Qing Ping

TL;DR: An integrative study of the use of CiteSpace, a visual analytic tool for finding trends and patterns in scientific literature, is investigated and three levels of proficiency are identified: level 1: low proficiency, level 2: intermediate proficiency, and level 3: high proficiency.

TL;DR: Comparing and contrasts symptom cluster patterns derived from messages on a breast cancer forum with those from a symptom checklist completed by breast cancer survivors participating in a research study shows the copious amount of data generated by social media outlets can augment findings from traditional data sources.

TL;DR: The clustering results suggest that some symptom clusters are consistent across social media data and clinical data, such as gastrointestinal related symptoms, menopausal symptoms, mood-change symptoms, cognitive impairment, and pain-related symptoms.

TL;DR: An adaptive visual exploration system named PaperPoles is introduced to support exploration of scientific publications in a context‐aware environment and demonstrates a potentially effective workflow for adaptive visual search of complex information.

TL;DR: A novel algorithm named SCP is proposed, which combines the ranking by a modified PageRank algorithm based on subcellular compartments information, with theranking by Pearson correlation coefficient calculated from gene expression data, which shows that sub cellular localization information is promising in boosting essential protein prediction.