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[公告] 博士論文口試 黃筌敬 (2013/11/7)
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ぃ熬み~
Oct 31, 2013, 4:26:35 AM10/31/13
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清華大學資訊工程學系博士學位口試
學生姓名： 黃筌敬
指導教授： 唐傳義 老師
指導委員：
口試委員： 韓永楷 老師、盧錦隆 老師、林俊淵 老師、葉信宏 老師
日期時間： 民國 102 年 11 月 07 日 13 時 30 分
口試地點： 清華大學台達館 629 會議室
口試題目： Applying Structural Domain Information for Enzyme Reaction Annotation
and Protein-Protein Interaction Inference
應用蛋白質片段資訊進行酵素功能標註及推論蛋白質間交互作用關係
論文摘要：
Domains are fundamental building blocks of proteins which perform a variety of
functions within living organisms, including catalysis, signal transduction,
and transport of nutrients. The majority of proteins are composed of more than
two domains that recognize and bind structural units in other proteins through
protein-protein interactions. This dissertation uses the nature of domains in
the proteins to investigate two main topics including “protein function
prediction based on the domain architecture of a protein” and “inferring
protein-protein interactions (PPIs) from domain-domain interactions (DDIs)”.
The gap between novel protein sequences and characterized protein functions has
been widened according to the advent of high-throughput genome sequencing
techniques in the post-genomics era. To identify functions of a protein from
manually curated sequence annotation is a challenging task; therefore, automated
protein function prediction techniques are necessary. The enzyme nomenclature
proposed by the International Union of Biochemistry and Molecular Biology has
provided a well-defined four-field number on enzyme classification. The first
three numbers of an enzyme reaction describe the overtype of enzymatic reaction,
and the last number denotes the substrate specificity of a reaction. Proteins
are grouped into two data sets, comprising the 3-numerical-block set and
the 4-numerical-block set. According to whether the protein performed more than
one enzymatic reaction, each data set was further divided into single-EC cases
and multiple-EC cases. For the case of single-EC, the fractions of entries
correctly classified using the well-known association rule method reached 96%
and 91% accuracy for the 3-numerical-block set and the 4-numerical-block set,
respectively. The proposed enzyme reaction prediction (ERP) method showed
marginally higher accuracy, with 99% and 92% separately. It is more difficult
to predict multiple enzymatic activities for a single protein of the multiple-EC
case. For the case of multiple-EC, the fractions of entries correctly predicted
for 3-numerical-block set and the 4-numerical-block setswere17% and 8%,
respectively, for the association rule method, and 49% and 42%, respectively,
for the ERP method.Biological processes could be carried out when one protein
recognize and bind certain structural elements in other proteins through PPIs.
Therefore, it is possible to explore protein functions from protein interactions
at domain level. Noroviruses cause severe gastroenteritis and foodborne illness
during the winter worldwide. There is no efficient vaccine for Noroviruses
because of their variable genome sequences. Vulnerable populations suffer from
Noroviruses often require hospitalization and may die. We attempted to build
the protein interaction network from the domain level for clinical applications
and drug design further.
主要著作：
Journal:
1.Chuan-Ching Huang, Chun-Yuan Lin, Cheng-Wen Chang, Chuan Yi Tang,
“Enzyme reaction annotation using cloud techniques”,
Journal of Biomedicine and Biotechnology, 2013
2.Chuan-Ching Huang, Chuan Yi Tang,
“Protein-protein interactions inferred from domain-domain interactions in
genogroup II genotype 4 Norovirus sequences”,
International Journal of Genomics, 2013.
Conference:
1.Chuan-Ching Huang, Chun-Yuan Lin, Cheng-Wen Chang, Chuan Yi Tang,
“Automatic Prediction of Enzyme Functions from Domain Compositions Using
Enzyme Reaction Prediction Scheme”,
The 2012 International Conference on Biomedical Engineering and Biotechnology
(iCBEB 2012), IEEE Computer Society, Macau, May 2012, p.82-85.
