ADVANTURES AND INNOVATIONS
Our scientific interest
We are passionate about improving human health with our expertise in computational biology and pharmacology. We are committed to helping biomedical and pharmacological researchers and developers find the most effective drug, vaccine, and treatment.
Precision medicine is to enhance paient care through refined diagnostic sensitivity, prognostic accuracy and pinpoint therapeutic design that can be acquired via translating insights from genomics to medicine. Steep decline in the cost of sequencing and global sharing of sequence and clinical data gave rise to steep increase in the potential for data scientists to apply powerful computational methods to discover causative genes and variants, to deeper understand disease etiology, and eventually to cure patients successfully.
Drug development pipeline is a complex, time-consuming, and expensive workflow that involves multiple stages with ample capability for application of machine learning to speed up the process with reduced cost. With the available data becoming bigger and more thoroughly covering the whole data space, with the computer power and computational techniques advacong, machine learning is increasingly applied in drug development to identify novel drug targets and biomarkers implicated in disease onset and development, to screen pharmacologic agent candidates for feasible lab test, to design small molecules drugs in silico.
Computational immunology combined with advances in genomic sciences and structural biology potentiated breakthroughs in vaccine design and optimization in the past decade. Computational approaches have proven to made essential contributions to these breakthroughs with identification of potential vaccine antigens, prediction of epitopes, enabling ratinal optimization of protective antigens. Computational immunology will be an integral part of the next-generation vaccine design.
Initial successes in cancer immunotherapy have embarked a hope for a revolution in cancer treatment, and sparked intensive basic and translational research in cancer immunity. In accordance to the call, computational methods and software tools are needed to mine inferences from datasets generated from novel cutting-edge technologies to interrogate cancer immunity for comprehensive molecular and cellular characterization, to identify tumor-associated antigens or neoantigens, to predict immune checkpoint blockade treatment response.
Preventive medicine and early detection can significantly reduce health costs, and improve possible treatment effects and prognosis. Computational methods applying on abundant biomedical data shared by the scientific community can help us develop models which, for a given person, can tell any diseases the person is predisposed to, and identify any potential indolent diseases.