Simpati: patient classifier identifies signature pathways based on similarity networks for the disease prediction

ABSTRACTBACKGROUNDPathway-based patient classification is a supervised learning task which supports the decision-making process of human experts in biomedical applications providing signature pathways associated to a patient class characterized by a specific clinical outcome. The task can potentially include to simulate the human way of thinking in predicting patients by pathways, decipher hidden multivariate relationships between the characteristics of patient class and provide more information than a probability value. However, these classifiers are rarely integrated into a routine bioinformatics analysis of high-dimensional biological data because they require a nontrivial hyper-parameter tuning, are difficult to interpret and lack in providing new insights. There is the need of new classifiers which can provide novel perspectives about pathways, be easy to apply with different biological omics and produce new data enabling a further analysis of the patients.RESULTSWe propose Simpati, a pathway-based patient classifier which combines the concepts of network-based propagation, patient similarity network, cohesive subgroup detection and pathway enrichment. It exploits a propagation algorithm to classify both dense, sparse, and non-homogenous data. It handles patient’s features (e.g. genes, proteins, mutations) organizing them in pathways represented by patient similarity networks for being interpretable, handling missing data and preserving the patient privacy. A network represents patients as nodes and a novel similarity determines how much every pair act co-ordinately in a pathway. Simpati detects signature biological processes based on how much the topological properties of the related networks discriminate the patient classes. In this step, it includes a novel cohesive subgroup detection algorithm to handle patients not showing the same pathway activity as the other class members. An unknown patient is classified based on how much is similar with known ones. Simpati outperforms state-of-art classifiers on five cancer datasets, classifies well sparse data and provides a novel concept of enrichment which calls pathways as up or down involved with respect the overall patient’s biology.CONCLUSIONSimpati can serve as interpretable accurate pathway-based patient classifier to discover novel signature pathways driving a clinical class, to detect biomarkers and to get insights about how patients are similar based on their regulation of biological processes. The biomarker detection is made possible with the propagation score, likelihood of association between the patient’s feature and outcome, and with the deconvolution of the single feature’s contributions in the patient similarities. The pathway enrichment is enhanced with the integration of the Disgnet and the Human Protein Atlas databases. We provide an R implementation which enables to start Simpati with one function, a GUI interface for the navigation of the patient’s propagated profiles and a function which offers an ad-hoc visualization of patient similarity networks. The software is available at: https://github.com/LucaGiudice/Simpati

Structure in personal networks: Constructing and comparing typologies

A recurrent finding in personal network research is that individual and social outcomes are influenced not just by the kind of people one knows, but also by how those people are connected to each other. Personal network structure – the way in which one’s personal contacts know and interact with each other – reflects broader trends in social organization and personal communities, and shapes patterns of social capital, support, and isolation. This article proposes a method to identify typologies of structure in large collections of personal networks. The method is applied to six datasets collected in widely different circumstances and using various survey instruments. It is then compared with another recently introduced method to extract typologies of egocentric network structure. Findings show that personal network structure can be effectively summarized using just three measures of cohesive subgroup characteristics. Structural typologies can then be identified by applying standard cluster analysis techniques to the three variables. Both methods considered in the article capture significant variation in network structures, but they also show substantial levels of disagreement and cross-classification. I discuss similarities and differences between the methods, and potential applications of the proposed typologies to substantive research on personal communities, social support, and social capital.

Trend analysis of co-authorship network in Library Hi Tech

Purpose The purpose of this paper is to examine the evolution of collaboration among researchers in Library Hi Tech based on the co-authorship network analysis. Design/methodology/approach The Library Hi Tech publications were retrieved from Web of Science database between 2006 and 2017. Social network analysis based on co-authorship was analyzed by using BibExcel software and a visual knowledge map was generated by Pajek. Three important social capital indicators: degree centrality, closeness centrality and betweenness centrality were calculated to indicate the co-authorship. Cohesive subgroup analysis which includes components and k-core was then applied to show the connectivity of co-authorship network of Library Hi Tech. Findings The results indicated that around 42 percent of the articles were written by single author, while an increasing trend of multi-authored articles suggesting the collaboration among researchers in librarian research field becomes popular. Furthermore, the social network analysis identified authorship network with three core authors – Markey, K., Fourie, I. and Li, X. Finally, six core subgroups each included six or seven tightly connected researchers were also identified. Originality/value This study contributed to the existing literature by revealing the co-authorship network in librarian research field. Key researchers in the major subgroup were identified. This is one of the limited studies that describe the collaboration network among authors from different perspectives showing a more comprehensive co-authorship network.