Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks

<p>Abstract</p> <p>Background</p> <p>Uncovering cellular roles of a protein is a task of tremendous importance and complexity that requires dedicated experimental work as well as often sophisticated data mining and processing tools. Protein functions, often referred to as its annotations, are believed to manifest themselves through topology of the networks of inter-proteins interactions. In particular, there is a growing body of evidence that proteins performing the same function are more likely to interact with each other than with proteins with other functions. However, since functional annotation and protein network topology are often studied separately, the direct relationship between them has not been comprehensively demonstrated. In addition to having the general biological significance, such demonstration would further validate the data extraction and processing methods used to compose protein annotation and protein-protein interactions datasets.</p> <p>Results</p> <p>We developed a method for automatic extraction of protein functional annotation from scientific text based on the Natural Language Processing (NLP) technology. For the protein annotation extracted from the entire PubMed, we evaluated the precision and recall rates, and compared the performance of the automatic extraction technology to that of manual curation used in public Gene Ontology (GO) annotation. In the second part of our presentation, we reported a large-scale investigation into the correspondence between communities in the literature-based protein networks and GO annotation groups of functionally related proteins. We found a comprehensive two-way match: proteins within biological annotation groups form significantly denser linked network clusters than expected by chance and, conversely, densely linked network communities exhibit a pronounced non-random overlap with GO groups. We also expanded the publicly available GO biological process annotation using the relations extracted by our NLP technology. An increase in the number and size of GO groups without any noticeable decrease of the link density within the groups indicated that this expansion significantly broadens the public GO annotation without diluting its quality. We revealed that functional GO annotation correlates mostly with clustering in a physical interaction protein network, while its overlap with indirect regulatory network communities is two to three times smaller.</p> <p>Conclusion</p> <p>Protein functional annotations extracted by the NLP technology expand and enrich the existing GO annotation system. The GO functional modularity correlates mostly with the clustering in the physical interaction network, suggesting that the essential role of structural organization maintained by these interactions. Reciprocally, clustering of proteins in physical interaction networks can serve as an evidence for their functional similarity.</p

On Modeling the Quality of Nutrition for Healthy Ageing Using Fuzzy Cognitive Maps

Modelling dietary intake of older adults can prevent nutritional deficiencies and diet-related diseases, improving their quality of life. Towards such direction, a Fuzzy Cognitive Map (FCM)-based modelling approach that models the interdependencies between the factors that affect the Quality of Nutrition (QoN) is presented here. The proposed FCM-QoN model uses a FCM with seven input-one output concepts, i.e., five food groups of the UK Eatwell Plate, Water (H2O), and older adult’s Emotional State (EmoS), outputting the QoN. The weights incorporated in the FCM structure were drawn from an experts’ panel, via a Fuzzy Logic-based knowledge representation process. Using various levels of analysis (causalities, static/feedback cycles), the role of EmoS and H2O in the QoN was identified, along with the one of Fruits/Vegetables and Protein affecting the sustainability of effective food combinations. In general, the FCM-QoN approach has the potential to explore different dietary scenarios, helping health professionals to promote healthy ageing and providing prognostic simulations for diseases effect (such as Parkinson’s) on dietary habits, as used in the H2020 i-Prognosis project (www.i-prognosis.eu)

Sentimental causal rule discovery from twitter

Social media, especially Twitter is now one of the most popular platforms where people can freely express their opinion. However, it is difficult to extract important summary information from many millions of tweets sent every hour. In this work we propose a new concept, sentimental causal rules, and techniques for extracting sentimental causal rules from textual data sources such as Twitter which combine sentiment analysis and causal rule discovery. Sentiment analysis refers to the task of extracting public sentiment from textual data. The value in sentiment analysis lies in its ability to reflect popularly voiced perceptions that are stated in natural language. Causal rules on the other hand indicate associations between different concepts in a context where one (or several concepts) cause(s) the other(s). We believe that sentimental causal rules are an effective summarization mechanism that combine causal relations among different aspects extracted from textual data as well as the sentiment embedded in these causal relationships. In order to show the effectiveness of sentimental causal rules, we have conducted experiments on Twitter data collected on the Kurdish political issue in Turkey which has been an ongoing heated public debate for many years. Our experiments on Twitter data show that sentimental causal rule discovery is an effective method to summarize information about important aspects of an issue in Twitter which may further be used by politicians for better policy making