Computational Systems Biology
The Computational Systems Biology Group develops computational and statistical models to characterise complex biological systems during differentiation and development.
Molecular regulations in cellular systems are central to health and disease. The Computational Systems Biology Group, led by Dr Pengyi Yang, focuses on developing computational and statistical models to reconstruct molecular networks and model their regulations in differentiation and development. To translate computational predictions to biological findings, the group also focuses on experimentally validating hypotheses generated from computational models.
Group Leader, Computational Systems Biology
Molecular trans-regulatory networks (TRNs) comprised of cell signalling, transcriptional, translational, and (epi)genomic regulations are central to health and disease. A major initiative in our group is to integrate trans-omic datasets generated by state‐of‐the‐art mass spectrometer (MS) and next-generation sequencer (NGS) from various cell systems for reconstructing TRNs and understand how different regulatory machineries (e.g. signalling, transcription, and epigenomics) co-operate to define cell states, functions, and fates.
We have previously developed various computational methods to integrate the multi-layered trans‐omic datasets generated during naive to formative pluripotency transition in embryonic stem cells (ESCs) (Yang et al. Cell Systems, 2019). Our current research project aims to further this study by developing methods to characterise signaling cascades, transcriptional networks, and protein networks and their cross‐talks with the aim of answering the following questions:
Single-cell based omics are becoming the next wave of development in biotechnologies. promising to revolutionise our ability to study biological systems at an unprecedented resolution. Our group is working on multiple methodological development and lab experiment projects with the goal of characterising cellular systems and diseases at the single-cell level.
On the methodology front, we have recently developed a computational method together with Prof. Jean Yang's group for multiple single-cell RNA-seq data integration (Lin et al. PNAS, 2019). Our current research project aims to extend on this work by developing a suite of data processing, cell type characterisation, and network reconstruction methods and tools for single-cell omic data. In parallel, we are planning to conduct experiments to profile single cells in ESC populations and during their differentiation to multiple cell lineages. Research findings from these projects will directly contribute to our aim in addressing the three questions raised in Theme I.
Computational and statistical methods are at the core of our research. To tackle complex biological questions by utilising heterogenous omic data generated from various biotechnology, our group is specialised in developing novel computational methods for analysing (i) MS-based proteomic and phosphoproteomic data, and (ii) NGS-based RNA-seq, ChIP-seq, and Hi-C data.
Build on our long-term success in computational methodology innovation, the group is developing various machine learning and deep learning methods with targeted application to biological questions and omic data types. Example of our recent developments include a knowledge-based unsupervised learning method for kinase identification (Yang et al. PLoS Computational Biology, 2015) and a semi-supervised learning method for kinase-substrate prediction (Yang et al. Bioinformatics, 2016) from phosphoproteomic data. Continued innovation in computational and statistical methods will be a key force of our group in answering fundamental biological questions.
Full List is in Reverse Chronological Order
Total peer-reviewed articles: 63; First and/or corresponding author: 40/63 (63.5%)
† Co-first author
* Corresponding/Co-corresponding author
# Lead bioinformatician
IF (5-year impact factor, Thomson Reuters 2019)
View all publications by Pengyi Yang.
Yue C, Geddes T, Yang J, Yang P* (2020), Nature Machine Intelligence, doi:10.1038/s42256-020-0217-y.
