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1

Conventional Verification for Unconventional Computing : a Genetic XOR Gate Example

Konur S., Gheorghe M., Dragomir C., Ipate F., Krasnogor N.

Fundamenta Informaticae

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2014

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Vol. 134, nr 1/2

97--110

EN

As unconventional computation matures and non-standard programming frameworks are demonstrated, the need for formal verification will become more prevalent. This is so because “programming” in unconventional substrates is difficult. In this paper we show how conventional verification tools can be used to verify unconventional programs implementing a logical XOR gate.

2

DomAns - pattern based method for protein domain boundaries prediction and analysis

Antczak M., Blazewicz J., Lukasiak P., Milostan M., Krasnogor N., Palik G.

Foundations of Computing and Decision Sciences

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2011

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Vol. 36, No. 1

35-56

EN

Determination of the native folded structure for a particular protein is a milestone towards understanding its function, and in most cases, can be done experimentally. However, the ability to predict in silico protein structure and related features would represent a fundamental breakthough in structural biology. The ability to predict domains in proteins is amongst the most important tasks needed for efective functional classification, homology-based structure prediction, structural genomics, as it makes function prediction easier. In this paper, we present the DomAnS, protein domain prediction approach, that is based on pattern alignment. DomAnS allows rapid screening for potential domain regions with the ability to recognize the most promising regions where domains might exists. The combination of the DomAnS algorithm with specialized databases that contains all known domains, allows us to find domain regions without solving 3D structure. Our approach has been tested on CASP7 data, and for 28 targets gave the best overall score.

3

Sequence similarity based method for protein function prediction

Blazewicz J., Lukasiak P., Milostan M., Krasnogor N., Jackowiak P.

Foundations of Computing and Decision Sciences

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2009

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Vol. 34, No. 3

173-191

EN

Motivation: Proteins are the main building blocks of life. They catalyze biological processes in living cells to sustain life and improve metabolism. They also act as biological scaffolds and are cell's workhorses. As a matter of fact, knowing their function is one of the most important milestones for understanding life.The function depends on the tertiary structure of the protein, but only for a fraction of amino acid sequences gathered in databases the structure is known. Thus, creation of efficient and accurate methods that predict function from sequences, based on already known function-sequence assignments, is a fundamental challenge in computational biology. Results: First, we show a detailed analysis of a usability of similarity search engines in the context of function prediction. Then we propose a simple and effective method for assigning function to sequences based on the results of similarity searches and information gathered from gene ontology annotation graphs. Availability: All data used for the analysis presented in this paper as well as raw result are available at the site: http://bio.cs.put.poznan.pl/funcpred/data/ Suplementary Material: Suplementary materials with additional charts are available at: http://bio.es.put.poznan.pl/funcpred/suplement/ Contact: protbio@cs.put.poznan.pl

4

3D-Judge : a metaserver approach to protein structure prediction

Jaśkowski W., Błażewicz J., Łukasiak P., Miłostan M., Krasnogor N.

Foundations of Computing and Decision Sciences

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2007

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Vol. 32, No. 1

3-14

EN

Analysing arid predicting the detailed three dimensional conformation of protein structures is a critical and important task within structural bioinformatics with impact on other fields, e.g.. drug design and delivery, sensing technologies, etc. Unfortunately, it is hard to identify one methodology that will give the best prediction of the three-dimensional structure for any sequence. That is, some predictors are best suited for some sequences and not for others. In trying to address this drawback of current prediction algorithms the research community introduced the concept of protein prediction metaservers. In this paper we propose a new metaserver method called 3D-Judge that uses an artificial neural network (ANN) to select the best model from among models produced by individual servers. The fundamental innovation we introduce is that the AXN is not only used to decide which models and servers to use as good predictions but, crucially, it is also used to analyse and "remember" the past performances of the servers it has access to. Thus, our method acts as both a kind of majority voting algorithm, by selecting models arising from different servers based on their mutual similarity, and also a reinforced learning method that takes cues from historical data of previously solved structures. We train and evaluate our metaserver based on previous GASP results and we compare SD-Judge with a popular and effective metaserver, namely. 3D-Jury. The obtained results indicate that 3D-Judge is competitive with 3D-Jury, outperforming it on many cases. We also present a discussion on future extensions to 3D-Judge.