A Survey of Transfer and Multitask Learning in Bioinformatics
Open accessAbstract
Machine learning and data mining have found many applications in biological domains, where we look to build predictive models based on labeled training data. However, in practice, high quality labeled data is scarce, and to label new data incurs high costs. Transfer and multitask learning offer an attractive alternative, by allowing useful knowledge to be extracted and transferred from data in auxiliary domains helps counter the lack of data problem in the target domain. In this article, we survey recent advances in transfer and multitask learning for bioinformatics applications. In particular, we survey several key bioinformatics application areas, including sequence classification, gene expression data analysis, biological network reconstruction and biomedical applications.
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Multi-task learning strategies have brought new insights to bioinformatics because compounds from related assays may share features at various feature levels, which is helpful when the data set is small.
“Indeed, MT learning strategies have brought new insights to bioinformatics since compounds from related assays may share features at various feature levels, which is extremely helpful if data set is small.”
From Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks (2018) · cites this paper as [10]
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Cites
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Self-citationSpellman, P. T., Sherlock, G. J., Zhang, M. Q., Iyer, V. R., Anders, K. R., Eisen, M. B., Brown, P. O., Botstein, D., & Futcher, B. (1998) · Molecular Biology of the Cell · doi:10.1091/mbc.9.12.3273 · Cited by 4,724 · see citing works
Activity Recognition from User-Annotated Acceleration Data
Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147 · see citing works
Boosting for transfer learning
Dai, W., Yang, Q., Xue, G.-R., & Yu, Y. H. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273521 · Cited by 1,788 · see citing works
Self-citationDomain adaptation with structural correspondence learning
Blitzer, J., McDonald, R., & Pereira, F. (2006) · Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06 · doi:10.3115/1610075.1610094 · Cited by 1,543 · see citing works
Convex multi-task feature learning
Argyriou, A., Evgeniou, T., & Pontil, M. (2008) · Machine Learning · doi:10.1007/s10994-007-5040-8 · Cited by 1,535 · see citing works
Whitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J. I., Ball, C. A., Alexander, K. E., Matese, J. C., Perou, C. M., Hurt, M. M., Brown, P. O., et al. (2002) · Molecular Biology of the Cell · doi:10.1091/mbc.02-02-0030 · Cited by 1,527 · see citing works
Raina, R., Battle, A. J., Lee, H., Packer, B., & Ng, A. Y. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273592 · Cited by 1,514 · see citing works
Regularized multi--task learning
Evgeniou, T., & Pontil, M. (2004) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1014052.1014067 · Cited by 1,473 · see citing works
Frustratingly Easy Domain AdaptationPreprint
Daumé, H. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0907.1815 · Cited by 1,471 · see citing works
Activity recognition from accelerometer data
Ravi, N., Dandekar, N., Mysore, P., & Littman, M. L. (2005) · Cited by 1,434 · see citing works
Argyriou, A., Evgeniou, T., & Pontil, M. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0010 · Cited by 1,376 · see citing works
Relational learning via collective matrix factorization
Singh, A. P., & Gordon, G. J. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1401969 · Cited by 1,237 · see citing works
Hansmann, U., Stöber, T., Merk, L., & Nicklous, M. S. (2003) · Cited by 1,168 · see citing works
Prediction of drug–target interaction networks from the integration of chemical and genomic spaces
Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., & Kanehisa, M. I. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn162 · Cited by 1,148 · see citing works
Machine learning in bioinformatics
Larrañaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J. A., Armañanzas, R., Santafé, G., Pérez, A., et al. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbk007 · Cited by 867 · see citing works
Design requirements for technologies that encourage physical activity
Consolvo, S., Everitt, K., Smith, I., & Landay, J. A. (2006) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1124772.1124840 · Cited by 801 · see citing works
Instance Weighting for Domain Adaptation in NLP
Jiang, J., & Zhai, C. (2007) · Cited by 780 · see citing works
Cross-domain video concept detection using adaptive svms
Yang, J. J., Yan, R., & Hauptmann, A. G. (2007) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1291233.1291276 · Cited by 673 · see citing works
Feature selection for high-dimensional genomic microarray data
Xing, E. P., Jordan, M. I., & Karp, R. M. (2001) · Cited by 635 · see citing works
Frustratingly Easy Domain Adaptation
Daum, H. (2007) · Cited by 605 · see citing works
Clustered Multi-Task Learning: A Convex FormulationPreprint
Jacob, L., Bach, F., & Vert, J. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0809.2085 · Cited by 417 · see citing works
Protein-ligand interaction prediction: an improved chemogenomics approach
Jacob, L., & Vert, J. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn409 · Cited by 382 · see citing works
Prediction of MHC class I binding peptides, using SVMHC
Dönnes, P., & Elofsson, A. (2002) · BMC Bioinformatics · doi:10.1186/1471-2105-3-25 · Cited by 330 · see citing works
Towards pervasive computing in health care – A literature review
Orwat, C., Graefe, A., & Faulwasser, T. (2008) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-8-26 · Cited by 232 · see citing works
Text-mining and information-retrieval services for molecular biology.
