Finding cures for children's genetic diseases


Full List in Reverse Chronological Order
Co-first author
* Corresponding/Co-corresponding author
# Lead bioinformatician
  1. 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) NF-Y controls fidelity of transcription initiation at gene promoters through maintenance of the nucleosome-depleted region. Nature Communications, accepted.
  2. Yang P*, Humphrey S*, Cinghu S, Pathania R, Oldfield A, Kumar D, Perera D, Yang J, James D, Mann M, Jothi R* (2019) Multi-omic profiling reveals dynamics of the phased progression of pluripotency. Cell Systems, 8(5), 427-445.
  3. Lin Y, Ghazanfar S, Wang K, Gagnon-Bartsch J, Lo K, Su X, Han Z, Ormerod J, Speed T, Yang P*, Yang J* (2019) scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. Proceedings of the National Academy of Sciences of the United States of America, 116(20), 9775-9784.
  4. Kim T, Chen I, Parker B, Humphrey S, Crossett B, Cordwell S, Yang P*, Yang J* (2019) QCMAP: An interactive web-tool for performance diagnosis and prediction of LC-MS Systems. Proteomics,
  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 (2018) A proteome- and lipidome-wide systems genetic analysis of hepatic lipid metabolism. Nature, 567, 187-193 [IF 44.9]
  6. Kim T, Chen I, Lin Y, Wang A, Yang J, Yang P* (2018) Impact of similarity metrics on single-cell RNA-seq data clustering. Briefings in Bioinformatics 10.1093/bib/bby076. [IF 7.1]
  7. Ridder M, Klein K, Yang J, Yang P, Lagopoulos J, Hickie I, Bennett M, Kim J (2018) An uncertainty visual analytics framework for fMRI functional connectivity. Neuroinformatics, 17(2), 211-223 [IF 3.5]
  8. Yang P*, Ormerod J, Liu W, Ma C, Zomaya A, Yang J (2018) AdaSampling for positive-unlabeled and label noise learning with bioinformatics applications. IEEE Transactions on Cybernetics, 49(5), 1932-1943. [IF 8.8]
  9. 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) Mitochondrial CoQ deficiency is a common driver of mitochondrial oxidants and insulin resistance. eLIFE, 7, e3211. [IF 8.4]
  10. Cinghu S, Yang P, Kosak J, Conway A, Kumar D, Oldfield A, Adelman K, Jothi R (2017) Intragenic enhancers attenuate host gene expression. Molecular Cell, 68(1), 104-117. [IF 14.4]
    • 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
  11. Norris DM, Yang P, Krycer JR, Fazakerley DJ, James DE, Burchfield JG (2017) An improved Akt reporter reveals intra- and inter-cellular heterogeneity and oscillations in signal transduction. Journal of Cell Science, 130, 2757-2766. [IF 5.2]
  12. 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.
  13. Yang P*, Oldfield A, Kim T, Yang A, Yang J, Ho J* (2017) Integrative analysis identifies co-dependent gene expression regulation of BRG1 and CHD7 at distal regulatory sites in embryonic stem cells. Bioinformatics, 33(13), 1916-1920. [IF 8.0]
  14. 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) CNOT3-dependent mRNA deadenylation safeguards the pluripotent state. Stem Cell Reports, 7(5), 897-910. [IF 7.5]
  15. Minard A, Tan S, Yang P#, Fazakerley D, Domanova W, Parker B, Humphrey S, Jothi R, Stöckli J, James D (2016) mTORC1 is a major regulatory node in the FGF21 signaling network in adipocytes. Cell Reports, 17(1), 29-36. [IF 8.7]
  16. Yang P, Patrick E, Humphrey SJ, Ghazanfar S, Jothi R, James DE, Yang YH (2016) Phosphoproteomics data annotation using hypothesis driven kinase perturbation analysis. Proteomics, 16(13), 1868-1871. [IF 3.8]
  17. Yang P*, Humphrey SJ, James DE, Yang YH, Jothi R* (2016) Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data. Bioinformatics, 32(2):252-259. [IF 8.0]
  18. Lu C, Wang J, Zhang Z, Yang P, Yu G (2016). NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity. Computational Biology and Chemistry, 65, 203-211. [IF 1.4]
  19. Domanova W, Krycer J, Chaudhuri R, Yang P, Vafaee F, Fazakerley D, Humphrey S, James D, Kuncic Z, (2016). Unraveling kinase activation dynamics using kinase-substrate relationships from temporal large-scale phosphoproteomics studies. PLoS One, 11(6), e0157763. [IF 3.4]
  20. Yang P*, Zheng X, Jayaswal V, Hu G, Yang YH, Jothi R* (2015) Knowledge-based analysis for detecting key signaling events from time-series phosphoproteomics data. PLoS Computational Biology, 11(8):e1004403. [IF 5.0]
  21. 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) Global phosphoproteomic analysis of human skeletal muscle reveals a network of exercise-regulated kinases and AMPK substrates. Cell Metabolism, 22(5):922-935. [IF 19.8]
