Dr Frank Lin

Senior Research Officer

Dr Frank Lin

I am a medical oncologist and a biomedical informatician at the Garvan Institute. I completed my undergraduate medical training in New Zealand in 2003 (University of Otago). After my internship year, I undertook doctoral studies in biomedical informatics at the University of New South Wales, using i

Research Level

Senior Research Officer

Biography

I am a medical oncologist and a biomedical informatician at the Garvan Institute. I completed my undergraduate medical training in New Zealand in 2003 (University of Otago). After my internship year, I undertook doctoral studies in biomedical informatics at the University of New South Wales, using informatics to study virulence factors of clinically relevant bacterial pathogens. I then commenced physician training in both New Zealand (Waikato Hospital) and Australia (St Vincent’s Hospital, Sydney), specialising in medical oncology.

The broad theme of my research is in the study of computational strategies to address complex information needs faced by oncologists, patients, and researchers alike. I am particularly interested in translating clinical and genomic data to deliver precision care in the oncology clinics. Some of my works, in the areas of machine learning-based decision support, electronic medical record (EMR) analysis, and clinical text mining, have been recognised previously.

I have developed several open source software projects for EMR-based clinical research, including a text mining pipeline for knowledge discovery from the EMR and a specialised ontology for profiling treatment outcomes of cancer patients from EMR. Recently, I have also been invited to serve on the editorial board of JCO Clinical Cancer Informatics.

I am a medical oncologist and a biomedical informatician at the Garvan Institute. I completed my undergraduate medical training in New Zealand in 2003 (University of Otago). After my internship year, I undertook doctoral studies in biomedical informatics at the University of New South Wales, using informatics to study virulence factors of clinically relevant bacterial pathogens. I then commenced physician training in both New Zealand (Waikato Hospital) and Australia (St Vincent’s Hospital, Sydney), specialising in medical oncology.

The broad theme of my research is in the study of computational strategies to address complex information needs faced by oncologists, patients, and researchers alike. I am particularly interested in translating clinical and genomic data to deliver precision care in the oncology clinics. Some of my works, in the areas of machine learning-based decision support, electronic medical record (EMR) analysis, and clinical text mining, have been recognised previously.

I have developed several open source software projects for EMR-based clinical research, including a text mining pipeline for knowledge discovery from the EMR and a specialised ontology for profiling treatment outcomes of cancer patients from EMR. Recently, I have also been invited to serve on the editorial board of JCO Clinical Cancer Informatics.

Awards and Honours

2019 - ASCO Breakthrough Abstract Award
2018 - Waikato Medical Research Foundation Research Grant
2018 - ANZUP/AstraZeneca Travel Fellowship
2017 - RACP Trainee Research Award of Excellence
2017 - Best Paper: Contributions from the 2016 Literature on Clinical Decision Support (IMIA)
2016 - Best Trainee Research Poster Award (Medical Oncology Group of Australia ASM, Gold Coast)
2016 - John Shine Translational Research Fellowship (SVHS/Garvan)
2011 - Best Research Poster Award (Bioinformatics & Computational Biology Conference, Singapore)
2005 - Australian Postgraduate Award (UNSW)
2001 - Summer Research Scholarship (University of Otago)

Education

2019 - FRACP, Internal Medicine (Medical Oncology), Royal Australasian College of Physicians
2009 - PhD, University of New South Wales (Medical informatics) - Australia
2003 - MB ChB, University of Otago - New Zealand

Selected Publications

Lin F, Groza T, Kocbek S, Epstein RJ. The Cancer Care Treatment Outcome Ontology (CCTOO): a novel computable ontology for profiling treatment outcomes in patients with solid tumors. JCO Clin Cancer Inform, 2018; 2: 1-14

Lin F, Pokorny A, Teng C, Epstein RJ. TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records. Sci Rep 2017; 7: 6918

Epstein RJ and Lin F. Cancer and the omics revolution. Aust Fam Physician 2017; 46(4): 189-193

Lin FP, Pokorny A, Teng C, Dear RF, Epstein RJ. Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach. BMC Cancer 2016; 16: 929. (IMIA Best Paper: Contributions from the 2016 Literature on Clinical Decision Support. Ref: Verlag and Stuttgart. Yearb Med Inform 2017; 26: 137-138).

Tan E, Lin FP, Sheck L, Salmon P J, Ng S. Growth of peri-ocular Basal Cell Carcinoma – the impact of waiting time for elective surgery. Br J Dermatol. 2015; 172 (4): 1002–1007

Tan E, Lin F, Sheck L, Salmon P, and Ng S. A practical decision-tree model to predict complexity of reconstructive surgery after periocular basal cell carcinoma excision. J Eur Acad Dermatol Venereol 2016; 31(4): 717-723.

Tsafnat G, Jasch D, Misra A, Choong MK, Lin FP, Coiera E. Gene-disease association with literature based enrichment. J Biomed Inform. 2014; 49: 221–226

Gallego B, Perez-Concha O, Lin F, Coiera E. Exploring the role of pathology test results in the prediction of remaining days of hospitalisation. Stud Health Technol Inform. 2012; 178:45-50

Polasek TM, Doogue MP, Lin FP. Clinically relevant information on pharmacokinetic drug-drug interactions. J Pharm Pract Res. 2011; 41(1):77-78

Lin F, Anthony S, Polasek TM, Tsafnat G,  Doogue MP. BICEPP: a statistical text mining framework for predicting the binary characteristics of drugs. BMC Bioinformatics 2011; 12: 112

Polasek TM, Lin F, Miners JO, Doogue MP. Perpetrators of pharmacokinetic drug-drug interactions arising from altered cytochrome P450 activity: a criteria-based assessment. Br J Clin Pharmacol 2011; 71(5): 727–736

Lin F, Lan RT, Sintchenko V, Kong F, Gilbert GL, Coiera E. Computational genome-wide analysis of phylogenetic profiles reveal potential virulence genes of Streptococcus agalactiae. PLoS ONE 2011; 6(4): e17964

Sintchenko V, Anthony S, Phan XH, Lin F, and Coiera EW. A PubMed-Wide Associational Study of Infectious Diseases. PLoS ONE. 2010; 5(3): e9535

Lin F, Coiera E, Lan RT, Sintchenko V. In silico prioritisation of candidate genes for prokaryotic gene function discovery. BMC Bioinform 2009; 10:86

Lin F, Sintchenko V, Kong F, Gilbert GL, Coiera E. Commonly-used molecular epidemiology markers of Streptococcus agalactiae do not appear to predict virulence. Pathology 2009; 41(6): 576–581