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Personal profile

Biography

Jiangning is an Associate Professor and Group Leader in the Cancer and Infection and Immunity Programs in the Monash Biomedicine Discovery Institute (BDI), and Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia. He received his Ph.D. degree in Computer Science from Monash University, supervised by Prof Geoffrey Webb, Director of the Monash Centre for Data Science, and co-supervised by A/Prof Reza Haffari, at the Faculty of Information Technology, Monash University. Trained as a bioinformatician and data-savvy scientist, he has a very strong specialty in Artificial Intelligence, Bioinformatics, Comparative Genomics, Cancer Genomics, Bacterial Genomics, Computational Biomedicine, Data Mining, Infection and Immunity, Machine Learning, Proteomics, and Biomedical Big Data Analytics, which are highly sought-after expertise and skill sets in the data-driven, paradigm-shifting biomedical research. Ranked as one of the top-performing young Australian bioinformaticians, he was awarded the NHMRC Peter Doherty Biomedical Fellowship with his supervisor ARC Federation Fellow and ARC Australian Laureate Fellow Prof James Whisstock, Director of the ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, and Scientific Head of EMBL Australia.

 

He is Head of the Bioinformatics and Computational Biomedicine Laboratory in the Monash BDI and an Associate Investigator of the ARC Centre of Excellence in Advanced Molecular Imaging at Monash University. He is also a member of the Monash Centre for Data Science and the Monash Bioinformatics Platform. His main research interests are bioinformatics, heterogeneous data modeling, machine learning, and data analytics in the fields of infection and immunity, and cancer biology. Jiangning is currently an Academic Editor of BMC Bioinformatics (one of the three top-tier specialist journals in Bioinformatics), an Advisory Board Member of Current Protein & Peptide Science and Protein & Peptide Letters, and a Guest Editor of Current Bioinformatics and BioMed Res International. He is the Program Committee (PC) Co-Chair of the 30th International Conference on Genome Informatics (GIW) & Australian Bioinformatics and Computational Biology Society (ABACBS) Annual Conference, which will be held in Sydney on 9-11 December 2019. He is also the PC Co-Chair of the 10th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB) 2020, which will be held in Kyoto, Japan, during January 19-22, 2020. He is an International Expert Committee member on Bioinformatics, the International Joint Usage/Research Center (iJURC), Kyoto University, Japan.

 

Jiangning is a recipient of the NHMRC Peter Doherty Biomedical Fellowship (undertaken at Monash University, Australia) and both JSPS Long-term and Short-term Fellowships (undertaken at Kyoto University, Japan). He is a PC member for more than 20 international conferences on bioinformatics, computational biology, health informatics and e-Science, including BIBM, IEEE e-Science, InCoB, ISB, ICPB, ICIC, IIBM, and GIW. He is an invited reviewer for >60 journals (on average 50-80 reviews per year) in bioinformatics, computational biology, machine learning, data mining, systems biology, and chemoinformatics, including top-tier journals Briefings in BioinformaticsBioinformaticsNucleic Acids ResPLoS Comput Biol, J Theor Biol, Pattern Recog, Neurocomputing, Mol Cell ProteomicsBMC Bioinformatics, IEEE/ACM Trans Comput Biol and Bioinform, J Comput Chem, J Chem Info Model, and BMC Syst Biol.

 

Since 2007, he has been awarded 30 grants ($11.0M, 20 as CIA/CIB) by the US NIH, Australian NHMRC/ARC, and other granting bodies (e.g. the Japan Society for the Promotion of Science [JSPS], the Ministry of Science and Technology of  China [MOST], the Chinese Academy of Sciences [CAS] and the National Natural Science Foundation of China [NSFC], Monash University, and Kyoto University). Remarkably, in 2014, he secured a major five-year NIH R01 project (CIB, AI111965, US$4.56 million) with the program lead, NHMRC Principal Research Fellow Professor Jian Li at Monash University and other US collaborators to develop cutting-edge systems pharmacology approaches for predicting and validating antibiotic/small-molecule non-antibiotic combinations for new targeted therapies for treating life-threatening infections caused by antibiotic-resistant bacterial 'superbugs'. His recent work with Prof Tatsuya Akutsu at Kyoto University on developing novel bioinformatic algorithms for identifying substrates of apoptotic caspases and limited proteolysis has been recognized and awarded two Collaborative Research grants by Kyoto University Japan.