  22. 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) DNMT1 is essential for mammary and cancer stem cell maintenance and tumorigenesis. Nature Communications, 6:6910. [IF 13.1]
  23. Oldfield AJ, Yang P, Conway AE, Cinghu S, Freudenberg JM, Yellaboina S, Jothi R (2014) Histone-fold domain protein NF-Y promotes chromatin accessibility for cell type-specific master transcription factors. Molecular Cell, 55(5):708-722. [IF 14.4]
  24. Yang P, Patrick E, Tan SX, Fazakerley DJ, Burchfield J, Gribben C, Prior MJ, James DE, Yang YH* (2014) Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway. Bioinformatics, 30(6):808-814. [IF 8.0]
  25. Ma X, Yang P#, Kaplan WH, Lee BH, Wu LE, Yang YH, Yasunaga M, Sato K, Chisholm DJ, James DE (2014) ISL1 regulates PPARγ activation and early adipogenesis via BMP4-dependent and independent mechanisms. Molecular and Cellular Biology, 34(19):3607-3617. [IF 4.8]
  26. 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) Fip1 regulates mRNA alternative polyadenylation to promote stem cell self-renewal. EMBO Journal, 33(8):878-889. [IF 9.9]
  27. Yang P*, Yoo PD, Fernando J, Zhou BB, Zhang Z, Zomaya AY (2014) Sample subset optimization techniques for imbalanced and ensemble learning problems in bioinformatics applications. IEEE Transactions on Cybernetics, 44(3):445-455. [IF 8.8]
  28. Yang P, Yang YH, Zhou BB, Zomaya AY (2013) Stability of feature selection algorithms and ensemble feature selection methods in bioinformatics. In Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data, Wiley, New Jersey, USA, 333-352
  29. Humphrey SJ, Yang G, Yang P#, Fazakerley DJ, Stockli J, Yang YH, James DE (2013) Dynamic adipocyte phosphoproteome reveals Akt directly regulates mTORC2. Cell Metabolism, 17(6):1009-1020. [IF 19.8]
  30. 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
  31. Yang P, Humphrey SJ, Fazakerley DJ, Prior MJ, Yang G, James DE, Yang YH* (2012) Re-Fraction: a machine learning approach for deterministic identification of protein homologs and splice variants in large-scale MS-based proteomics. Journal of Proteome Research, 11(5):3035-3045. [IF 4.4]
  32. Yang P*, Ma J, Wang P, Zhu Y, Zhou BB, Yang YH* (2012) Improving X!Tandem on peptide identification from mass spectrometry by self-boosted Percolator. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(5):1273-1280. [IF 1.9]
  33. Wang P, Yang P, Yang YH (2012) OCAP: an open comprehensive analysis pipeline for iTRAQ. Bioinformatics, 28(10):1404-1405. [IF 8.0]
  34. Yang P†,*, Ho JWK, Yang YH, Zhou BB* (2011) Gene-gene interaction filtering with ensemble of filters. BMC Bioinformatics, 12:S10. [IF 3.5]
  35. 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
  36. Yang P*, Yang YH, Zhou BB, Zomaya AY (2010) A review of ensemble methods in bioinformatics. Current Bioinformatics, 5(4):296-308 [IF 0.78]
  37. Yang P*, Ho JWK, Zomaya AY, Zhou BB* (2010) A genetic ensemble approach for gene-gene interaction identification. BMC Bioinformatics, 11:524. [IF 3.5]
  38. Wang P, Yang P, Arthur J, Yang YH (2010) A dynamic wavelet-based algorithm for pre-processing mass spectrometry data. Bioinformatics, 26(18):2242-2249. [IF 8.0]
  39. Yoo PD, Ho YS, Ng J, Charleston M, Saksena NK, Yang P, Zomaya AY (2010) Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles. BMC Genomics, 11:S4. [IF 4.3]
  40. Yang P*, Zhang Z, Zhou BB, Zomaya AY (2010) A clustering based hybrid system for biomarker selection and sample classification of mass spectrometry data. Neurocomputing, 73:2317-2331. [IF 3.2]
  41. Yang P*, Zhou BB, Zhang Z, Zomaya AY (2010) A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data. BMC Bioinformatics, 11:S5. [IF 3.5]
  42. 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
  43. Yang P*, Xu L, Zhou BB, Zhang Z, Zomaya AY (2009) A particle swarm based hybrid system for imbalanced medical data sampling. BMC Genomics, 10:S34. [IF 4.3]
  44. Yang P*, Zhang Z* (2009) An embedded two-layer feature selection approach for microarray data analysis. IEEE Intelligent Informatics Bulletin, 10:24-32
  45. Zhang Z, Yang P, Wu X, Zhang C (2009) An agent-based hybrid system for microarray data analysis. IEEE Intelligent Systems, 24(5):53-63. [IF 4.0]
  46. 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
  47. Zhang Z*, Yang P* (2008) An ensemble of classifiers with genetic algorithm based feature selection. IEEE Intelligent Informatics Bulletin, 9:18-24
  48. 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
  49. 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
  50. 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