Inference on chains of disease progression based on disease networks

Exploring novel disease-disease associations based on multi-view fusion network

Established taxonomy system based on disease symptom and tissue characteristics have provided an important basis for physicians to correctly identify diseases and treat them successfully. However, these classifications tend to be based on phenotypic observations, lacking a molecular biological foundation. Therefore, there is an urgent to integrate multi-dimensional molecular biological information or multi-omics data to redefine disease classification in order to provide a powerful perspective for understanding the molecular structure of diseases. Therefore, we offer a flexible disease classification that integrates the biological process, gene expression, and symptom phenotype of diseases, and propose a disease-disease association network based on multi-view fusion. We applied the fusion approach to 223 diseases and divided them into 24 disease clusters. The contribution of internal and external edges of disease clusters were analyzed. The results of the fusion model were compared with Medical Subject Headings, a traditional and commonly used disease taxonomy. Then, experimental results of model performance comparison show that our approach performs better than other integration methods. As it was observed, the obtained clusters provided more interesting and novel disease-disease associations. This multi-view human disease association network describes relationships between diseases based on multiple molecular levels, thus breaking through the limitation of the disease classification system based on tissues and organs. This approach which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies, extends the existing disease taxonomy.

Availability of data and materials

The preprocessed dataset and source code supporting the conclusions of this article are available at GitHub repository https://github.com/yangxiaoxi89/mvHDN.

Network analysis of cognitive deficit in patients with schizophrenia spectrum disorders

Background

Cognitive impairments are found in 80% of patients with schizophrenia. The severity of these impairments significantly affects the recovery of patients in terms of social functioning.

Network analysis is the most suitable approach for studying complex relationships among cognitive functions.

Aim

To build a network model of neurocognitive functions for identifying both the severity of impairments in individual functions and the vertices central to the whole model.

Methods

The study included 115 patients with schizophrenia and schizophrenia spectrum disorders and a comparison group, comprising 99 healthy subjects. The severity of clinical symptoms was assessed using the PANSS, CDSS and YMRS, and the SAS and BARS for extrapyramidal symptoms and akathisia. Subjects from the comparison group completed screening questionnaires QIDS-SR and PQ-16. Neurocognitive functions were assessed using the BACS.

Results

The patients performed worse than the healthy subjects on all tests. In the cognitive network models of healthy subjects, fewer connections were revealed and the central place was occupied by working memory, the functioning of which depends upon everyday functioning in the community. In the cognitive models of patients there was a greater connectedness of neurocognitive functions. Furthermore, the central place of the networks in patients is occupied by the processing speed, evaluated primarily using the Symbol Coding test, which reflects the dependence of patient activity on lower-order functions.

Conclusion

The processing speed deficit is key to schizophrenia and it may be considered a potential endophenotype of the disease.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.