 

Jiangning is motivated to investigate, develop and deploy cutting-edge bioinformatics methodologies to better understand and address a range of open and challenging problems in genomics, molecular biology, and systems biology. To date, he and his team members have developed 60+ bioinformatics toolkits/webservers/software to serve the wider research community, including Cascleave, Cascleave 2.0, APIS, PROSPER, SSPKA, Crysalis, Crysf, Periscope, GlycoMine, SecretEPDB, PROSPERous, Bastion6, Bastion3, PhosphoPredict, POSSUM, iFeature, iProt-SubQuokka, Muscadel, iLearn, Procleave, DIFFUSER and DeepCleave. Many of these tools have been highlighted as useful bioinformatic tools and have been widely used by the international research community.

 

For instance, PROSPER (https://prosper.erc.monash.edu/, a bioinformatic tool for predicting target substrates and their specific cleavage sites for 23 proteases, delivered as a representative outcome of the support of the NHMRC Program Grant on Protease Systems Biology headed by Prof James Whisstock at Monash University), has attracted more than 30,000 unique users worldwide and processed >80,000 job submissions since its inception in the end of 2012. The PROSPER tool, together with Cascleave (http://sunflower.kuicr.kyoto-u.ac.jp/sjn/Cascleave/ (a bioinformatic tool for predicting substrates of cell apoptosis caspases, enzymes that play essential roles in programmed cell death and inflammation), has been highlighted as two powerful bioinformatic tools by the International Proteolysis Society (IPS). An ultrafast and high-throughput tool PROSPERous has been recently published in Bioinformatics for predicting the target substrates and cleavage sites for 90 different proteases and can be freely accessible at http://prosperous.erc.monash.edu/. The high impact of these tools signifies how biomedical knowledge discovery can indeed be alternatively catalyzed by data-driven machine-learning techniques.

 

A career is a journey. Jiangning has received substantial training in three world-leading universities - The University of Queensland, Kyoto University and Monash University, in the cutting-edge, cross-disciplinary bioinformatics, computational biomedicine, high-performance computing, mathematical modelling, machine learning and biomedical big data analytics. Some of the challenges encountered are part of his fast-paced, continuous learning curve, as a function of an incomplete knowledge in the new Era of 'Data Science and Analytics'.  Jiangning is always on the road to search for new knowledge, just like Monash University's motto quotes - "Ancora Imparo" ("I am still learning").  He is seen as a 'rare bird' as he has been extremely fortunate to work with and have three ARC Australian Federation/Laureate Fellows Prof Kevin Burrage (The University of Queensland & University of Oxford, now at Queensland University of Technology), Prof James Whisstock (Monash) and Prof Trevor Lithgow (FAA, Monash), Prof Tatsuya Akutsu (Kyoto University, Japan), IEEE Fellow Prof Geoffrey Webb (Monash), Prof Jian Li (NHMRC PRF, Monash), Prof Roger Daly (NHMRC PRF, Monash) and Prof Ian Smith (Vice-Provost of Monash University, Research and Research Infrastructure) as his collaborators and supervisors  throughout his academic career. Down the track along the journey, he has received substantial training in fermentation engineering, applied microbiology, protein engineering, applied mathematics, high-performance computing, computer science, statistics, bioinformatics, algorithms, structural biology, machine learning, data mining and integrative systems biology. Such a rare but critical 'blend' of caliber, expertise and skill sets places him in a privileged position to tackle and address inter-disciplinary research projects in the context of large-scale heterogeneous biomedical data analysis and modeling in the new era of Data Science and Analytics.

 

In his spare time, Jiangning enjoys his family time with his two adorable daughters Evelyn and Eleanor, watching movies, listening to music, and indulging himself in meandering the villages and discovering the heart of the beautiful Dandenong Ranges and Mornington Peninsula.