Kim T, Lin Y, Geddes T, Yang J, Yang P* (2020). Bioinformatics, 36(14):4137-4143. (IF: 9.9)
Kaur S, Vuong J, Peters T, Luu L, Yang P, Krycer J, O’Donohue S (2020). npj Systems Biology and Applications, 6:22. (IF: 4.3)
Lin Y, Cao Y, Kim H, Salim A, Speed T, Lin D, Yang P*, Yang J* (2020). Molecular Systems Biology, 16(6): e9389. (IF: 10.0)
Kim H, Osteil P, Humphrey S, Cinghu S, Oldfield A, Patrick E, Wilkie E, Peng G, Suo S, Jothi R, Tam P, Yang P* (2020). Nucleic Acids Research, 48(4):1828-1842. (IF: 11.8)
Kim T, Lo K, Geddes T, Kim H, Yang J, Yang P* (2019). BMC Genomics, 20:913. (IF: 4.1)
Su Z†, Burchfield J†, Yang P†, Humphrey S, Yang G, Francis D, Yasmin S, Shin S, Norris D, Kearney A, Astore M, Scavuzzo J, Fisher-Wellman K, Wang Q, Parker B, Neely G, Vafaee F, Chiu J, Yeo R, Hogg P, Fazakerley D, Nguyen L, Kuyucak S, James D (2019). Nature Communications, 10:5486. (IF: 13.6)
Geddes T, Kim T, Nan L, Burchfield J, Yang J, Tao D, Yang P* (2019). BMC Bioinformatics, 20:660. (IF: 3.2)
Cao Y, Lin Y, Ormerod J, Yang P, Yang J, Lo K (2019) BMC Bioinformatics, 20:721. (IF: 3.2)
Azimi A, Yang P, Ali M, Howard V, Mann G, Kaufman K, Fernandez-Penas P (2019). Journal of Investigative Dermatology, 140(1):212-222. (IF: 6.9).
Lin Y, Ghazanfar S, Strbenac D, Wang A, Patrick E, Lin D, Speed T, Yang J*, Yang P* (2019). GigaScience, 8(9):giz106. (IF: 7.7)
Oldfield A, Henriques T, Kumar D, Burkholder A, Cinghu S, Paulet D, Bennett B, Yang P, Scruggs B, Lavender C, Rivals E, Adelman K, Jothi R (2019), Nature Communications.
Yang P †*, Humphrey S †*, Cinghu S†, Pathania R, Oldfield A, Kumar D, Perera D, Yang J, James D, Mann M, Jothi R* (2019) Cell Systems, 8(5), 427-445.
Lin Y, Ghazanfar S, Wang K, Gagnon-Bartsch J, Lo K, Su X, Han Z, Ormerod J, Speed T, Yang P*, Yang J* (2019). Proceedings of the National Academy of Sciences of the United States of America, 116(20):9775-9784. (IF: 10.6)
Kim T, Chen I, Parker B, Humphrey S, Crossett B, Cordwell S, Yang P*, Yang J* (2019). Proteomics, 19(13):1900068. (IF: 3.5)
Parker B†, Calkin A†, Seldin M†, Keating M, Tarling E, Yang P, Moody S, Liu Y, Zerenturk E, Needham, E, Jayawardana K, Pan C, Mellet N, Weir J, Lazarus R, Lusis A, Meikle P, James D, Vallim T, Drew B (2019). Nature, 567:187-193. (IF: 46.5)
Kim T, Chen I, Lin Y, Wang A, Yang J, Yang P* (2019). Briefings in Bioinformatics, 20(6):2316-2326. (IF: 7.5)
O'Sullivan J, Neylon A, Fahey E, Yang P, McGorrian C, Blake G (2019). Heart Asia, 11(1):e011134.
