AI helps biologists understand complex data to come up with targeted therapeutics for patients
When Dr Chen Jinmiao was studying for her PhD in 2002 at Nanyang Technological University, she invented novel artificial neural network algorithms to characterise the protein structure of cells. However, the papers she published were not well cited.
It dawned on her that her lack of deep understanding of immunology and biology might be due to her computer science background. She then dived deep into learning about immunology, determined to leverage artificial intelligence (AI) to help biologists transform data into meaningful findings that could be used to help patients.
Today, Dr Chen is ranked as a Highly Cited Researcher and recognised as one of the world's most influential scientific minds in 2022. She is a Principal Investigator (PI) and heads a team of 15 researchers at A*STAR’s Singapore Immunology Network (SIgN).
"One of the innovations we do at SIgN is precision immunology, a subset of precision medicine," she says. Her team leverages AI to analyse the microenvironment of tumour cells to understand their heterogeneity, so therapeutics can eventually be developed to target the malignant cells within the tumour.
"We use single cell and spatial omics technologies to analyse and derive findings to help design new drugs for specific cancer treatment. This is important, as cell heterogeneity is very high even within a tumour."
Dr Chen also works on spatial transcriptomics, where she analyses the spatial distance, including interaction, between immune cells and tumour cells. The data biologists collect is validated and used to build algorithms investigating spatial relationships where immune cells can optimally cure cancer cells.
Deeply Integrated Single-Cell Omics data (DISCO)
In addition, her team has developed a database of Deeply Integrated Single-Cell Omics (DISCO) data. The site is a valuable integrated cell atlas with harmonised data for biologists to analyse, derive and share new findings that benefit the medical community for drug discovery.
"For example, a PI has collected 20 patient samples for his study targeting gastric cancer. The PI can then tap on the public domain, DISCO, to compare in-house data with published data. It saves time and money, because collecting patient data can be expensive." She explains.
With the explosion of big data, AI helps to pull together complex data in ways that traditional statistical analysis systems lack,
Having immersed herself in the role since 2009, Dr Chen shares that her job is like a "match made in heaven" where she leverages her AI expertise to further immunology discoveries.
"There's a gap between computer science and immunology disciplines," she adds. "I hope to be able to build up a team of competent people across these disciplines to further the development of science."
With a vision of doing something for the greater good of society, Dr Chen urges scientists and those considering a research career to stay curious, be ambitious about pursuing their passion and expand their knowledge and network through collaboration.