Eser Aygün

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24.07.2010 · Print · History · Edit

MS Thesis

Computer.MSThesis History

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24.07.2010 by 85.101.227.68 -
Changed lines 15-16 from:
* Aygün, E.; Oommen B.J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', accepted
to:
* Aygün, E.; Oommen B.J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', '''2010''', ''43'', 3891
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* Aygün, E.; Oommen B.J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', kabul edildi
to:
* Aygün, E.; Oommen B.J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', '''2010''', ''43'', 3891
19.02.2010 by 160.75.26.183 -
Changed line 5 from:
My MS thesis was a research on protein function prediction. As a part of this work, I measured the contribution of the secondary structure information on the performance of protein function prediction and I applied [[Oommen-Kashyap syntactic transition probability calculation->http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.479]] on the peptide classification problem. This is the first published work, as far as I know, that incorporates Oommen-Kashyap method and biological sequence analysis.
to:
My MS thesis was a research on protein function prediction. As a part of this work, I measured the contribution of the secondary structure information on the performance of protein function prediction and I applied [[Oommen-Kashyap syntactic transition probability calculation->http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.479]] on the peptide classification problem. This is the first published work, as far as I know, that combines Oommen-Kashyap method and biological sequence analysis.
25.09.2009 by 160.75.17.6 -
Changed lines 43-45 from:
* Aygün, E.; Oommen B. J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', kabul edildi

* Aygün, E.; Oommen B. J. & Cataltepe, Z. On Utilizing Optimal and Information Theoretic Syntactic Modelling for Peptide Classification ''Pattern Recognition in Bioinformatics'', '''2009''' ([[Sunum->(Attach:)Aygun2009.ppt]])
to:
* Aygün, E.; Oommen B.J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', kabul edildi

* Aygün, E.; Oommen B.J. & Cataltepe, Z. On Utilizing Optimal and Information Theoretic Syntactic Modelling for Peptide Classification ''Pattern Recognition in Bioinformatics'', '''2009''' ([[Sunum->(Attach:)Aygun2009.ppt]])
25.09.2009 by 160.75.17.6 -
Changed lines 15-17 from:
* Aygün, E.; Oommen B. J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', accepted

* Aygün, E.; Oommen B. J. & Cataltepe, Z. On Utilizing Optimal and Information Theoretic Syntactic Modelling for Peptide Classification ''Pattern Recognition in Bioinformatics'', '''2009''' ([[Presentation->(Attach:)Aygun2009.ppt]])
to:
* Aygün, E.; Oommen B.J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', accepted

* Aygün, E.; Oommen B.J. & Cataltepe, Z. On Utilizing Optimal and Information Theoretic Syntactic Modelling for Peptide Classification ''Pattern Recognition in Bioinformatics'', '''2009''' ([[Presentation->(Attach:)Aygun2009.ppt]])
25.09.2009 by 160.75.17.6 -
Changed lines 15-16 from:
* Aygün, E.; Oommen B. J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', waiting for publishing
to:
* Aygün, E.; Oommen B. J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', accepted
Changed line 43 from:
* Aygün, E.; Oommen B. J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', waiting for publishing
to:
* Aygün, E.; Oommen B. J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', kabul edildi
25.09.2009 by 160.75.17.6 -
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(:title Ba�l�k:)

T�rk�e
to:
(:title Yüksek Lisans Tezi:)

Yüksek lisans tezim, protein işlev kestirimi üzerine bir araştırmaydı. Bu çalışmanın bir parçası olarak, ikincil yapı bilgisinin protein işlev kestirimi başarısına olan katkısını ölçtüm ve [[Oommen-Kashyap yazı dizimsel geçiş olasılığı hesabını->http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.479]] peptit sınıflandırma problemine uyguladım. Bu, bildiğim kadarıyla, Oommen-Kashyap yöntemiyle biyolojik dizi analizini bir araya getiren ilk yayınlanmış çalışma.

