Publications
2024
W Banzhaf, T Hu, and G Ochoa (2024): How the combinatorics of neutral space leads genetic programming to discover simple solutions, Genetic Programming Theory and Practice XX, Springer, Chapter 4, pp. 65-86 [pdf]
2023
R Zhou and T Hu (2023): Evolutionary approaches to explainable machine learning, Handbook of Evolutionary Machine Learning, Springer, Chapter 16, pp. 487-506 [pdf]
T Hu (2023): Genetic programming for interpretable and explainable machine learning, Genetic Programming Theory and Practice XIX, Chapter 4, pp. 81-90 [pdf]
Z Sha, Y Chen, and T Hu (2023): NSPA: characterizing the disease association of multiple genetic interactions at single-subject resolution, Bioinformatics Advances, 3(1): vbad010
Z Dong, Y Chen, TS Tricco, C Li, and T Hu (2023): Ego-aware graph neural network, IEEE Transactions on Network Science and Engineering
R Zhou and T Hu (2023): Evolving better initialization for neutral networks with pruning. Proceedings of the 32nd Genetic and Evolutionary Computation Conference (GECCO), pp. 703-796 [pdf]
T Hu, G Ochoa, W Banzhaf (2023): Phenotype search trajectory networks for linear genetic programming. Proceedings of the 26th European Conference on Genetic Programming (EuroGP), Lecture Notes in Computer Science, 13986:52-67 [pdf]
2022
C Little, S Choudhury, T Hu, and K Salomaa (2022): Comparison of genetic operators for the multiobjective pickup and delivery problem. Mathematics, 10(22): 4308
J Robertson, C Stinson, and T Hu (2022): A bio-inspired framework for machine bias interpretation. Proceedings of the 5th AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), pp. 588-598 [pdf]
KL Nickerson, T Tricco, A Kolokolova, F Shoeleh, C Robertson, J Hawkin, and T Hu (2022): Banksformer: A deep generative model for synthetic transaction sequences. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) [pdf]
A Altabaa, D Huang, C Byles-Ho, H Khatib, F Sosa, and T Hu (2022): geneDRAGNN: gene disease prioritization using graph neural networks. Proceedings of the 19th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1-10 [pdf] (Best Student Paper Award)
Z Sha, Y Chen, and T Hu (2022): Genetic heterogeneity analysis using genetic algorithm and network science. Proceedings of the 31st Genetic and Evolutionary Computation Conference (GECCO), pp. 763-766 [pdf]
J Zhang and T Hu (2022): Regulatory genotype-to-phenotype mappings improve evolvability in genetic programming. Proceedings of the 31st Genetic and Evolutionary Computation Conference (GECCO), pp. 623-626 [pdf]
R Zhou, C Muise, and T Hu (2022): Permutation-invariant representation of neural networks with neuron embedding. Proceedings of the 25th European Conference on Genetic Programming (EuroGP), Lecture Notes in Computer Science, 12332:294-338 [pdf]
KL Nickerson, A Kolokolova, and T Hu (2022): Creating diverse ensembles for classification with genetic programming. Proceedings of the 25th European Conference on Genetic Programming (EuroGP), Lecture Notes in Computer Science, 12332:212-227 [pdf]
Y Zhang, Z Li, M Gao, Y Lang, M Zhang, S Likhodii, T Hu, M Zhang, and W Zhang (2022): Magnetic solidphase extraction method with modified magnetic ferroferric oxide nanoparticles in a deep eutectic solvent and high-performance liquid chromatography. Journal of Chromatography A, 1679: 463395
2021
C Sha, M Cuperlovic-Culf, and T Hu (2021): SMILE: Systems metabolomics using interpretable learning and evolution. BMC Bioinformatics, 22:284
S Kafaie, L Xu, and T Hu (2021): Statistical methods with exhaustive search in the identification of gene-gene interactions for colorectal cancer. Genetic Epidemiology, 45:222-234 [pdf]
Z Dong, Y Chen, TS Tricco, C Li, and T Hu (2021): Hunting for vital nodes in complex networks using local information. Scientific Reports, 11:9190 [pdf]
M Contreras, E Burchard, C Eng, S Huntsman, D Hu, K Keys, A Mak, J Elhawary, N Thakur, E Lee, LA Samedy, J Magana, CG Page, A Zeiger, T Hu, A Neophytou, S Oh, and MJ White (2021): Native American ancestry and air pollution interact to impact bronchodilator response in Puerto Rican children with asthma. Ethnicity & Diseases, 31(1):77-88
A Vega-Beyhart, M Iruarrizaga, A Pane, G Gurcia-Eguren, O Giro, L Boswell, G Aranda, V Flores, G Casals, C Alonso, M Mora, I Halperin, F Carmona, J Ensenat, O Vidal, T Hu, G Rojo, R Gomis, and FA Hanzu (2021): Endogenous cortisol excess confers a unique lipid signiture and metabolic network. Journal of Molecular Medicine
Z Sha, Y Chen, and T Hu (2021): Feature selection for polygenic risk scores using genetic algorithm and network science. Proceedings of the annual IEEE Congress on Evolutionary Computation (CEC), [pdf]