 

If you are interested in his research, you might want to read the following Monash BDI featured stories that report on his team's recent bioinformatics research:

https://www.monash.edu/medicine/discovery-institute/news-and-events/news/new-tool-to-speed-up-translation-of-genome-sequences

 

https://research.monash.edu/en/clippings/bioinformatics-tool-to-unearth-new-pointers-to-disease

 

If you would like to find out more about his research or inquire about potential collaboration opportunities, please contact Jiangning.Song@monash.edu.  Prospective postgraduate students in Australia or overseas who wish to pursue a Ph.D. career path with Jiangning at Monash University are also welcome to contact.

Keywords

  • Artificial intelligence
  • Bioinformatics
  • Computer science
  • Computational biomedicine
  • Computational biology
  • Machine learning
  • Data Mining
  • Big Data Analytics
  • From Genotype to Phenotype
  • Infectious Diseases
  • Cancer
  • Functional genomics
  • Sequence analysis
  • Feature engineering
  • Multimodal data
  • Protein post-translational modification
  • Pattern recognition
  • Structural biology
  • Antimicrobial resistance
  • Antibiotic resistance
  • Data-driven knowledge discovery
  • Bioinformatics software
  • Multi-variable data modeling
  • Heterogeneous data analysis
  • Computational pipeline
  • Host-pathogen interaction
  • High-throughput screening

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Projects 2007 2023

Research Output 2004 2019

A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

Mei, S., Li, F., Leier, A., Marquez-Lago, T. T., Giam, K., Croft, N. P., Akutsu, T., Smith, A. I., Li, J., Rossjohn, J., Purcell, A. W. & Song, J., 14 Jun 2019, (Accepted/In press) In : Briefings in Bioinformatics. 17 p., bbz051.

Research output: Contribution to journalReview ArticleResearchpeer-review

Bastion3: A two-layer ensemble predictor of type III secreted effectors

Wang, J., Li, J., Yang, B., Xie, R., Marquez-Lago, T. T., Leier, A., Hayashida, M., Akutsu, T., Zhang, Y., Chou, K-C., Selkrig, J., Zhou, T., Song, J. & Lithgow, T., 1 Jun 2019, In : Bioinformatics. 35, 12, p. 2017-2028 12 p., bty914.

Research output: Contribution to journalArticleResearchpeer-review

Characterization of the ERG-regulated Kinome in Prostate Cancer Identifies TNIK as a Potential Therapeutic Target

Lee, R. S., Zhang, L., Berger, A., Lawrence, M. G., Song, J., Niranjan, B., Davies, R. G., Lister, N. L., Sandhu, S. K., Rubin, M. A., Risbridger, G. P., Taylor, R. A., Rickman, D. S., Horvath, L. G. & Daly, R. J., 1 Apr 2019, In : Neoplasia (United States). 21, 4, p. 389-400 12 p.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File

Characterization of the Src-regulated kinome identifies SGK1 as a key mediator of Src-induced transformation

Ma, X., Zhang, L., Song, J., Nguyen, E., Lee, R. S., Rodgers, S. J., Li, F., Huang, C., Schittenhelm, R. B., Chan, H., Chheang, C., Wu, J., Brown, K. K., Mitchell, C. A., Simpson, K. J. & Daly, R. J., 17 Jan 2019, In : Nature Communications. 10, 1, 16 p., 296.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Zhang, Y., Xie, R., Wang, J., Leier, A., Marquez-Lago, T. T., Akutsu, T., Webb, G. I., Chou, K-C. & Song, J., 2019, (Accepted/In press) In : Briefings in Bioinformatics. 15 p., bby079.

Research output: Contribution to journalArticleResearchpeer-review

Press / Media

AI tool to boost genomic data analysis

Jiangning Song, Roger Daly & Jian Li

25/04/19

1 media contribution

Press/Media: Research

Bioinformatics tool to unearth new pointers to disease

Jiangning Song, Roger Daly & Fuyi Li

10/07/18

1 media contribution

Press/Media: Research

Genome scale model of a superbug

Trevor Lithgow, Jian Li & Jiangning Song

16/03/1830/03/18

2 media contributions

Press/Media: Research