Ridder M, Klein K, Yang J, Yang P, Lagopoulos J, Hickie I, Bennett M, Kim J (2019). Neuroinformatics, 17(2):211-223. (IF: 5.0)
Yang P*, Ormerod J, Liu W, Ma C, Zomaya A, Yang J (2019). IEEE Transactions on Cybernetics, 49(5):1932-1943. (IF: 10.1)
Fazakerley D, Chaudhuri R, Yang P, Maghzal G, Thomas K, Krycer J, Humphrey S, Parker B, Fisher-Wellman K, Meoli C, Hoffman N, Diskin C, Burchfield J, Cowley M, Kaplan W, Modrusan Z, Kolumam G, Yang H, Chen D, Samocha-Bonet D, Greenfield J, Hoehn K, Stocker R, James D (2018). eLIFE, 7:e3211. (IF: 8.2)
Cinghu S†, Yang P†, Kosak J, Conway A, Kumar D, Oldfield A, Adelman K, Jothi R (2017). Molecular Cell, 68(1):104-117. (IF: 16.1)
– Highlighted in Nature Reviews Genetics, doi:10.1038/nrg.2017.90, 2017
– Highlighted in Nature Reviews Molecular Cell Biology, doi:10.1038/nrm.2017.111, 2017
Norris DM†, Yang P†, Krycer JR, Fazakerley DJ, James DE, Burchfield JG (2017). Journal of Cell Science, 130:2757-2766. (IF: 4.9)
Yang P*, Oldfield A, Kim T, Yang A, Yang J, Ho J* (2017). Bioinformatics, 33(13):1916-1920. (IF: 9.9)
Zheng X, Yang P#, Lackford B, Bennett B, Wang L, Li H, Wang Y, Miao Y, Foley J, Fargo D, Jin Y, Williams C, Jothi R, Hu G (2016). Stem Cell Reports, 7(5), 897-910. (IF: 6.6)
Minard A, Tan S, Yang P#, Fazakerley D, Domanova W, Parker B, Humphrey S, Jothi R, Stöckli J, James D (2016). Cell Reports, 17(1):29-36. (IF: 8.8)
Yang P, Patrick E, Humphrey SJ, Ghazanfar S, Jothi R, James DE, Yang YH (2016). Proteomics, 16(13):1868-1871. (IF: 3.5)
Yang P*, Humphrey SJ, James DE, Yang YH, Jothi R* (2016). Bioinformatics, 32(2):252-259. (IF: 9.9)
Lu C, Wang J, Zhang Z, Yang P, Yu G (2016). Computational Biology and Chemistry, 65:203-211. (IF: 1.8)
Domanova W, Krycer J, Chaudhuri R, Yang P, Vafaee F, Fazakerley D, Humphrey S, James D, Kuncic Z, (2016). PLoS One, 11(6):e0157763. (IF: 3.2)
Yang P*, Zheng X, Jayaswal V, Hu G, Yang YH, Jothi R* (2015). PLoS Computational Biology, 11(8):e1004403. (IF: 5.3)
Hoffman N, Parker B, Chaudhuri R, Fisher-Wellman K, Kleinert M, Humphrey S, Yang P, Holliday M, Trefely S, Fazakerley D, Stockli J, Burchfield J, Jensen T, Jothi R, Kiens B, Wojtaszewski J, Richter E, James DE (2015). Cell Metabolism, 22(5):922-935. (IF: 24.3)
– Recommended by Faculty of 1000 Biology
Pathania R, Ramachandran S, Elangovan S, Padia R, Yang P#, Cinghu S, Veeranan-Karmegam R, Fulzele S, Pei L, Chang C-S, Choi J-H, Shi H, Manicassamy S, Prasad PD, Sharma S, Ganapathy V, Jothi R, Thangaraju M (2015). Nature Communications, 6:6910. (IF: 13.6)
– Recommended by Faculty of 1000 Biology
Oldfield AJ†, Yang P†, Conway AE, Cinghu S, Freudenberg JM, Yellaboina S, Jothi R (2014). Molecular Cell, 55(5):708-722. (IF: 16.1)
Yang P†, Patrick E†, Tan SX, Fazakerley DJ, Burchfield J, Gribben C, Prior MJ, James DE, Yang YH* (2014). Bioinformatics, 30(6):808-814. (IF: 9.9)
Ma X, Yang P#, Kaplan WH, Lee BH, Wu LE, Yang YH, Yasunaga M, Sato K, Chisholm DJ, James DE (2014). Molecular and Cellular Biology, 34(19):3607-3617. (IF: 4.0)
Lackford B, Yao C, Charles GM, Weng L, Zheng X, Choi E, Xie X, Wan J, Xing Y, Freudenberg JM, Yang P, Jothi R, Hu G, Shi Y (2014). EMBO Journal, 33(8):878-889. (IF: 10.4)
Yang P*, Yoo PD, Fernando J, Zhou BB, Zhang Z, Zomaya AY (2014). IEEE Transactions on Cybernetics, 44(3):445-455. (IF: 10.1)
Humphrey SJ, Yang G, Yang P#, Fazakerley DJ, Stockli J, Yang YH, James DE (2013). Cell Metabolism, 17(6):1009-1020. (IF: 24.3)
