A new multitask learning method for multiorganism gene network estimation

Nassar, Marcel; Abdallah, R. Ben; Zeineddine, Hady; Yaacoub, Elias; Dawy, Zaher

doi:10.1109/isit.2008.4595398

Cited by 31 publications

(2 citation statements)

References 9 publications

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“…Other examples of domain adaptation and transfer learning in fields that employ machine learning include: in bioinformatics, adaptive approaches have been successful in sequence classification [205,149], gene expression analysis [38,210], and biological network reconstruction [153,118]. Most often, domains correspond to different model organisms or different data-collecting research institutes [211].…”

Section: Relevancementioning

confidence: 99%

An introduction to domain adaptation and transfer learning

Kouw,

Loog

2018

Preprint

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In machine learning, if the training data is an unbiased sample of an underlying distribution, then the learned classification function will make accurate predictions for new samples. However, if the training data is not an unbiased sample, then there will be differences between how the training data is distributed and how the test data is distributed. Standard classifiers cannot cope with changes in data distributions between training and test phases, and will not perform well. Domain adaptation and transfer learning are sub-fields within machine learning that are concerned with accounting for these types of changes. Here, we present an introduction to these fields, guided by the question: when and how can a classifier generalize from a source to a target domain? We will start with a brief introduction into risk minimization, and how transfer learning and domain adaptation expand upon this framework. Following that, we discuss three special cases of data set shift, namely prior, covariate and concept shift. For more complex domain shifts, there are a wide variety of approaches. These are categorized into: importance-weighting, subspace mapping, domain-invariant spaces, feature augmentation, minimax estimators and robust algorithms. A number of points will arise, which we will discuss in the last section. We conclude with the remark that many open questions will have to be addressed before transfer learners and domain-adaptive classifiers become practical.

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Section: Relevancementioning

confidence: 99%

An introduction to domain adaptation and transfer learning

Kouw,

Loog

2018

Preprint

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“…As a follow-up, in 2008, Nassar et al [44] proposed a new score function that captures the evolutionary information shared between A and B by a single parameter β, instead of choosing two free parameters. The inputs of their multitask learning algorithm now include data samples D of the given organism, an input directed acyclic graph (DAG) G i n of the other organism, and a similarity parameter β.…”

Section: Systems Biologymentioning

confidence: 99%

A Survey of Transfer and Multitask Learning in Bioinformatics

Xu¹,

Yang²

2011

Journal of Computing Science and Engineering

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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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