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You searched for +publisher:"AUT University" +contributor:("Benuskova, Lubica"). Showing records 1 – 3 of 3 total matches.

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AUT University

1. Wang, Yuepeng. Integrative methods for gene data analysis and knowledge discovery on the case study of KEDRI’s brain gene ontology .

Degree: 2009, AUT University

In 2003, Pomeroy et al. published a research study that described a gene expression based prediction of central nervous system embryonal tumour (CNS) outcome. Over a half of decade, many models and approaches have been developed based on experimental data consisting of 99 samples with 7,129 genes. The way, how meaningful knowledge from these models can be extracted, and how this knowledge for further research is still a hot topic. This thesis addresses this and has developed an information method that includes modelling of interactive patterns, important genes discovery and visualisation of the obtained knowledge. The major goal of this thesis is to discover important genes responsible for CNS tumour and import these genes into a well structured knowledge framework system, called Brain-Gene-Ontology. In this thesis, we take the first step towards finding the most accurate model for analysing the CNS tumour by offering a comparative study of global, local and personalised modelling. Five traditional modelling approaches and a new personalised method – WWKNN (weighted distance, weighted variables K-nearest neighbours) – are investigated. To increase the classification accuracy and one-vs.-all based signal to- noise ratio is also developed for pre-processing experimental data. For the knowledge discovery, CNS-based ontology system is developed. Through ontology analysis, 21 discriminate genes are found to be relevant for different CNS tumour classes, medulloblastoma tumour subclass and medulloblastoma treatment outcome. All the findings in this thesis contribute for expanding the information space of the BGO framework. Advisors/Committee Members: Kasabov, Nikola (advisor), Benuskova, Lubica (advisor).

Subjects/Keywords: Bioinformatics; Ontology; Modeling experiment; Microarray

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, Y. (2009). Integrative methods for gene data analysis and knowledge discovery on the case study of KEDRI’s brain gene ontology . (Thesis). AUT University. Retrieved from http://hdl.handle.net/10292/467

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Yuepeng. “Integrative methods for gene data analysis and knowledge discovery on the case study of KEDRI’s brain gene ontology .” 2009. Thesis, AUT University. Accessed February 17, 2019. http://hdl.handle.net/10292/467.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Yuepeng. “Integrative methods for gene data analysis and knowledge discovery on the case study of KEDRI’s brain gene ontology .” 2009. Web. 17 Feb 2019.

Vancouver:

Wang Y. Integrative methods for gene data analysis and knowledge discovery on the case study of KEDRI’s brain gene ontology . [Internet] [Thesis]. AUT University; 2009. [cited 2019 Feb 17]. Available from: http://hdl.handle.net/10292/467.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang Y. Integrative methods for gene data analysis and knowledge discovery on the case study of KEDRI’s brain gene ontology . [Thesis]. AUT University; 2009. Available from: http://hdl.handle.net/10292/467

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


AUT University

2. Wysoski, Simei Gomes. Evolving spiking neural networks for adaptive audiovisual pattern recognition .

Degree: 2008, AUT University

This dissertation presents new modular and integrative information methods and systems inspired by the way the brain performs information processing, in particular, pattern recognition. The proposed artificial systems use spiking neurons as basic elements, which are the key components of spiking neural networks. Of particular interest to this research are various spiking neural network architectures and learning procedures that permit different pattern recognition problems to be solved in an evolvable and adaptive way. Spiking neural networks are used to model human visual and auditory pathways and are trained to perform the specific task of person authentication. The systems are individually tuned and trained to recognize facial information and to analyze sound signals from spoken sentences. The modelling of the integration of different sources of information (multisensory integration) using spiking neural networks is also a subject of investigation. A network architecture is proposed and a model for audiovisual pattern recognition is designed as an example. The main original contributions of this thesis are: a) Evaluation and further extension of adaptive learning procedures to perform visual pattern recognition. A new learning procedure that enables the system to change its structure, creating/merging neuronal maps of spiking neurons is presented and evaluated on a face recognition problem. b) Design of two new spiking neural network architectures to perform person authentication through the processing of speech signals. c) Design and evaluation of a new architecture that integrates sensory modalities based on spiking neurons. The integrative architecture combines opinions from individual modalities within a supramodal layer, which contains neurons sensitive to multiple sensory information. An additional feature that increases biological relevance is the crossmodal coupling of modalities, which effectively enables a given sensory modality to exert direct influence upon the processing areas typically related to other modalities. The contributions were published in one journal paper and in four refereed international conference proceedings. The proposed system designs were implemented and, through computer simulations, demonstrated comparable performance with traditional benchmarking methods. The systems have some promising features: they can be naturally optimized in respect to different criteria: accuracy (when very accurate results are expected), energy efficiency (when management of resources play an important role), and speed (when a decision needs to be made within a limited time). In this thesis, most of the parameters have been exhaustively optimized by hand or by using simple heuristics. As a direction for future work, there is an opportunity to include automated, specially tailored parameters optimization procedures or even general-purpose optimization algorithms, e.g., Genetic Algorithms and Particle Swarm Optimization. Overall, the results obtained in this thesis clearly indicate that it is indeed… Advisors/Committee Members: Kasabov, Nikola (advisor), Benuskova, Lubica (advisor).