Yang P, Humphrey SJ, Fazakerley DJ, Prior MJ, Yang G, James DE, Yang YH* (2012). Journal of Proteome Research, 11(5):3035-3045. (IF: 3.9)
Yang P*, Ma J, Wang P, Zhu Y, Zhou BB, Yang YH* (2012). IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(5):1273-1280. (IF: 2.7)
Wang P, Yang P, Yang YH (2012). Bioinformatics, 28(10):1404-1405. (IF: 9.9)
Yang P†,*, Ho JWK†, Yang YH, Zhou BB* (2011). BMC Bioinformatics, 12:S10. (IF: 3.2)
Yang P*, Ho JWK, Zomaya AY, Zhou BB* (2010). BMC Bioinformatics, 11:524. (IF: 3.2)
Wang P, Yang P, Arthur J, Yang YH (2010). Bioinformatics, 26(18):2242-2249. (IF: 9.9)
Yoo PD, Ho YS, Ng J, Charleston M, Saksena NK, Yang P, Zomaya AY (2010). BMC Genomics, 11:S4. (IF: 4.1)
Yang P*, Yang YH, Zhou BB, Zomaya AY (2010). Current Bioinformatics, 5(4):296-308. (IF: 1.2)
Yang P*, Zhang Z, Zhou BB, Zomaya AY (2010). Neurocomputing, 73:2317-2331. (IF: 4.0)
Yang P*, Zhou BB, Zhang Z, Zomaya AY (2010). BMC Bioinformatics, 11:S5. (IF: 3.2)
Yang P*, Xu L, Zhou BB, Zhang Z, Zomaya AY (2009). BMC Genomics, 10:S34. (IF: 4.1)
Yang P*, Zhang Z* (2009). IEEE Intelligent Informatics Bulletin, 10:24-32.
Zhang Z, Yang P, Wu X, Zhang C (2009). IEEE Intelligent Systems, 24(5):53-63. (IF: 4.0)
Zhang Z*, Yang P* (2008). IEEE Intelligent Informatics Bulletin, 9:18-24.
Tang T, Wu H, Bao W, Yang P, Yuan D, Zhou B (2020) New parallel algorithms for all pairwise computation on large HPC clusters. Proceeding of the 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE, 196-201.
Yang P, Liu W, Yang J (2017) Positive unlabeled learning via wrapper-based adaptive sampling. Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI). 3273-3279.
Yang P, Liu W, Zhou BB, Chawla S, Zomaya AY (2013) Ensemble-based wrapper methods for feature selection and class imbalance learning. Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Lecture Notes in Artificial Intelligence 7818, Springer, 544-555.
Yang P, Zhang Z, Zhou BB, Zomaya AY (2011) Sample subsets optimization for classifying imbalanced biological data. Proceedings of the 15th Pacific- Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Lecture Notes in Artificial Intelligence 6635, Springer, 333-344.
Li L, Yang P, Qu L, Zhang Z, Cheng P (2010) Genetic algorithm-based multi-objective optimisation for QoS-aware web services composition. Proceedings of the 4th International Conference on Knowledge Science, Engineering and Management (KSEM). Lecture Notes in Artificial Intelligence 6291, Springer, 549-554.
Yang P, Tao L, Xu L, Zhang Z (2009) Multiagent framework for bio-data mining. Proceedings of the Fourth International Conference on Rough Set and Knowledge Technology (RSKT). Lecture Notes in Computer Science 5589, Springer, 200-207.
Yang P, Zhang Z (2008) A clustering based hybrid system for mass spectrometry data analysis. Proceedings of Pattern Recognition in Bioinformatics (PRIB). Lecture Notes in Bioinformatics 5265, Springer, 98-109.
Yang P, Zhang Z (2008) A hybrid approach to selecting susceptible single nucleotide polymorphisms for complex disease analysis. Proceedings of BioMedical and Engineering Informatics (BMEI). IEEE, 214-218.
Yang P, Zhang Z (2007) Hybrid methods to select informative gene sets in microarray data classification. Proceedings of the 20th Australian Joint Conference on Artificial Intelligence (AI). Lecture Notes in Artificial Intelligence 4830, Springer, 811-815.