Song Lab research
About Associate Professor Jiangning Song
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 Artificial Intelligence, and co-supervised by Dr 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 a four-year NHMRC Peter Doherty Biomedical Fellowship (2008-2012) 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. He 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 an International Expert Committee member on bioinformatics, the International Joint Usage/Research Center (iJURC), 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, APIS, PROSPER, SSPKA, Crysalis, Periscope, GlycoMine, SecretEPDB, PROSPERous, Bastion6, Bastion3, PhosphoPredict, POSSUM, iFeature, iProt-Sub, Quokka, Muscadel, MULTiPly, iLearn, DeepPRoMIse, and DeepCleave. Many of these tools have been highlighted as useful bioinformatic tools and have been widely used by the international research community.
- Bioinformatics approaches for targeting secreted effector proteins and virulence factors in Gram-positive and -negative pathogenic bacteria
- Reconstruction of structural interaction networks at the host-pathogen synapse
- Development of artificial intelligence programs to enable antimicrobial resistance phenotype prediction and antibiotic treatment regimen optimization from whole-genome sequence data
- Predicting the functional impact of regulatory and coding variants de novo in the human genome
- Computational oncology by integrating multi-omics data, histopathology and machine learning techniques
- Machine-learning-based approaches for interrogating protein post-translational modification stoichiometry and functional phenotypes
Visit Associate Professor Song's Monash research profile to see a full listing of current projects.
Our group’s research interests are primarily focused on the analysis, prediction, modelling and annotation of both sequence and three-dimensional structures of biological macromolecules on the whole-genomic and -proteomic scale by developing data-driven computational methods. In this context, machine-learning techniques have recently provided cost-effective solutions to challenging problems that were previously considered difficult to address. The development of heterogeneous biological feature-integrated approaches and tools based on machine learning and data mining makes it possible to further our understanding of the complex biological systems and discovery new biochemical knowledge by learning from the existing data. To date, our bioinformatics team at Monash University has developed more than 60 different bioinformatics tools and web servers. These include several widely used tools such as Cascleave, PROSPER, GlycoMine, POSSUM, PhosphoPredict, Quokka, PROSPERous and iFeature.
The flowchart of our developed PROSPER 2.0 web server
- artificial intelligence
- machine learning
- deep learning
- feature engineering
- data integration
- sequence analysis
- homology modelling
- imaging analysis
- pattern recognition
We collaborate with many scientists and research organisations around the world. Click on the map to see the details for each of these collaborators (dive into specific publications and outputs by clicking on the dots).
Student research projects
The Song Lab offers a variety of Honours, Masters and PhD projects for students interested in joining our group. There are also a number of short term research opportunities available.
Please visit Supervisor Connect to explore the projects currently available in our Lab.