Translational Bioinformatics: Past, Present, and Future

Though a relatively young discipline, translational bioinformatics （TBI） has become a key component of biomedical research in the era of precision medicine. Development of high-throughput technologies and electronic health records has caused a paradigm shift in both healthcare and biomedical research. Novel tools and methods are required to convert increasingly voluminous datasets into information and actionable knowledge. This review provides a definition and contex- tualization of the term TBI, describes the discipline＇s brief history and past accomplishments, as well as current loci, and concludes with predictions of future directions in the field.

CTRR-ncRNA:A Knowledgebase for Cancer Therapy Resistance and Recurrence Associated Non-coding RNAs

Cancer therapy resistance and recurrence(CTRR)are the dominant causes of death in cancer patients.Recent studies have indicated that non-coding RNAs(ncRNAs)can not only reverse the resistance to cancer therapy but also are crucial biomarkers for the evaluation and prediction of CTRR.Herein,we developed CTRR-ncRNA,a knowledgebase of CTRR-associated ncRNAs,aiming to provide an accurate and comprehensive resource for research involving the association between CTRR and ncRNAs.Compared to most of the existing cancer databases,CTRRncRNA is focused on the clinical characterization of cancers,including cancer subtypes,as well as survival outcomes and responses to personalized therapy of cancer patients.Information pertaining to biomarker ncRNAs has also been documented for the development of personalized CTRR prediction.A user-friendly interface and several functional modules have been incorporated into the database.Based on the preliminary analysis of genotype-phenotype relationships,universal ncRNAs have been found to be potential biomarkers for CTRR.The CTRR-ncRNA is a translation-oriented knowledgebase and it provides a valuable resource for mechanistic investigations and explainable artificial intelligence-based modeling.

Opportunities for Computational Techniques for Multi-Omics Integrated Personalized Medicine

Personalized medicine is defined as ＂a model of healthcare that is predictive, personalized, preventive,and participator＂ and has very broad content. With the rapid development of high-throughput technologies, an explosive accumulation of biological information is collected from multiple layers of biological processes, including genomics, transcriptomics, proteomics, metabonomics, and interactomics（omics）. Implementing integrative analysis of these multiple omics data is the best way of deriving systematical and comprehensive views of living organisms, achieving better understanding of disease mechanisms, and finding operable personalized health treatments. With the help of computational methods, research in the field of biology and biomedicine has gained tremendous benefits over the past few decades. In the new era of personalized medicine, we will rely more on the assistance of computational analysis. In this paper, we briefly review the generation of multiple omics and their basic characteristics. And then the challenges and opportunities for computational analysis are discussed and some state-of-art analysis methods that were recently proposed by peers for integrative analysis of multiple omics data are reviewed. We foresee that further integrated omics data platform and computational tools would help to translate the biological knowledge to clinical usage and accelerate development of personalized medicine.