Computer scientists are finding new ways to extract information from the literature, making data more accessible in bioinformatics, chemistry and clinical decision support
Translational bioinformatics in mental health: open access data sources and computational biomarker discovery
Tenenbaum JD, Bhuvaneshwar K, Gagliardi JP, Fultz Hollis K, Jia P, Ma L, Nagarajan R, Rakesh G, Subbian V, Visweswaran S, Zhao Z, Rozenblit L. Translational bioinformatics in mental health: open access data sources and computational biomarker discovery. Brief Bioinform. 2017 Nov 27. doi: 10.1093/bib/bbx157. [Epub ahead of print] PMID: 29186302
Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probemolecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic,metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) andmental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area ofmental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches tomolecular biomarker discovery thatmake use of mental health data.
Dr. Michael Becich to Give Keynote Address “Towards A Digital Pathology Commons” at Pathology Visions 2017
What Dental Educators Need to Understand About Emerging Technologies to Incorporate Them Effectively into the Educational Process
Stein CD, Eisenberg ES, O'Donnell JA, Spallek H. What dental educators need to understand about emerging technologies to incorporate them effectively into the educational process. J Dent Educ. 2014 Apr;78(4):520-9. PubMed PMID: 24706681
Many dental schools are currently struggling with the adoption of emerging technologies and the incorporation of these technologies into the educational process. Dental students exhibit an increasing degree of digital comfort when using social networking, mobile devices, search engines, or e-textbooks. Although the majority of students might consider themselves to be very skilled at using information technology, many faculty members would claim the opposite when evaluating their own knowledge and skills in the use of technology. As the use of technology, both formally and informally, continues to increase, dental educators are faced with many questions, such as: Does students’ digital comfort disguise a lack of information literacy? What is the appropriate path of implementing technology into teaching and learning, and how can institutions support such an implementation? This article surveys a series of myths that exist about the use of technology in education and raises questions about their validity and how dental educators can avoid being misled by them.
John J. Frazier, Corey D. Stein, Eugene Tseytlin, Tanja Bekhuis. Building a gold standard to construct search filters: A case study with biomarkers for oral cancer. J Med Libr Assoc. 2015 Jan;103(1):22-30. doi: 10.3163/1536-5050.103.1.005.
OBJECTIVE: To support clinical researchers, librarians and informationists may need search filters for particular tasks. Development of filters typically depends on a "gold standard" dataset. This paper describes generalizable methods for creating a gold standard to support future filter development and evaluation using oral squamous cell carcinoma (OSCC) as a case study. OSCC is the most common malignancy affecting the oral cavity. Investigation of biomarkers with potential prognostic utility is an active area of research in OSCC. The methods discussed here should be useful for designing quality search filters in similar domains.
METHODS: The authors searched MEDLINE for prognostic studies of OSCC, developed annotation guidelines for screeners, ran three calibration trials before annotating the remaining body of citations, and measured inter-annotator agreement (IAA).
RESULTS: We retrieved 1,818 citations. After calibration, we screened the remaining citations (n = 1,767; 97.2%); IAA was substantial (kappa = 0.76). The dataset has 497 (27.3%) citations representing OSCC studies of potential prognostic biomarkers.
CONCLUSIONS: The gold standard dataset is likely to be high quality and useful for future development and evaluation of filters for OSCC studies of potential prognostic biomarkers.
IMPLICATIONS: The methodology we used is generalizable to other domains requiring a reference standard to evaluate the performance of search filters. A gold standard is essential because the labels regarding relevance enable computation of diagnostic metrics, such as sensitivity and specificity. Librarians and informationists with data analysis skills could contribute to developing gold standard datasets and subsequent filters tuned for their patrons' domains of interest.
Background: Research networking systems hold great promise for helping biomedical scientists identify collaborators with the expertise needed to build interdisciplinary teams. Although efforts to date have focused primarily on collecting and aggregating information, less attention has been paid to the design of end-user tools for using these collections to identify collaborators. To be effective, collaborator search tools must provide researchers with easy access to information relevant to their collaboration needs.
Objective: The aim was to study user requirements and preferences for research networking system collaborator search tools and to design and evaluate a functional prototype.
Results: Initial interviews identified consensus regarding several novel requirements for collaborator search tools, including chronological display of publication and research funding information, the need for conjunctive keyword searches, and tools for tracking candidate collaborators. Participant responses were positive (SUS score: mean 76.4%, SD 13.9). Opportunities for improving the interface design were identified.
Conclusions: Interactive, timeline-based displays that support comparison of researcher productivity in funding and publication have the potential to effectively support searching for collaborators. Further refinement and longitudinal studies may be needed to better understand the implications of collaborator search tools for researcher workflows.