Krallinger, M., & Valencia, A. (2005) · Genome biology · doi:10.1186/gb-2005-6-7-224 · Cited by 214 · see citing works
Yang, C., He, Z., Wan, X., Yang, Q., Xue, H., & Yu, W. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn652 · Cited by 205 · see citing works
Self-citationTransfer learning for text classification
Shulman, J. M., & Ng, A. Y. (2005) · neural information processing systems · Cited by 187 · see citing works
Multi-task learning for HIV therapy screening
Bickel, S., Bogojeska, J., Lengauer, T., et al. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390164 · Cited by 181 · see citing works
Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins
Qi, Y., Taştan, Ö., Carbonell, J. G., Klein‐Seetharaman, J., & Weston, J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq394 · Cited by 151 · see citing works
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis
Schweikert, G., Rätsch, G., Widmer, C., et al. (2008) · Cited by 142 · see citing works
Efficient peptide–MHC-I binding prediction for alleles with few known binders
Jacob, L., & Vert, J. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm611 · Cited by 137 · see citing works
Cross-domain activity recognition
Zheng, V. W., Hu, D. H., & Yang, Q. (2009) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/1620545.1620554 · Cited by 133 · see citing works
Self-citationSpeaker adaptation for continuous density HMMs: a review
Woodland, P. (2001) · Cambridge University Engineering Department Publications Database · Cited by 127 · see citing works
Wan, X., Yang, C., Yang, Q., et al. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-13 · Cited by 108 · see citing works
Self-citationAn Improved Multi-task Learning Approach with Applications in Medical Diagnosis
Bi, J., Xiong, T., Yu, S., Dündar, M., & Rao, R. B. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87479-9_26 · Cited by 107 · see citing works
Finding Correlated Biclusters from Gene Expression Data
Yang, W.-H., Dai, D., & Yan, H. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.150 · Cited by 91 · see citing works
Regularized Adaptation of Discriminative Classifiers
Li, X., & Bilmes, J. A. (2006) · doi:10.1109/icassp.2006.1660001 · Cited by 81 · see citing works
Multitask Learning for Protein Subcellular Location Prediction
Xu, Q., Pan, S. J., Xue, H., & Yang, Q. (2010) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2010.22 · Cited by 79 · see citing works
Self-citationMulti-task learning for cross-platform siRNA efficacy prediction: an in-silico study
Liu, Q., Xu, Q., Zheng, V. W., Xue, H., Cao, Z., & Yang, Q. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-181 · Cited by 78 · see citing works
Self-citationLeveraging Sequence Classification by Taxonomy-Based Multitask Learning
Widmer, C., Leiva, J., Altün, Y., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12683-3_34 · Cited by 74 · see citing works
Domain adaptation for semantic role labeling in the biomedical domain
Dahlmeier, D., & Ng, H. T. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq075 · Cited by 74 · see citing works
Multi-population GWA mapping via multi-task regularized regression
Puniyani, K., Kim, S. Y., & Xing, E. P. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq191 · Cited by 72 · see citing works
Jog Falls: A Pervasive Healthcare Platform for Diabetes Management
Nachman, L., Baxi, A., Bhattacharya, S., Darera, V., Deshpande, P., Kodalapura, N., Mageshkumar, V., Rath, S. P., Shahabdeen, J., & Acharya, R. V. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12654-3_6 · Cited by 71 · see citing works
Gene ontology based transfer learning for protein subcellular localization
Mei, S., Fei, W., & Zhou, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-44 · Cited by 69 · see citing works
Indoor localization in multi-floor environments with reduced effort
Wang, H.-Y., Zheng, V. W., Zhao, J., et al. (2010) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2010.5466971 · Cited by 66 · see citing works
On-Line Handwritten Character Recognition with 3D Accelerometer
Choi, S., Lee, A. W., & Lee, S.-Y. (2006) · IEEE International Conference on Information Acquisition · doi:10.1109/icia.2006.305842 · Cited by 65 · see citing works
Inferring latent task structure for Multitask Learning by Multiple Kernel Learning
Widmer, C., Toussaint, N. C., Altün, Y., & Rätsch, G. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-s8-s5 · Cited by 64 · see citing works
Magic Wand: A Hand-Drawn Gesture Input Device in 3-D Space with Inertial Sensors
Cho, S.-J., Oh, J. K., Bang, W.-C., et al. (2004) · International Workshop on Frontiers in Handwriting Recognition (IWFHR) · doi:10.1109/iwfhr.2004.66 · Cited by 64 · see citing works
