Generative Kernels for Gene Function Prediction Through Probabilistic Tree Models of Evolution

Nicotra, Luca; Micheli, Alessio; Starita, Antonina

doi:10.1007/978-3-540-73400-0_65

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…by building kernels that exploit an underlying generative model. This is, for instance, the case of the Fisher tree kernel [19] or of a family of adaptive tree kernels built using similarity metrics on multisets of HTMM state information [4], [20], [21].…”

Section: B Hidden Tree Markov Modelsmentioning

confidence: 99%

Hidden tree Markov networks: Deep and wide learning for structured data

Bacciu

2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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The paper introduces the Hidden Tree Markov Network (HTN), a neuro-probabilistic hybrid fusing the representation power of generative models for trees with the incremental and discriminative learning capabilities of neural networks. We put forward a modular architecture in which multiple generative models of limited complexity are trained to learn structural feature detectors whose outputs are then combined and integrated by neural layers at a later stage. In this respect, the model is both deep, thanks to the unfolding of the generative models on the input structures, as well as wide, given the potentially large number of generative modules that can be trained in parallel. Experimental results show that the proposed approach can outperform state-of-the-art syntactic kernels as well as generative kernels built on the same probabilistic model as the HTN.

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Section: B Hidden Tree Markov Modelsmentioning

confidence: 99%

Hidden tree Markov networks: Deep and wide learning for structured data

Bacciu

2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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Modeling adaptive kernels from probabilistic phylogenetic trees

Nicotra

Micheli

2009

Artificial Intelligence in Medicine

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Adaptive tree kernel by multinomial generative topographic mapping

Bacciu

Micheli

Sperduti

2011

The 2011 International Joint Conference on Neural Networks

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Learning the kernel function from data is a challenging open issue in structured data processing. In the paper, we propose a novel adaptive kernel, defined over a generative learning model, that exploits a novel multinomial extension of the Generative Topographic Mapping for Structured Data (GTM-SD). We show how the proposed kernel effectively exploits the GTM-SD continuity and smoothness properties to provide dense kernels characterized by an high discriminative power even with small topographic maps. Experimental evaluations on challenging structured XML document repositories show the effectiveness of the proposed approach against state-of-the-art syntactic and adaptive convolutional kernels

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Generative Kernels for Gene Function Prediction Through Probabilistic Tree Models of Evolution

Cited by 3 publications

References 4 publications

Hidden tree Markov networks: Deep and wide learning for structured data

Hidden tree Markov networks: Deep and wide learning for structured data

Modeling adaptive kernels from probabilistic phylogenetic trees

Adaptive tree kernel by multinomial generative topographic mapping

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