Lukas Folkman
Research Fellow
machine learning | data science | environmental modelling | computational biology

Coastal and Marine Research Centre &
Institute for Integrated and Intelligent Systems
Griffith University, Australia

About me

I am a computer scientist with research interests in developing machine learning methods for the life sciences. I earned my PhD from the Institute for Integrated and Intelligent Systems, Griffith University (Australia) in 2015, with a thesis on the prediction of stability and functional changes caused by genomic variants. Following this, I moved to Switzerland to continue my training as a postdoctoral fellow at ETH Zurich in the Machine Learning & Computational Biology Lab. There, my research focused on machine learning methods for precision medicine. In 2018, I moved to Austria to join CeMM Research Center for Molecular Medicine to develop machine learning methods for single-cell RNA-seq data analysis, for which I received European Commission's Marie Skłodowska-Curie Actions Postdoctoral Fellowship. While at CeMM, and later at the Institute of Artificial Intelligence, Medical University of Vienna, I was also the lead data scientist for a project exploring epigenetic-immunological interplay and developed graph neural networks for virtual screening of small molecules. In 2022, I returned to Griffith University, where my research focuses on computer vision methods for marine wildlife monitoring and precision aquaculture at the Coastal and Marine Research Centre.

Contact

Research

• applied machine learning and data science
• computer vision for wildlife monitoring and ecology
• bioinformatics for high-dimensional molecular data

Profiles

• Google Scholar
• ORCID
• ResearchGate
• Twitter
• LinkedIn
• GitHub

Publications

^# denotes equal contributions

• Moorlag# SJCFM, Folkman# L, ter Horst# R, Krausgruber# T, Barreca B, Schuster LC, Fife V, Matzaraki V, Li W, Reichl S, Mourits VP, Koeken VACM, de Bree LCJ, Dijkstra H, Lemmers H, van Cranenbroek B, van Rijssen E, Koenen HJPM, Joosten I, Xu C,Li Y, Joosten LAB, van Crevel R, Netea* MG & Bock* C (2024)
  Multi-omics analysis of innate and adaptive responses to BCG vaccination reveals epigenetic cell states that predict trained immunity
  Immunity 57(1), 171–187
  📄 [Open access] [PubMed] [Press release] [Website] [GitHub]
  ★ Selected for Nature Research Highlights
• Teufel LU, Matzaraki V, Folkman L, ter Horst R, Moorlag SJCFM, Mulders-Manders CM, Netea MG, Krausgruber T, Joosten LAB, Arts RJW (2024)
  Insights into the multifaceted role of interleukin-37 on human immune cell regulation
  Clinical Immunology 268(1), 110368
  📄 [Open access] [PubMed]
  