Sparse multitask regression for identifying common mechanism of response to therapeutic targets
Zhang, K., Gray, J. W., & Parvin, B. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq181 · Cited by 61 · see citing works
Tamada, Y., Bannai, H., Imoto, S., et al. (2005) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720005001569 · Cited by 57 · see citing works
High-level goal recognition in a wireless LAN
Yin, J., Chai, X., & Yang, Q. (2004) · Cited by 52 · see citing works
Self-citationProtein-protein interaction prediction via Collective Matrix Factorization
Xu, Q. S., Xiang, E. W., & Yang, Q. (2010) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2010.5706537 · Cited by 51 · see citing works
Self-citationKashima, H., Yamanishi, Y., Kato, T., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp494 · Cited by 50 · see citing works
Recognizing activities in multiple contexts using transfer learning
van Kasteren, T. L. M., Englebienne, G., & Kröse, B. (2008) · UvA-DARE (University of Amsterdam) · Cited by 44 · see citing works
Human Abnormal Gait Modeling via Hidden Markov Model
Chen, M., Huang, B., & Xu, Y. (2007) · IEEE International Conference on Information Acquisition · doi:10.1109/icia.2007.4295787 · Cited by 39 · see citing works
Novel Machine Learning Methods for MHC Class I Binding Prediction
Widmer, C., Toussaint, N. C., Altün, Y., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-16001-1_9 · Cited by 38 · see citing works
A New Multi-Task Learning Technique to Predict Classification of Leukemia and Prostate Cancer
Chen, A. H., & Huang, Z.-W. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13923-9_2 · Cited by 36 · see citing works
A new multitask learning method for multiorganism gene network estimation
Nassar, M., Abdallah, R., Zeineddine, H. A., et al. (2008) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2008.4595398 · Cited by 36 · see citing works
Kato, T., Okada, K., Kashima, H., et al. (2010) · International Journal of Knowledge Discovery in Bioinformatics · doi:10.4018/jkdb.2010100205 · Cited by 35 · see citing works
Multi Home Transfer Learning for Resident Activity Discovery and Recognition
Rashidi, P., & Cook, D. J. (2010) · Cited by 31 · see citing works
Transferring Learned Activities in Smart Environments
Rashidi, P., & Cook, D. J. (2009) · Ambient intelligence and smart environments · doi:10.3233/978-1-60750-034-6-185 · Cited by 23 · see citing works
Cross-mobile ELM based Activity Recognition
Zhao, Z., Chen, Y., Liu, J., & Liu, M. (2010) · International Journal of Engineering and Industries · doi:10.4156/ijei.vol1.issue1.3 · Cited by 16 · see citing works
Multi-platform gene-expression mining and marker gene analysis
Xu, Q. S., Xue, H., & Yang, Q. (2011) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2011.043030 · Cited by 12 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- R. A. Caruana, "Multitask learning: a knowledge-based source of inductive bias," Proceedings of theTenth International Confer- ence on Machine Learning, Amherst, MA, 1993, pp. 41-48.
- P. Rashidi and D. J. Cook, "Home to home transfer learning," Proceedings of the AAAI Plan, Activity, and Intent Recognition Workshop, Atlanta, GA, 2010.
- P. T. Spellman, G. Sherlock, M. Q. Zhang, V. R. Iyer, K. Anders, M. B. Eisen, P. O. Brown, D. Botstein, and B. Futcher, "Compre- hensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization," Molecu- lar Biology of the Cell, vol. 9, no. 12, pp. 3273-3297, Dec. 1998.
Cited by
Sorted by citations
Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks
Wu, K., & Wei, G.-W. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00558 · Cited by 160 · see citing works
- Authors
- Xu, Q., & Yang, Q.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- ORCID
- Venue
- Journal of Computing Science and Engineering
- Published
- 2011-09-30
- Volume
- 5
- Issue
- 3
- Pages
- 257-268
- ISSN
- 1976-4677 (ISSN-L)
- OpenAlex
- W2126574516
- Cited by
- 89
- References
- 64
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 1 · 2022: 7 · 2021: 4 · 2020: 32 · 2019: 5 · 2018: 7 · 2017: 13 · 2016: 4 · 2015: 4 · 2014: 3 · 2013: 3
- FWCI
- 2.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Computing Science and Engineering (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Hong Kong University of Science and Technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- transfer learning, multi-task learning, bioinformatics, limited labeled data, sequence classification, gene expression analysis, biological network reconstruction, biomedical applications, prediction model
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