NRG1/ErbB4 signaling

BackgroundMinimal hepatic encephalopathy (MHE) is implicated in the impairment of memory function. Fibroblast growth factor-2 (FGF2) is involved in modulating synaptic and neuronal formation. MethodsThe aim of this study is to examined the impacts of FGF2 on MHE pathology. Our study addressed whether FGF2 could trigger neuregulin-1 (NRG1) release to ameliorate synaptic impairment in MHE rats and in primary cultured neurons. ResultsThe results showed the decreased FGF2 expression in MHE brains. After treatment with FGF2, secreted neuregulin-1 (NRG1) and ErbB4 were increased, and the interaction of the 2 proteins was enhanced. Additionally, treatment with FGF2 or NRG1 induced synaptic formation, with increase in the activity of synapse and the density of dendritic spine, through Sirt1. NRG1 signaling was prevented by administration of FGF2, which acts through the FGFR1 in MHE rats. Finally, intracerebroventricular injection with FGF2 or NRG1 mitigated the impairment of synaptogenesis. ConclusionThe data suggest that FGF2 may be a promising latent therapeutic reagent for MHE pathogenesis.

neuroscience↗

bioRxiv2018

NetGO: Improving Large-scale Protein Function Prediction with Massive Network Information

Automated function prediction (AFP) of proteins is of great significance in biology. In essence, AFP is a large-scale multi-label classification over pairs of proteins and GO terms. Existing AFP approaches, however, have their limitations on both sides of proteins and GO terms. Using various sequence information and the robust learning to rank (LTR) framework, we have developed GOLabeler, a state-of-the-art approach of CAFA3, which overcomes the limitation of the GO term side, such as imbalanced GO terms. Unfortunately, for the protein side issue, available abundant protein information, except for sequences, have not been effectively used for large-scale AFP in CAFA. We propose NetGO that is able to improve large-scale AFP with massive network information. The novelties of NetGO have threefold in using network information: 1) the powerful LTR framework of NetGO efficiently and effectively integrates both sequence and network information, which can easily make large-scale AFP; 2) NetGO can use whole and massive network information of all species (>2000) in STRING (other than only high confidence links and/or some specific species); and 3) NetGO can still use network information to annotate a protein by homology transfer even if it is not covered in STRING. Under numerous experimental settings, we examined the performance of NetGO, such as general performance comparison, species-specific prediction, and prediction on difficult proteins, by using training and test data separated by time-delayed settings of CAFA. Experimental results have clearly demonstrated that NetGO outperforms GOLabeler, DeepGO, and other compared baseline methods significantly. In addition, several interesting findings from our experiments on NetGO would be useful for future AFP research.

bioinformatics↗

bioRxiv2017

GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank

Motivation: Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only {inverted exclamation}1% of more than 70 million proteins in UniProtKB have experimental GO annotations, implying the strong necessity of automated function prediction (AFP) of proteins, where AFP is a hard multi-label classification problem due to one protein with a diverse number of GO terms. Most of these proteins have only sequences as input information, indicating the importance of sequence-based AFP (SAFP: sequences are the only input). Furthermore, homology-based SAFP tools are competitive in AFP competitions, while they do not necessarily work well for so-called difficult proteins, which have {inverted exclamation}60% sequence identity to proteins with annotations already. Thus, the vital and challenging problem now is to develop a method for SAFP, particularly for difficult proteins.\n\nMethods: The key of this method is to extract not only homology information but also diverse, deep-rooted information/evidence from sequence inputs and integrate them into a predictor in an efficient and also effective manner. We propose GOLabeler, which integrates five component classifiers, trained from different features, including GO term frequency, sequence alignment, amino acid trigram, domains and motifs, and biophysical properties, etc., in the framework of learning to rank (LTR), a new paradigm of machine learning, especially powerful for multi-label classification.\n\nResults: The empirical results obtained by examining GOLabeler extensively and thoroughly by using large-scale datasets revealed numerous favorable aspects of GOLabeler, including significant performance advantage over state-of-the-art AFP methods.\n\nContact: zhusf@fudan.edu.cn

bioinformatics↗