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Development of a biomarker database toward performing disease classification and finding disease interrelations.

ABSTRACT: A biomarker is a measurable indicator of a disease or abnormal state of a body that plays an important role in disease diagnosis, prognosis and treatment. The biomarker has become a significant topic due to its versatile usage in the medical field and in rapid detection of the presence or severity of some diseases. The volume of biomarker data is rapidly increasing and the identified data are scattered. To provide comprehensive information, the explosively growing data need to be recorded in a single platform. There is no open-source freely available comprehensive online biomarker database. To fulfill this purpose, we have developed a human biomarker database as part of the KNApSAcK family databases which contain a vast quantity of information on the relationships between biomarkers and diseases. We have classified the diseases into 18 disease classes, mostly according to the National Center for Biotechnology Information definitions. Apart from this database development, we also have performed disease classification by separately using protein and metabolite biomarkers based on the network clustering algorithm DPClusO and hierarchical clustering. Finally, we reached a conclusion about the relationships among the disease classes. The human biomarker database can be accessed online and the inter-disease relationships may be helpful in understanding the molecular mechanisms of diseases. To our knowledge, this is one of the first approaches to classify diseases based on biomarkers. Database URL: http://www.knapsackfamily.com/Biomarker/top.php.

SUBMITTER: Hossain SF

PROVIDER: S-EPMC7951048 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCurrently, many risk assessment tools are available for clinicians to assess a patient's periodontal disease risk. Numerous studies demonstrate the potential of these tools to promote preventive management and reduce morbidity due to periodontal disease. Despite these promising results, solo and small group dental practices, where most people receive care, have not adopted risk assessment tools widely, primarily due to lack of studies in these settings. The objective of this study was to explore the knowledge, attitudes, and beliefs of dental providers in these settings toward risk-based care through focus groups.MethodsWe conducted six focus group sessions with 52 dentists and dental hygienists practicing in solo and small group practices in Pittsburgh, PA and New York City (NYC), NY. An experienced moderator and a note-taker conducted the six sessions, each including 8-10 participants and lasting approximately 90 min. All sessions were audio-recorded and transcribed verbatim. Two researchers coded the focus group transcripts. Using a thematic analysis approach, they reviewed the coding results to identify important themes and selected representative excerpts that best described each theme.ResultsProviders strongly believed identifying risk factors could predict periodontal disease and use this information to change their patients' behavior. A successful risk assessment tool could assist them in educating and changing their patient's behaviors to adopt a healthy lifestyle, thus enabling them to play a major role in their patients' overall health. However, to achieve this goal, it is essential to educate all dental providers and not just dentists on performing risk assessment and translating the results into actionable recommendations for patients. According to study participants, the research community has focused more on translating research findings into a risk assessment tool, and less on how clinicians would use these tools during patient encounters and if it affects a patients' risk or outcome.ConclusionsDental practitioners were open to performing risk assessment as routine care and playing a bigger role in their patients' overall health. Recommendations to overcome major barriers included educating dental providers at all levels, conducting more research about their adoption and use in real-world settings and developing appropriate reimbursement models.

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Development of a biomarker database toward performing disease classification and finding disease interrelations.

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