Kısa özet:
-->'''Protein İşlev Kestiriminde Yapısal Bilginin Katkısı ve Dizi Geçiş Olasılıkları ile Peptit Sınıflandırma'''
-->Biyolojik dizi analizi, nükleotid ve amino asit dizilerinin evrimsel, yapısal ve işlevsel özelliklerini ortaya çıkarmayı amaçlar. İkili hizalama algoritmaları, biyolojik dizi analizinde yoğun olarak kullanılan araçlardır. Bu çalışmada; standart ikili hizalama algoritmalarının bir derlemesini sunmak, Oommen ve Kashyap'ın tanımladığı dizi geçiş olasılığını bir biyolojik dizi benzerlik ölçütü olarak tanıtmak, yapısal bilginin protein işlev kestiriminin başarısını nasıl arttırdığını göstermek, Oommen ve Kashyap'ın dizi geçiş olasılığını, iki peptit sınıflandırma problemi üzerine standart dizi benzerlik ölçütleriyle kıyaslamak, ve gereken dizi analiz araçlarını bir bilgisayar yazılımı olarak gerçeklemek amaçlanmıştır. Çalışmanın deneysel kısmının ilk aşamasında, ikincil yapı dizilerini amino asit dizisi hizalamalarıyla birlikte kullanmanın moleküler işlev kestirim başarısını arttırdığını açıkça ortaya koymuştur. Buna karşılık kestirilmiş ikincil yapıların kestirime herhangi bir katkısının olmadığı gözlenmiştir. İkinci olarak, dizi geçiş olasılıkları, sınıflandırıcıya sunulan nitelikler olarak, standart genel hizalama puanları ile kıyaslanmıştır. Sınıflandırma başarısı ölçümleri, dizi geçiş olasılıklarının genel hizalama puanlarından çok daha iyi nitelikler sağladığını şüpheye yer bırakmayacak şekilde ortaya koymuştur. Önerilen yöntem ayrıca aynı veri kümeleri üzerinde uygulanmış önceki yöntemlerin neredeyse hepsinden daha başarılı olarak genel kabul görmüş peptit benzerlik ölçütü olmaya aday olduğunu kanıtlamıştır.

Tez: [[(Attach:)Aygun2009.pdf]]

!!! İlgili Yayınlar

* Aygün, E.; Oommen B. J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', waiting for publishing

* Aygün, E.; Oommen B. J. & Cataltepe, Z. On Utilizing Optimal and Information Theoretic Syntactic Modelling for Peptide Classification ''Pattern Recognition in Bioinformatics'', '''2009''' ([[Sunum->(Attach:)Aygun2009.ppt]])

* Aygün, E.; Komurlu, C.; Aydin, Z. & Cataltepe, Z. Protein Function Prediction with Amino Acid Sequence and Secondary Structure Alignment Scores ''International Symposium on Health Informatics and Bioinformatics'', '''2008'''

* Filiz, A.; Aygün, E.; Keskin, O. & Cataltepe, Z. Importance of Secondary Structure Elements for Prediction of GO Annotations ''International Symposium on Health Informatics and Bioinformatics'', '''2008'''

* Aygün E. & Cataltepe Z. Gene Ontology (GO) Molecular Function Prediction Based on Alignment Scores ''International Symposium on Health Informatics and Bioinformatics'', '''2007'''

* Cataltepe, Z.; Ayan, U. & Aygün, E. Protein Function Prediction Using Motifs, Sequence Features, Alignment Scores ''Research in Computational Molecular Biology'', '''2007'''

* Cataltepe, Z.; Aygün, E.; Filiz, A.; Keskin, O.; Komurlu, C. & Altunbasak, Y. Dimensionality Reduction for Protein Function Prediction ''Automated Function Prediction – Biosapiens Joint Special Interest Group Meeting at ISMB/ECCB'', '''2007'''
25.09.2009 by 160.75.17.6 -
Changed line 5 from:
My MS thesis was a research on protein function prediction. As a part of this work, I measured the contribution of the secondary structure information on the performance of protein function prediction and I applied [[Oommen-Kashyap transition probability calculation->http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.479]] on the peptide classification problem. This is the first published work, as far as I know, that incorporates Oommen-Kashyap method and biological sequence analysis.
to:
My MS thesis was a research on protein function prediction. As a part of this work, I measured the contribution of the secondary structure information on the performance of protein function prediction and I applied [[Oommen-Kashyap syntactic transition probability calculation->http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.479]] on the peptide classification problem. This is the first published work, as far as I know, that incorporates Oommen-Kashyap method and biological sequence analysis.
25.09.2009 by 160.75.17.6 -
Changed lines 5-6 from:
My MS thesis was a research on protein function prediction. As a part of this work, I measured the contribution of the secondary structure information on the performance of protein function prediction and I applied Oommen-Kashyap transition probability calculation on the peptide classification problem. This is the first published work, as far as I know, that incorporates Oommen-Kashyap method and biological sequence analysis.
to:
My MS thesis was a research on protein function prediction. As a part of this work, I measured the contribution of the secondary structure information on the performance of protein function prediction and I applied [[Oommen-Kashyap transition probability calculation->http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.479]] on the peptide classification problem. This is the first published work, as far as I know, that incorporates Oommen-Kashyap method and biological sequence analysis.
Changed lines 11-12 from:
Thesis (in Turkish): Aygun2009.pdf
to:
Thesis (in Turkish): [[(Attach:)Aygun2009.pdf]]
Changed line 17 from:
* Aygün, E.; Oommen B. J. & Cataltepe, Z. On Utilizing Optimal and Information Theoretic Syntactic Modelling for Peptide Classification ''Pattern Recognition in Bioinformatics'', '''2009'''
to:
* Aygün, E.; Oommen B. J. & Cataltepe, Z. On Utilizing Optimal and Information Theoretic Syntactic Modelling for Peptide Classification ''Pattern Recognition in Bioinformatics'', '''2009''' ([[Presentation->(Attach:)Aygun2009.ppt]])
25.09.2009 by 160.75.17.6 -
Changed lines 7-8 from:
Here is the abstract:
to:
Abstract:
25.09.2009 by 160.75.17.6 -
Changed lines 16-42 from:
* Aygün, E.; Oommen B. J. & Cataltepe, Z.
Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling
''Pattern Recognition'', waiting for publishing

