| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Srinivasan, A.
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (15/15 displayed)
- 2022Resistive switching in polyvinylpyrrolidone/molybdenum disulfide composite-based memory devicescitations
- 2021Reduced graphene oxide (RGO) based optical fiber humidity sensor
- 2018Synthesis of finest superparamagnetic carbon-encapsulated magnetic nanoparticles by a plasma expansion method for biomedical applicationscitations
- 2016Creep behavior of Mg–10Gd–xZn (x=2 and 6wt%) alloyscitations
- 2016Hot tearing characteristics of Mg–2Ca–xZn alloyscitations
- 2015Hot Tearing Susceptibility of Mg-Ca Binary Alloyscitations
- 2015Effect of Zn addition on hot tearing behaviour of Mg–0.5Ca–xZn alloyscitations
- 2014Microstructures and mechanical properties of pure Mg processed by rotary swagingcitations
- 2014Investigations on microstructures, mechanical and corrosion properties of Mg-Gd-Zn alloyscitations
- 2014Corrosion behavior of Mg–Gd–Zn based alloys in aqueous NaCl solutioncitations
- 2013Hot tearing susceptibility of binary MgY alloy castingscitations
- 2013Microstructure, Mechanical and Corrosion Properties of Mg-Gd-Zn Alloyscitations
- 2012Metallurgical characterization of hot tearing curves recorded during solidification of magnesium alloyscitations
- 2012Magnetic properties and spin polarization of Co2Mn(SixSn1-x) alloys containing two L2(1) phasescitations
- 2001Are grammatical representations useful for learning from biological sequence data?— a case studycitations
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article
Are grammatical representations useful for learning from biological sequence data?— a case study
Abstract
This paper investigates whether Chomsky-like grammar representations are useful for learning cost-effective, comprehensible predictors of members of biological sequence families.The Inductive Logic Programming (ILP) Bayesian approach to learning from positive examples is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs).Collectively, five of the co-authors of this paper, have extensive expertise on NPPs and general bioinformatics methods. Their motivation for generating a NPP grammar was that none of the existing bioinformatics methods could provide sufficient cost-savings during the search for new NPPs. Prior to this project experienced specialists at SmithKline Beecham had tried for many months to hand-code such a grammar but without success. Our best predictor makes the search for novel NPPs more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity.As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the ILP Bayesian approach to learning from positive examples. A group of features is derived from this grammar. Other groups of features of NPPs are derived using other learning strategies.Amalgams of these groups are formed. A recognition model is generated for each amalgam using C4.5 and C4.5rules and its performance is measured using both predictive accuracy and a new cost function, Relative Advantage (RA). The highest RA was achieved by a model which includes grammar-derived features. This RA is significantly higher than the best RA achieved without the use of the grammar-derived features. Predictive accuracy is not a good measure of performance for this domain because it does not discriminate well between NPP recognition models: despite covering varying numbers of (the rare) positives, all the models are awarded a similar (high) score by predictive accuracy because they all exclude most of the abundant negatives.