Big Data for Treatment Planning: Pathways and Possibilities for Smart Healthcare Systems

Background: Treatment planning is one of the crucial stages of healthcare assessment and delivery. Moreover, it also has a significant impact on patient outcomes and system efficiency. With the evolution of transformative healthcare technologies, most areas of healthcare have started collecting data at different levels, as a result of which there is a splurge in the size and complexity of health data being generated every minute. Introduction: This paper explores the different characteristics of health data with respect to big data. Besides this, it also classifies research efforts in treatment planning on the basis of the informatics domain being used, which include medical informatics, imaging informatics and translational bioinformatics. Method: This is a survey paper that reviews existing literature on the use of big data technologies for treatment planning in the healthcare ecosystem. Therefore, a qualitative research methodology was adopted for this work. Results : Review of existing literature has been analyzed to identify potential gaps in research, identifying and providing insights into high prospect areas for potential future research. Conclusion: Use of big data for treatment planning is rapidly evolving and findings of this research can head start and streamline specific research pathways in the field.

Predictions, Pivots, and a Pandemic: a Review of 2020's Top Translational Bioinformatics Publications

Summary Objectives: Provide an overview of the emerging themes and notable papers which were published in 2020 in the field of Bioinformatics and Translational Informatics (BTI) for the International Medical Informatics Association Yearbook. Methods: A team of 16 individuals scanned the literature from the past year. Using a scoring rubric, papers were evaluated on their novelty, importance, and objective quality. 1,224 Medical Subject Headings (MeSH) terms extracted from these papers were used to identify themes and research focuses. The authors then used the scoring results to select notable papers and trends presented in this manuscript. Results: The search phase identified 263 potential papers and central themes of coronavirus disease 2019 (COVID-19), machine learning, and bioinformatics were examined in greater detail. Conclusions: When addressing a once in a centruy pandemic, scientists worldwide answered the call, with informaticians playing a critical role. Productivity and innovations reached new heights in both TBI and science, but significant research gaps remain.

MicroRNA childhood Cancer Catalog (M3Cs): A Resource for Translational Bioinformatics Toward Health Informatics in Pediatric Cancer.

Background: MicroRNA childhood Cancer Catalog (M3Cs) is high-quality curated collection of published miRNA research studies on 16 pediatric cancer diseases. M3Cs scope was based on two approaches: data-driven clinical significance and data-driven human pediatric cell line models. Method: M3Cs development passed through three phases: 1. Literature Mining: It includes external database search and screening. 2. Data processing that includes 3 steps: a- Data Extraction, b- Data Curation & annotation, c- Web Development. 3. Publishing: Shinyapps.io was used as a web interface for the deployment of M3Cs. M3Cs is now available online and can be accessed through https://m3cs.shinyapps.io/M3Cs/. Results: For Data-driven clinical significance Approach, 538 miRNAs from 268 publications were reported in the clinical domain while 7 miRNAs from 5 publications were reported in the clinical & drug domain. For data-driven human pediatric cell line models approach, 538 miRNAs from 1268 publications were reported in cell line domain while 211 miRNAs from 177 publications in cell line & drug domain. Conclusion: M3Cs acted to fill the gap by applying translational bioinformatics (TBI) general pathway to transfer data-driven research toward data-driven clinical care and/or hypothesis generation. Aggregated and well-curated data of M3Cs will enable stakeholders in health care to incorporate miRNA in the clinical policy.

Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors—let us call that the silver bullet approach to cancer therapeutics—to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns—we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases “shrapnel gunshots” may become more effective than “silver bullets”. Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.

Translational Bioinformatics of HIV Peptide 2.0: From Dry To Wet Bench

Infectious diseases have become part of human civilization as a constant phenomenon throughout time. The Human Immunodeficiency Virus (HIV) is a virus that can cause immune system weakness by attacking cells that have CD4 receptors and CCR5 or CXCR4 chemokine coreceptors, namely helper T cells macrophages and dendritic cells. Therefore, people infected with this virus will be vulnerable to opportunistic infections and cancer. Here we proposed the translational bioinformatics approach from dry to wet bench. From proof of concept to the applications will be highlighted and discussed further in diagnostic of HIV and vaccine candidate development based on reverse vaccinology approach. We performed sequence data analysis to find the best epitope for HIV strains in Indonesia and compatible with the MHC class 1, the linear core with MHC class 2, and the B cell epitope. Epitope compatible with the two cells is expected to have high immunogenicity and potentially produce a response to good T memory. The epitope was then tested using an indirect ELISA against the serum of HIV positive patients. In this study, a negative control is used in blood serum from healthy individuals originating from Stored Biological Material and carried out with a commercial ELISA kit for the negative controls. The epitope in PBS buffer solution was coated to a microplate with an incubation time of 16 hours and various concentrations. As a result, the epitope from the concentration range of 1-2500 ng epitope/well can be recognized by HIV patient antibodies. In summary, the epitope from the in silico prediction had antigenicity against anti-HIV antibodies in sera samples and could also be a promising candidate for the vaccine.