* Aygün, E.; Oommen B. J. & Cataltepe, Z.
On Utilizing Optimal and Information Theoretic Syntactic Modelling for Peptide Classification
''Pattern Recognition in Bioinformatics'', '''2009'''

* Aygün, E.; Komurlu, C.; Aydin, Z. & Cataltepe, Z.
Protein Function Prediction with Amino Acid Sequence and Secondary Structure Alignment Scores
''International Symposium on Health Informatics and Bioinformatics'', '''2008'''

* Filiz, A.; Aygün, E.; Keskin, O. & Cataltepe, Z.
Importance of Secondary Structure Elements for Prediction of GO Annotations
''International Symposium on Health Informatics and Bioinformatics'', '''2008'''

* Aygün E. & Cataltepe Z.
Gene Ontology (GO) Molecular Function Prediction Based on Alignment Scores
''International Symposium on Health Informatics and Bioinformatics'', '''2007'''

* Cataltepe, Z.; Ayan, U. & Aygün, E.
Protein Function Prediction Using Motifs, Sequence Features, Alignment Scores
''Research in Computational Molecular Biology'', '''2007'''

* Cataltepe, Z.; Aygün, E.; Filiz, A.; Keskin, O.; Komurlu, C. & Altunbasak, Y.
Dimensionality Reduction for Protein Function Prediction
''Automated Function Prediction – Biosapiens Joint Special Interest Group Meeting at ISMB/ECCB'', '''2007'''
to:
* Aygün, E.; Oommen B. J. & Cataltepe, Z. Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling ''Pattern Recognition'', waiting for publishing

* Aygün, E.; Oommen B. J. & Cataltepe, Z. On Utilizing Optimal and Information Theoretic Syntactic Modelling for Peptide Classification ''Pattern Recognition in Bioinformatics'', '''2009'''

* Aygün, E.; Komurlu, C.; Aydin, Z. & Cataltepe, Z. Protein Function Prediction with Amino Acid Sequence and Secondary Structure Alignment Scores ''International Symposium on Health Informatics and Bioinformatics'', '''2008'''

* Filiz, A.; Aygün, E.; Keskin, O. & Cataltepe, Z. Importance of Secondary Structure Elements for Prediction of GO Annotations ''International Symposium on Health Informatics and Bioinformatics'', '''2008'''

* Aygün E. & Cataltepe Z. Gene Ontology (GO) Molecular Function Prediction Based on Alignment Scores ''International Symposium on Health Informatics and Bioinformatics'', '''2007'''

* Cataltepe, Z.; Ayan, U. & Aygün, E. Protein Function Prediction Using Motifs, Sequence Features, Alignment Scores ''Research in Computational Molecular Biology'', '''2007'''

* Cataltepe, Z.; Aygün, E.; Filiz, A.; Keskin, O.; Komurlu, C. & Altunbasak, Y. Dimensionality Reduction for Protein Function Prediction ''Automated Function Prediction – Biosapiens Joint Special Interest Group Meeting at ISMB/ECCB'', '''2007'''
25.09.2009 by 160.75.17.6 -
Added lines 11-42:

Thesis (in Turkish): Aygun2009.pdf

!!! Related Publications

* Aygün, E.; Oommen B. J. & Cataltepe, Z.
Peptide Classification Using Optimal and Information Theoretic Syntactic Modelling
''Pattern Recognition'', waiting for publishing

* Aygün, E.; Oommen B. J. & Cataltepe, Z.
On Utilizing Optimal and Information Theoretic Syntactic Modelling for Peptide Classification
''Pattern Recognition in Bioinformatics'', '''2009'''

