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PhD projects

Artificial Intelligence, Bioinformatics, Biomedical Big Data Analytics, Medical Imaging, Computational Biomedicine, Computer Vision, Deep Learning, Multimodal Data Modelling, Pattern Recognition

20042022

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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. Trained as a bioinformatician and data-savvy scientist, he has a very strong speciality 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 data-driven, paradigm-shifting biomedical research.

 

He is Head of the AI-driven Bioinformatics and Biomedicine Laboratory in the Monash BDI and an Associate Investigator of the ARC Centre of Excellence in Advanced Molecular Imaging. He is also a member of the Monash Data Futures Institute (MDFI), Alliance for Digital Health at Monash (ADAM) and the Monash Bioinformatics Platform. His main research interests are bioinformatics, digital health, heterogeneous data modeling, machine learning, and data analytics in the fields of infection and immunity, and cancer biology/pathology. Jiangning is currently an Associate Editor of four top-tier bioinformatics and computational biology journals BMC Bioinformatics, Genomics, Proteomics & Bioinformatics, Frontiers in Bioinformatics, and BMC Genomic Data, and an Editorial Board member of Computers in Biology and MedicineBiomolecules, Protein & Peptide Letters and Advisory Board Member of Current Protein & Peptide Science, Guest Editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Current Bioinformatics, Frontiers in Genetics, Frontiers in Developmental and Cell Biology, BMC Genomics and BMC Medical Genomics. He is the Program Committee (PC) member for more than 20 international conferences in the fields of bioinformatics, computational biology and e-Science. He is the PC Co-Chair of the 30th International Conference on Genome Informatics (GIW) & Australian Bioinformatics and Computational Biology Society (ABACBS) Annual Conference, 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, held in Kyoto, Japan during January 19-22, 2020. He is the Publicity Co-Chair of The IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021 Houston, USA. He has an honorary position as Associate Professor at the Bioinformatics Center, Institute for Chemical Research and is an International Expert Committee member on Bioinformatics, the International Joint Usage/Research Center (iJURC), Kyoto University, Japan. He is also an Honorary Principal Research Fellow of the University of Melbourne. He joined Monash University's Centre to Impact AMR as a founding member in 2020 and chairs the bioinformatics and computational biomedicine group. He is developing the Centre’s AMR Big Data and AI-driven research capacity.

 

Jiangning 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 Nature Communications, Nature Machine Intelligence, Genome BiologyNucleic Acids ResBriefings in BioinformaticsBioinformaticsPLoS Comput Biol, J Theor Biol, Pattern Recog, Neurocomputing, Mol Cell ProteomicsBMC Bioinformatics, IEEE/ACM Trans Comput Biol and Bioinform, IEEE Journal of Biomedical and Health Informatics, and J Chem Info Model.

 

As Chief Investigator, he has been awarded 32 grants (~$19.0M, 22 as CIA/CIB) by the US NIH, Australian MRFF/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, 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-Sub, Quokka, 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 at 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 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 incomplete knowledge in the new Era of 'Data Science and Analytics'.  Jiangning is always on the road to searching 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), Prof James Whisstock (Monash), and Prof Trevor Lithgow (FAA, Monash), 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 skillsets places him in a privileged position to tackle and address interdisciplinary 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, 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 featured stories that report on his team and collaborators' most-recent interdisciplinary research:

 

https://www.monash.edu/impact-amr/case-studies-and-stories/antimicrobial-resistance-evolves-quickly,-and-we-need-to-be-prepared

 

https://www.monash.edu/medicine/news/latest/2020-articles/funding-for-fight-against-superbugs

 

https://www.monash.edu/discovery-institute/news-and-events/news/2019-articles/combating-superbugs-with-ai-and-big-data

 

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

 

University ranking: Monash University is one of Australia’s group of eight - the most research-intensive universities in the country - and consistently positioned in the top 100 universities globally (US News World: 40th; QS World: 58th; The Times World: 57th; CWTS Leiden World: 53th); Top 2 QS World University Rankings of Pharmacy & Pharmacology, Top 36 QS World University Rankings of Medicine. Monash has an international reputation for interdisciplinary research with an emphasis on global issues.

 

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.

Supervision interests

Jiangning is a Monash University Ph.D. mentor/supervisor and examiner. He supervises 8 PhD students (4 as primary supervisor) in the research areas of bioinformatics, medical imaging, computer vision, machine learning, high-throughput sequencing and multi-omics data analytics, and multimodal data modelling. He has supervised 8 PhD and 3 master completions to date. He currently chairs the PhD review committee for 4 Monash PhD students. Among his Ph.D. graduates, some received NHMRC CJ Martin Overseas Fellowships and others work as research leaders and staff members elsewhere.

Monash teaching commitment

 

BMS5022 Advanced Bioinformatics - teaching Bioinformatics and Aligning genomes and modelling

 

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

External positions

Honorary Principal Fellow, University of Melbourne

20 Oct 202120 Jan 2025

International Expert Committee member on Bioinformatics, the International Joint Usage/Research Center (iJURC), Kyoto University

1 Jan 202131 Dec 2024

Research area keywords

  • Artificial intelligence
  • Bioinformatics
  • Computer science
  • Computational biomedicine
  • Computational biology
  • Computer vision, image and signal processing
  • Machine learning
  • 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
  • Software engineering
  • Meta learning

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