• The Critical Assessment of Genome Interpretation Consortium (2024)
  CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods
  Genome Biology 25, 53
  📄 [Open access] [PubMed]
• de Bree LCJ, Mourits VP, Koeken VACM, Moorlag SJCFM, Janssen R, Folkman L, Barreca D, Krausgruber T, Fife-Gernedl V, Novakovic B, Arts RJW, Dijkstra H, Lemmers H, Bock C, Joosten LAB, van Crevel R, Benn CS & Netea MG (2020)
  Circadian rhythm influences induction of trained immunity by BCG vaccination
  The Journal of Clinical Investigation 130(10), 5603–5617
  📄 [Open access] [PubMed]
• Pejaver V, Babbi G, Casadio R, Folkman L, Katsonis P, Kundu K, Lichtarge O, Martelli PL, Miller M, Moult J, Pal LR, Savojardo C, Yin Y, Zhou Y, Radivojac P & Bromberg Y (2019)
  Assessment of methods for predicting the effects of PTEN and TPMT protein variants
  Human Mutation 40(9), 1495–1506
  📄 [Article] [Open access via PMC] [PubMed] [CAGI5]
• Savojardo C, Petrosino M, Babbi G, Bovo S, Corbi‐Verge C, Casadio R, Fariselli P, Folkman L, Garg A, Karimi M, Katsonis P, Kim PM, Lichtarge O, Martelli PL, Pasquo A, Pal D, Shen Y, Strokach AV, Turina P, Zhou Y, Andreoletti G, Brenner S, Chiaraluce R, Consalvi V & Capriotti E (2019)
  Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge
  Human Mutation 40(9), 1392–1399
  📄 [Article] [Open access via PMC] [PubMed] [CAGI5]
• Clark WT, Kasak L, Bakolitsa C, Hu Z, Andreoletti G, Babbi G, Bromberg Y, Casadio R, Dunbrack R, Folkman L, Ford CT, Jones D, Katsonis P, Kundu K, Lichtarge O, Martelli PL, Mooney SD, Nodzak C, Pal LR, Radivojac P, Savojardo C, Shi X, Zhou Y, Uppal A, Xu Q, Yin Y, Pejaver V, Wang M, Wei L, Moult J, Yu GK, Brenner SE & LeBowitz JH (2019)
  Assessment of predicted enzymatic activity of alpha‐N‐acetylglucosaminidase (NAGLU) variants of unknown significance for CAGI 2016
  Human Mutation 40(9), 1519–1529
  📄 [Article] [Open access via PMC] [PubMed] [CAGI4]
• He^# X, Folkman^# L & Borgwardt K (2018)
  Kernelized rank learning for personalized drug recommendation
  Bioinformatics 34(16), 2808–2816
  📄 [Open access] [Poster] [PubMed] [GitHub] [Datasets]
  ★ ISMB/ECCB 2017 and [BC]² 2017 best poster awards
• Livingstone^# M, Folkman^# L, Yang Y, Zhang P, Mort M, Cooper DN, Liu Y, Stantic B & Zhou Y (2017)
  Investigating DNA-, RNA-, and protein-based features as a means to discriminate pathogenic synonymous variants
  Human Mutation 38(10), 1336–1347
  📄 [Article] [Preprint] [PubMed] [Web server and datasets] [Singularity container]
• Folkman L, Stantic B, Sattar A & Zhou Y (2016)
  EASE-MM: sequence-based prediction of mutation-induced stability changes with feature-based multiple models
  Journal of Molecular Biology, 428(6), 1394–1405
  📄 [Article] [Preprint] [PubMed] [Web server and datasets] [Singularity container]
  ★ Ranked 3rd in the frataxin stability prediction during the Critical Assessment of Genome Interpretation 2018 competition
• Folkman L, Yang Y, Li Z, Stantic B, Sattar B, Mort M, Cooper DN, Liu Y & Zhou Y (2015)
  DDIG-in: detecting disease-causing genetic variations due to frameshifting indels and nonsense mutations by sequence and structural properties at nucleotide and protein levels
  Bioinformatics 31(10), 1599–1606
  📄 [Open access] [PubMed] [Web server and datasets] [Singularity container]
  ★ Winner of the Three Minutes Thesis Competition 2014 at the School of Information and Communication Technology, Griffith University
• Folkman L, Stantic B & Sattar A (2014)
  Feature-based multiple models improve classification of mutation-induced stability changes
  BMC Genomics 15(Suppl 4), S6
  📄 [Open access] [PubMed]
• Folkman L, Stantic B & Sattar A (2014)
  Towards sequence-based prediction of mutation-induced stability changes in unseen non-homologous proteins
  BMC Genomics 15(Suppl 1), S4
  📄 [Open access] [PubMed]
• Folkman L, Stantic B & Sattar A (2013)
  Sequence-only evolutionary and predicted structural features for the prediction of stability changes in protein mutants
  BMC Bioinformatics 14(Suppl 2), S6
  📄 [Open access] [PubMed]
• Higgs T, Folkman L & Stantic B (2013)
  Combining protein fragment feature-based resampling and local optimisation
  in ‘IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB)’, Vol. 7986 of LNCS, pp. 114–125, Springer
  📄 [Article]
• Folkman L, Pullan W & Stantic B (2011)
  Generic parallel genetic algorithm framework for protein optimisation
  in ‘Algorithms and architectures for parallel processing (ICA3PP)’, Vol. 7017 of LNCS, pp. 64–73, Springer
  📄 [Article]
• Stetsko A, Folkman L, & Matyas V (2010)
  Neighbor-based intrusion detection in wireless sensor networks
  in ‘Wireless and Mobile Communications (ICWMC)’, pp. 420–42, IEEE
  📄 [Article]