* Aygün, E.; Komurlu, C.; Aydin, Z. & Cataltepe, Z.
Protein Function Prediction with Amino Acid Sequence and Secondary Structure Alignment Scores
''International Symposium on Health Informatics and Bioinformatics'', '''2008'''

* Filiz, A.; Aygün, E.; Keskin, O. & Cataltepe, Z.
Importance of Secondary Structure Elements for Prediction of GO Annotations
''International Symposium on Health Informatics and Bioinformatics'', '''2008'''

* Aygün E. & Cataltepe Z.
Gene Ontology (GO) Molecular Function Prediction Based on Alignment Scores
''International Symposium on Health Informatics and Bioinformatics'', '''2007'''

* Cataltepe, Z.; Ayan, U. & Aygün, E.
Protein Function Prediction Using Motifs, Sequence Features, Alignment Scores
''Research in Computational Molecular Biology'', '''2007'''

* Cataltepe, Z.; Aygün, E.; Filiz, A.; Keskin, O.; Komurlu, C. & Altunbasak, Y.
Dimensionality Reduction for Protein Function Prediction
''Automated Function Prediction – Biosapiens Joint Special Interest Group Meeting at ISMB/ECCB'', '''2007'''
25.09.2009 by 160.75.17.6 -
Changed line 5 from:
My MS thesis was a research on protein function prediction. As a part of this work, I measured the contribution of the secondary structure information on the performance of protein function prediction and I applied Oommen-Kashyap transition probability calculation on the peptide classification problem. This is the first published work that incorporates Oommen-Kashyap method and biological sequence analysis, as far as I know.
to:
My MS thesis was a research on protein function prediction. As a part of this work, I measured the contribution of the secondary structure information on the performance of protein function prediction and I applied Oommen-Kashyap transition probability calculation on the peptide classification problem. This is the first published work, as far as I know, that incorporates Oommen-Kashyap method and biological sequence analysis.
25.09.2009 by 160.75.17.6 -
Changed line 9 from:
-->IMPROVEMENT OF PROTEIN FUNCTION PREDICTION USING STRUCTURAL INFORMATION AND PEPTIDE CLASSIFICATION USING SYNTACTIC TRANSITION PROBABILITIES
to:
-->'''Improvement of Protein Function Prediction Using Structural Information and Peptide Classification Using Syntactic Transition Probabilities'''
25.09.2009 by 160.75.17.6 -
Changed lines 5-6 from:
My MS thesis was a research on protein function prediction. As a part of this work, I measured the contribution of the secondary structure information on the performance of protein function prediction and I applied Oommen-Kashyap transition probability calculation onto the peptide classification problem.
to:
My MS thesis was a research on protein function prediction. As a part of this work, I measured the contribution of the secondary structure information on the performance of protein function prediction and I applied Oommen-Kashyap transition probability calculation on the peptide classification problem. This is the first published work that incorporates Oommen-Kashyap method and biological sequence analysis, as far as I know.
Added line 9:
-->IMPROVEMENT OF PROTEIN FUNCTION PREDICTION USING STRUCTURAL INFORMATION AND PEPTIDE CLASSIFICATION USING SYNTACTIC TRANSITION PROBABILITIES
25.09.2009 by 160.75.17.6 -
Added lines 1-17:
(:if userlang en:)
(:comment ----------------------------------------------------------------- :)
(:title MS Thesis:)

My MS thesis was a research on protein function prediction. As a part of this work, I measured the contribution of the secondary structure information on the performance of protein function prediction and I applied Oommen-Kashyap transition probability calculation onto the peptide classification problem.

Here is the abstract:

-->Biological sequence analysis deals with nucleotide and amino acid sequences, aiming to expose their evolutionary, structural and functional properties. This study intends to provide a review of well known pairwise alignment methods, to introduce the syntactic transition probability of Oommen and Kashyap as a biological sequence similarity metric, to demonstrate how the structural information improves protein function prediction, to compare syntactic transition probability of Oommen and Kashyap with standard sequence similarity metrics on two peptide classifaction problems, and to implement necessary sequence analysis tools as a computer software. In the first part of the experiments, the results clearly indicate that the use of secondary structure sequences along with amino acid sequence alignments improves molecular function prediction performance, while the use of predicted secondary structures does not. In the second part, syntactic transition probabilities are compared with standard global alignment scores as being features fed into a machine learning classifier. The classification performance measurements undoubtedly proved that syntactic transition probabilities are much better features than global alignment scores for peptides.
(:comment ----------------------------------------------------------------- :)
(:if userlang tr:)
(:comment ----------------------------------------------------------------- :)
(:title Ba�l�k:)

T�rk�e
(:comment ----------------------------------------------------------------- :)
(:if:)