J Robertson and T Hu (2021): An evolutionary approach to interpretable learning. Proceedings of the 30th Genetic and Evolutionary Computation Conference (GECCO), [pdf]
KL Nickerson and T Hu (2021): Principled quality diversity for ensemble classifiers using MAP-Elites. Proceedings of the 30th Genetic and Evolutionary Computation Conference (GECCO), [pdf]
T Hu, N Loureno, E Medvet, and F Divina, editors (2021): Proceedings of the 24th European Conference on Genetic Programming (EuroGP), Lecture Notes in Computer Science, vol. 12691
2020
Y Zhang, Y Chen, and T Hu (2020): PANDA: prioritization of Autism-genes using network-based deep-learning approach. Genetic Epidemiology, 44(4):382-394 [pdf]
T Hu, M Tomassini, and W Banzhaf (2020): A network perspective on genotype-phenotype mapping in genetic programming. Genetic Programming and Evolvable Machines, 21:375-397 [pdf]
CA Costello, T Hu, M Liu, W Zhang, A Furey, Z Fan, P Rahman, EW Randell, and G Zhai (2020): Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients. Metabolomics, 16:61 [pdf]
J Magana, MG Contreras, KL Keys, O Risse-Adams, PC Goddard, AM Zeiger, ACY Mak, JR Elhawary, L Samedy-Bates, E Lee, N Thakur, D Hu, C Eng, S Salazar, S Huntsman, T Hu, EG Burchard, MJ White (2020): An epistatic interaction between pre-natal smoke exposure and socioeconomic status has a significant impact on bronchodilator drug response in African American youth with asthma. BioData Mining, 13:7
T Hu (2020): Can genetic programming perform explainable machine learning for bioinformatics? Genetic Programming Theory and Practice XVII, chapter 4, pp.63-77 [pdf]
CA Costello, T Hu, M Liu, W Zhang, A Furey, Z Fan, P Rahman, EW Randell, and G Zhai (2020): Metabolomics signature for non‐responders to total joint replacement surgery in primary osteoarthritis patients: The Newfoundland Osteoarthritis Study. Journal of Orthopaedic Research, 38(4):793-802
Y Wang, G Wang, R Jing, T Hu, S Likhodii, G Sun, E Randell, G Jia, T Yu, and W Zhang (2020): Metabolomics analysis of human plasma metabolites reveals the age- and sex-specific associations. Journal of Liquid Chromatography & Related Technologies, 43(5-6):185-194
T Hu, M Nicolau, and L Sekanina (2020): Editorial: Special issue on highlights of genetic programming 2019 events. Genetic Programming and Evolvable Machines, 21:283-285
Z Dong, Y Chen, TS Tricco, C Li, and T Hu (2020): Practical strategy of acquaintance immunization without contact tracing. Proceedings of the 13th IEEE International Symposium on Social Computing and Networking (SocialCom)
Y Zhang, Y Chen, and T Hu (2020): Classification of autism genes using network science and linear genetic programming. Proceedings of the 23rd European Conference on Genetic Programming (EuroGP), Lecture Notes in Computer Science, 12102:279-294 [pdf]
S Geng and T Hu (2020): Sports games modeling and prediction using genetic programming, Proceedings of 2020 IEEE Congress on Evolutionary Computation (CEC) [pdf]
T Hu, N Loureno, E Medvet, and F Divina, editors (2020): Proceedings of the 23rd European Conference on Genetic Programming (EuroGP), Lecture Notes in Computer Science, vol. 12101
2019
W Banzhaf and T Hu (2019): Evolutionary Computation. Oxford Bibliographies, DOI: 10.1093/OBO/9780199941728-0122
S Kafaie, Y Chen, and T Hu (2019): A network approach to prioritizing susceptibility genes for genome-wide association studies. Genetic Epidemiology, 43(5):477-491 [pdf]
MY Lee and T Hu (2019): Computational methods for the discovery of metabolic markers of complex traits. Metabolites, 9(4): 66
SM Almasi and T Hu (2019): Measuring the importance of vertices in the weighted human disease network. PLoS ONE, 14(3): e0205936
F Wang, H Zhang, T Hu, XL Shen, and Y Li (2019): An adaptive weight vector guided evolutionary algorithm for preference-based multi-objective optimization. Swarm and Evolutionary Computation, 49:220-233
W Zhang, X Cui, X Yu, G Sun, T Hu, S Likhodii, J Zhang, E Randell, X Gao, and Z Fan (2019): A differential metabolomic network analysis of menopausal status. PLoS ONE, 14(9):e0222353
MZ Ali, MNSK Shabbir, X Liang, Y Zhang, and T Hu (2019): Machine learning based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals. IEEE Transactions on Industry Applications, 55(3):2378-2391
T Hu and L Sekanina (2019): EuroGP 2019 panel discussion: what is the killer application of GP? SIGEVOlution, 12(2):3-7
