BackgroundIdentification of protein complexes and functional modules from protein-protein interaction (PPI) networks is crucial to understanding the principles of cellular organization and predicting protein functions. In the past few years, many computational methods have been proposed. However, most of them considered the PPI networks as static graphs and overlooked the dynamics inherent within these networks. Moreover, few of them can distinguish between protein complexes and functional modules.ResultsIn this paper, a new framework is proposed to distinguish between protein complexes and functional modules by integrating gene expression data into protein-protein interaction (PPI) data. A series of time-sequenced subnetworks (TSNs) is constructed according to the time that the interactions were activated. The algorithm TSN-PCD was then developed to identify protein complexes from these TSNs. As protein complexes are significantly related to functional modules, a new algorithm DFM-CIN is proposed to discover functional modules based on the identified complexes. The experimental results show that the combination of temporal gene expression data with PPI data contributes to identifying protein complexes more precisely. A quantitative comparison based on f-measure reveals that our algorithm TSN-PCD outperforms the other previous protein complex discovery algorithms. Furthermore, we evaluate the identified functional modules by using “Biological Process” annotated in GO (Gene Ontology). The validation shows that the identified functional modules are statistically significant in terms of “Biological Process”. More importantly, the relationship between protein complexes and functional modules are studied.ConclusionsThe proposed framework based on the integration of PPI data and gene expression data makes it possible to identify protein complexes and functional modules more effectively. Moveover, the proposed new framework and algorithms can distinguish between protein complexes and functional modules. Our findings suggest that functional modules are closely related to protein complexes and a functional module may consist of one or multiple protein complexes. The program is available at http://netlab.csu.edu.cn/bioinfomatics/limin/DFM-CIN/index.html.

Clustering of protein-protein interaction networks is one of the most prevalent methods for identifying protein complexes, detecting functional modules and predicting protein functions. In the past few years, many clustering methods have been proposed. However, it is still a challenging task to evaluate how well the protein clusters are identified. Even for two of the most popular measurements, F-measure and p-value, bias exists when evaluating the identified clusters. In this paper, we propose two new types of measurements to evaluate clusters more finely and distinctly. One is hF-measure(Tf) , a topology-free measurement and another is hF-measure(Tb) , a topology-based measurement. Unlike F-measure, the new measurements of hF-measure(Tf) and hF-measure(Tb) can discriminate between different types of errors. Both artificial test data and practical test data were used to evaluate the effectiveness of hF-measure(Tf) and hF-measure(Tb) . For the artificial test data, artificial errors were generated by replacing some cluster members with functionally similar or non-similar members. The practical test data was produced by seven clustering algorithms Markov Clustering, Molecular Complex Detection, HC-PIN, SPICI, CPM, Core-Attachment and RRW. The experimental results on artificial and practical test data both show that hF-measure(Tf) and hF-measure(Tb) evaluate clusters more accurately compared to F-measure. Especially, hF-measure(Tb) can capture the topology changes in clusters, which can also be used to the analysis of dynamic network.

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

Purpose: Drug-induced liver injury (DILI) is a common adverse reaction in the clinic; however, there are relatively few reports of DILI in critically ill newborns and children. Making use of the Pediatric Intensive Care database (PIC), this study identifies which drugs are related to DILI in neonates and children in China.Methods: Using the PIC, we screened for patients whose liver was suspected of being injured by drugs during hospitalization. The medicine they used was then assessed by the Roussel Uclaf Causality Assessment Method (RUCAM). At the same time, we also collated drug combinations that may affect CYP (Cytochrome P) enzyme metabolism, which may cause DILI.Results: A total of 13,449 patients were assessed, of whom 77 newborns and 261 children were finally included. The main type of liver injury in neonates was mixed (83.1%), while the hepatic injury types of children were mostly distributed between hepatocellular (59.4%) and cholestatic (28.4%). In terms of the RUCAM assessment, the drugs that were most considered to cause or be associated with hepatic injury in newborns were medium and long chain fat emulsions (17%), sodium glycerophosphate (12%), and meropenem (9%); while omeprazole (11%), methylprednisolone sodium succinate (10%), and meropenem (8%) were the primary culprits of DILI in children. Drug combinations frequently seen in neonates that may affect CYP enzyme metabolism are omeprazole + budesonide (16.9%), dexamethasone + midazolam (10.4%), and midazolam + sildenafil (10.4%). In children, the commonly used drug combinations are fentanyl + midazolam (20.7%), ibuprofen + furosemide (18.4%), and diazepam + omeprazole (15.3%).Conclusions: In this study, medium and long chain fat emulsions and sodium glycerophosphate have been strongly associated with DILI in newborns, while omeprazole and methylprednisolone sodium succinate play an important role in the DILI of children. Also, attention should be paid to the effect on CYP enzymes when using multiple drugs at the same time. In future DILI cases, it is advisable to use the latest RUCAM for prospective study design so that complete case data and high RUCAM scores can be collected.