Lukas Folkman
Lukas Folkman

Lukas Folkman

Lecturer in Information Technology

machine learning  |  data science  |  bioinformatics

Faculty of Science and Engineering
Southern Cross 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 in the Machine Learning & Computational Biology Lab, ETH Zurich. 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 bioinformatician and 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 I worked as a Blue Economy Cooperative Research Centre research fellow at the Coastal and Marine Research Centre, developing computer vision methods for marine wildlife monitoring and precision aquaculture. Since 2025, I am a Lecturer (Assistant Professor) in Information Technology at Faculty of Science and Engineering, Southern Cross University, Australia.

Research

  • machine learning for the life sciences
  • computer vision for precision aquaculture and ecology
  • bioinformatics and biomedical data science

Profiles

Projects

Publications

# equal contributions    * senior author(s)

  1. 1.
    Folkman L, Vo QLK, Johnston C, Stantic B, Pitt KA (2026)
    A computer vision method to estimate ventilation rate of Atlantic salmon in sea fish farms
    Aquacultural Engineering, 112, 102645
    📄 [Open access]
  2. 2.
    Folkman L, Pitt KA, Strickland JK, Smark A, Johnston C, Albinsson ME, Huynh C, Stantic B (2026)
    Domain adaptation and computer vision approaches for robust detection of jellyfish in aquaculture
    Aquaculture International, 34, 32
    📄 [Open access]
  3. 3.
    Vo QLK, Pitt KA, Johnston C, Kennedy B, Folkman* L (2025)
    Computer vision detects an association between gross gill score and ventilation rates in farmed Atlantic salmon (Salmo salar)
    Journal of Fish Diseases, e70055
    📄 [Open access]
  4. 4.
    Folkman L, Pitt KA, Stantic B (2025)
    A data-centric framework for combating domain shift in underwater object detection with image enhancement
    Applied Intelligence 55, 272
    📄 [Open access] [GitHub]
  5. 5.
    Traxler# P, Reichl# S, Folkman L, …, Farlik* M, Bock* C (2025)
    Integrated time-series analysis and high-content CRISPR screening delineate the dynamics of macrophage immune regulation
    Cell Systems 16, 101346
    📄 [Open access] [Press release] [Website] [GitHub]
  6. 6.
    Teufel LU, Matzaraki V, Folkman L, ..., Arts RJW (2025)
    Interleukin-38 is a negative regulator of trained immunity: A retrospective multi-omics study
    iScience, 28(11), 113758
    📄 [Open access]
  7. 7.
    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
  8. 8.
    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]
  9. 9.
    Turina# P, Cortivo# GD, …, Folkman L, …, Dell'Orco* D & Capriotti* E (2025)
    Assessing the predicted impact of single amino acid substitutions in calmodulin for CAGI6 challenges
    Human Genetics 144, 113–125
    📄 [Article] [PubMed]
  10. 10.
    Turina# P, Petrosino# M, …, Folkman L, …, Chiaraluce* R, Consalvi* V & Capriotti* E (2025)
    Assessing the predicted impact of single amino acid substitution in MAPK proteins for CAGI6 challenges
    Human Genetics 144, 265–280
    📄 [Article] [PubMed]
  11. 11.
    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]
  12. 12.
    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]
  13. 13.
    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]
  14. 14.
    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]
  15. 15.
    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]
  16. 16.
    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
  17. 17.
    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]
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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]
  21. 21.
    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]
  22. 22.
    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]
  23. 23.
    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]
  24. 24.
    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]
  25. 25.
    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]