T Hu, W Banzhaf, and M Tomassini (2019): Complex network analysis of a genetic programming phenotype network. Proceedings of the 22nd European Conference on Genetic Programming (EuroGP), Lecture Notes in Computer Science, 11451:49-63 [pdf]
Y Zhang, T Hu, X Liang, MZ Ali, and MNSK Shabbir (2019): Fault detection and classification for induction motors using genetic programming. Proceedings of the 22nd European Conference on Genetic Programming (EuroGP), Lecture Notes in Computer Science, 11451:178-193 [pdf]
L Sekanina, T Hu, N Lourenco, H Richter, and P Garcia-Sanchez, editors (2019): Proceedings of the 22nd European Conference on Genetic Programming (EuroGP), Lecture Notes in Computer Science, vol. 11451
2018
T Hu, K Oksanen, W Zhang, E Randell, A Furey, G Sun, and G Zhai (2018): An evolutionary learning and network approach to identifying key metabolites for osteoarthritis. PLoS Computational Biology, 14(3):e1005986
F Dorani, T Hu, MO Woods, and G Zhai (2018): Ensemble learning for detecting gene-gene interactions in colorectal cancer. PeerJ, 6:e5854
Z Li, Y Zhang, T Hu, S Likhodii, G Sun, G Zhai, Z Fan, C Xuan, and W Zhang (2018): Differential metabolomics analysis allows characterization of diversity of metabolite networks between males and females. PLoS ONE, 13(11):e0207775
T Hu and W Banzhaf (2018): Neutrality, robustness, and evolvability in genetic programming. Genetic Programming Theory and Practice XIV (Springer), chapter 7, pp.101-117 [pdf]
T Hu, K Oksanen, W Zhang, E Randell, A Furey, and G Zhai (2018): Analyzing feature importance for metabolomics using genetic programming. Proceedings of the 21st European Conference on Genetic Programming (EuroGP), Lecture Notes in Computer Science, 10781:68-83 (Nominated Best Paper Award)
KL Nickerson, Y Chen, F Wang, and T Hu (2018): Measuring evolvability and accessibility using the Hyperlink-Induced Topic Search algorithm. Proceedings of the 27th Genetic and Evolutionary Computation Conference (GECCO), pp.1175-1182
F Dorani and T Hu (2018): Feature selection for detecting gene-gene interactions in genome-wide association studies. Proceedings of 21st European Conference on the Applications of Evolutionary Computation (EvoApplications), Lecture Notes in Computer Science, 10784:33-46
MZ Ali, MNSK Shabbier, X Liang, Y Zhang, and T Hu (2018): Experimental investigation of machine learning based fault diagnosis for induction motors. Proceedings of 2018 IEEE Industry Applications Society Annual Meeting (IAS), pp.1-14
S Ye, Y Chen, T Hu, JY Son, Q Li, and A Farrokhtala (2018): Observing friendship patterns through smart phone radios. Proceedings of the 31st IEEE Canadian Conference on Electrical & Computer Engineering (CCECE)
A Farrokhtala, Y Chen, T Hu, and S Ye (2018): Toward understanding hidden patterns in human mobility using Wi-Fi. Proceedings of the 31st IEEE Canadian Conference on Electrical & Computer Engineering (CCECE)
T Hu, F Wang, H Li, and Q Wang, editors (2018): Proceedings of the 18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2018), Lecture Notes in Computer Science, vol. 11338
2017
T Hu and JH Moore (2017): A network-guided MDR approach to searching for high-order genetic interactions. Multifactor Dimensionality Reduction Book (Cambridge University Press)
K Oksanen and T Hu (2017): Lexicase selection promotes effective search and behavioural diversity of solutions in linear genetic programming. Proceedings of the 2017 IEEE Congress on Evolutionary Computation (CEC), pp.169-176
EvoApplications chairs (including T Hu as chair of EvoBio), editors (2017): Applications of Evolutionary Computation, Proceedings of the 20th European Conference on the Applications of Evolutionary Computation (EvoApplications), Lecture Notes in Computer Science, vol. 10199 & 10200
2016
T Hu and W Banzhaf (2016): Quantitative analysis of evolvability using vertex centralities in phenotype networks. Proceedings of the 25th Genetic and Evolutionary Computation Conference (GECCO), pp.733-740 (Nominated Best Paper Award) [pdf]
T Hu, W Zhang, Z Fan, G Sun, S Likhodi, E Randell, and G Zhai (2016): Metabolomics differential correlation network analysis of Osteoarthritis. Proceedings of the Pacific Symposium on Biocomputing (PSB), 21:120-131
S Ye, Y Chen, and T Hu (2016): Evolutionary algorithmic deployment of radio beacons for indoor positioning. Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC), pp.2829-2835
EvoApplications chairs (including T Hu as chair of EvoBio), editors (2016): Applications of Evolutionary Computation, Proceedings of the 19th European Conference on the Applications of Evolutionary Computation (EvoApplications), Lecture Notes in Computer Science, vol. 9597 & 9598