Subjects/Keywords: Artificial intelligence; Visual pattern recognition; Auditory pattern recognition; Experimentation and quantitative evaluation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wysoski, S. G. (2008). Evolving spiking neural networks for adaptive audiovisual pattern recognition . (Thesis). AUT University. Retrieved from http://hdl.handle.net/10292/390

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wysoski, Simei Gomes. “Evolving spiking neural networks for adaptive audiovisual pattern recognition .” 2008. Thesis, AUT University. Accessed February 17, 2019. http://hdl.handle.net/10292/390.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wysoski, Simei Gomes. “Evolving spiking neural networks for adaptive audiovisual pattern recognition .” 2008. Web. 17 Feb 2019.

Vancouver:

Wysoski SG. Evolving spiking neural networks for adaptive audiovisual pattern recognition . [Internet] [Thesis]. AUT University; 2008. [cited 2019 Feb 17]. Available from: http://hdl.handle.net/10292/390.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wysoski SG. Evolving spiking neural networks for adaptive audiovisual pattern recognition . [Thesis]. AUT University; 2008. Available from: http://hdl.handle.net/10292/390

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


AUT University

3. Jain, Vishal. Integrative approaches to modelling and knowledge discovery of molecular interactions in bioinformatics .

Degree: 2008, AUT University

The core focus of this research lies in developing and using intelligent methods to solve biological problems and integrating the knowledge for understanding the complex gene regulatory phenomenon. We have developed an integrative framework and used it to: model molecular interactions from separate case studies on time-series gene expression microarray datasets, molecular sequences and structure data including the functional role of microRNAs; to extract knowledge; and to build reusable models for the central dogma theme. Knowledge was integrated with the use of ontology and it can be reused to facilitate new discoveries as demonstrated on one of our systems – the Brain Gene Ontology (BGO). The central dogma theme states that proteins are produced from the DNA (gene) via an intermediate transcript called RNA. Later these proteins play the role of enzymes to perform the checkpoints as a gene expression control. Also, according to the recently emerged paradigm, sometimes genes do not code for proteins but results in small molecules of microRNAs which in turn controls the gene regulation. The idea is that such a very complicated molecular biology process (central dogma) results in production of a wide variety of data that can be used by computer scientists for modelling and to enable discoveries. We have suggested that this range of data should actually be taken into account for analysis to understand the concept of gene regulation instead of just taking one source of data and applying some standard methods to reveal facts in the system biology. The problem is very complex and, currently, computational algorithms have not been really successful because either existing methods have certain problems or the proven results were obtained for only one domain of the central dogma of molecular biology, so there has always been a lack of knowledge integration. Proper maintenance of diverse sources of data, structures and, in particular, their adaptation to new knowledge is one of the most challenging problems and one of the crucial tasks towards the knowledge integration vision is the efficient encoding of human knowledge in ontologies. More specifically this work has contributed towards the development of novel computational and information science methods and we have promoted the vision of knowledge integration by developing brain gene ontology (BGO) system. With the integrative use of several bioinformatics methods, this research has indeed resulted in modelling of such knowledge that has not been revealed in system biology so far. There are many discoveries made during my study and some of the findings are briefly mentioned as follows: (1) in relation to leukaemia disease we have discovered a new gene “TCF-1” that interacts with the “telomerase” gene. (2) With respect to yeast cell cycle analysis, we hypothesize that exoglucanase gene “exg1” is now implicated to be tied with “MCB cluster regulation” and a “mannosidase” with “histone linked mannoses”. A new quantitative prediction is that the time delay of the interaction… Advisors/Committee Members: Kasabov, Nikola (advisor), Benuskova, Lubica (advisor), Pang, Paul (advisor).

Subjects/Keywords: Bioinformatics; Gene regulatory networks (GRNs); Interaction; Computational; Ontology; Integration

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jain, V. (2008). Integrative approaches to modelling and knowledge discovery of molecular interactions in bioinformatics . (Thesis). AUT University. Retrieved from http://hdl.handle.net/10292/439

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Jain, Vishal. “Integrative approaches to modelling and knowledge discovery of molecular interactions in bioinformatics .” 2008. Thesis, AUT University. Accessed February 17, 2019. http://hdl.handle.net/10292/439.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Jain, Vishal. “Integrative approaches to modelling and knowledge discovery of molecular interactions in bioinformatics .” 2008. Web. 17 Feb 2019.

Vancouver:

Jain V. Integrative approaches to modelling and knowledge discovery of molecular interactions in bioinformatics . [Internet] [Thesis]. AUT University; 2008. [cited 2019 Feb 17]. Available from: http://hdl.handle.net/10292/439.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jain V. Integrative approaches to modelling and knowledge discovery of molecular interactions in bioinformatics . [Thesis]. AUT University; 2008. Available from: http://hdl.handle.net/10292/439

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.