Health, Biomedical Science, and Translational Research

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Biomedical Science is an exciting and dynamic field that is highly relevant to the understanding of the biology of human health and diagnostic and treatment of human diseases. It relies on several basic sciences including anatomy, physiology, molecular and cell biology, biochemistry, microbiology, genetics, immunology, physics, mathematics, and statistics and bioinformatics. 

In this regard, translational research seeks to produce more meaningful, applicable results that directly benefit human health by means of moving or "translating" basic science discoveries more quickly and efficiently into practice. But, actually, most of the findings from basic science do not result in new treatments, diagnostics and prevention.  This large gap is called the "valley of death". 

The traditional academic research paradigm has not matched up well with translational research, which focuses on applying knowledge (e.g. creating a product) rather than on intellectual novelty. Thus, to successfully cross this valley of death, public-private partnership between academia and industry has become increasingly common in the last years. The emergence of a key component or field from this alliance is translational research informatics, as seen as the intersection between biomedical informatics and translational research. 

New advances in biomedical sciences, capable also of generating massive datasets, and revolutionary approaches in statistics, machine learning, and artificial intelligence are transforming data collection, generation, and storing, and also changing the way we analyze data. Translational informatics increases the industry productivity by means of improving the understanding and use of data (e.g. pharmaceutical and clinical applications), and  also enables the researchers to analyze their datasets in an optimal and meaningful way. 

In NEWENCODE we have the complementary skill sets, approaches and resources you need for reaching the goals of your project, laboratory, research center, or biomedical company. Working together we will develop data processing pipelines, statistical and computational tools, including machine learning, for managing and analyzing your data, while we also educate your trainees in core data analyses.