--
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學生姓名： 黃筌敬
指導教授： 唐傳義 老師
指導委員：
口試委員： 韓永楷 老師、盧錦隆 老師、林俊淵 老師、葉信宏 老師
日期時間： 民國 102 年 11 月 07 日 13 時 30 分
口試地點： 清華大學台達館 629 會議室
口試題目： Applying Structural Domain Information for Enzyme Reaction Annotation
and Protein-Protein Interaction Inference
應用蛋白質片段資訊進行酵素功能標註及推論蛋白質間交互作用關係
論文摘要：
Domains are fundamental building blocks of proteins which perform a variety of
functions within living organisms, including catalysis, signal transduction,
and transport of nutrients. The majority of proteins are composed of more than
two domains that recognize and bind structural units in other proteins through
protein-protein interactions. This dissertation uses the nature of domains in
the proteins to investigate two main topics including “protein function
prediction based on the domain architecture of a protein” and “inferring
protein-protein interactions (PPIs) from domain-domain interactions (DDIs)”.
The gap between novel protein sequences and characterized protein functions has
been widened according to the advent of high-throughput genome sequencing
techniques in the post-genomics era. To identify functions of a protein from
manually curated sequence annotation is a challenging task; therefore, automated
protein function prediction techniques are necessary. The enzyme nomenclature
proposed by the International Union of Biochemistry and Molecular Biology has
provided a well-defined four-field number on enzyme classification. The first
three numbers of an enzyme reaction describe the overtype of enzymatic reaction,
and the last number denotes the substrate specificity of a reaction. Proteins
are grouped into two data sets, comprising the 3-numerical-block set and
the 4-numerical-block set. According to whether the protein performed more than
one enzymatic reaction, each data set was further divided into single-EC cases
and multiple-EC cases. For the case of single-EC, the fractions of entries
correctly classified using the well-known association rule method reached 96%
and 91% accuracy for the 3-numerical-block set and the 4-numerical-block set,
respectively. The proposed enzyme reaction prediction (ERP) method showed
marginally higher accuracy, with 99% and 92% separately. It is more difficult
to predict multiple enzymatic activities for a single protein of the multiple-EC
case. For the case of multiple-EC, the fractions of entries correctly predicted
for 3-numerical-block set and the 4-numerical-block setswere17% and 8%,
respectively, for the association rule method, and 49% and 42%, respectively,
for the ERP method.Biological processes could be carried out when one protein
recognize and bind certain structural elements in other proteins through PPIs.
Therefore, it is possible to explore protein functions from protein interactions
at domain level. Noroviruses cause severe gastroenteritis and foodborne illness
during the winter worldwide. There is no efficient vaccine for Noroviruses
because of their variable genome sequences. Vulnerable populations suffer from
Noroviruses often require hospitalization and may die. We attempted to build
the protein interaction network from the domain level for clinical applications
and drug design further.
主要著作：
Journal:
1.Chuan-Ching Huang, Chun-Yuan Lin, Cheng-Wen Chang, Chuan Yi Tang,
“Enzyme reaction annotation using cloud techniques”,
Journal of Biomedicine and Biotechnology, 2013
2.Chuan-Ching Huang, Chuan Yi Tang,
“Protein-protein interactions inferred from domain-domain interactions in
genogroup II genotype 4 Norovirus sequences”,
International Journal of Genomics, 2013.
Conference:
1.Chuan-Ching Huang, Chun-Yuan Lin, Cheng-Wen Chang, Chuan Yi Tang,
“Automatic Prediction of Enzyme Functions from Domain Compositions Using
Enzyme Reaction Prediction Scheme”,
The 2012 International Conference on Biomedical Engineering and Biotechnology
(iCBEB 2012), IEEE Computer Society, Macau, May 2012, p.82-85.
--
[;30m�[44mc_�[1;30m纂�[33m（_ [;34m▇▆ [1;31;44m' * [;34;44m�[31m奇 [34;41m�[31;44m岷 [41m�[34;40m▆▇ [30;44m [37m▁_ [30m ▄▆▇ [m�[33mC [31m. [1;34m楓橋驛站�[37mD [30mtelnet://imaple.tw [;30m�[41m�[31;40m�[41m�[40m} [m
[34m= [37m�[44mb ▔﹊ [30m�[34m� [31m* [33m. [34;41m▆ [1;30;44m�[;30;41mt@ [31;44m▋ [30m▃ [m▔ [1;35m▂~ [5m+ [;33m [30m�[31m�[1;31m�[;30m�[m
￣ [30;44m▃▂▁▂�[31mC [30m▁▂ˍ [31m_�[41m�[30m〢 [44m_▇ [33;40m. [31m* [1;35m├=rom： [37mallan.cs.nthu.edu.tw [m
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ぃ熬み~
Oct 31, 2013, 4:32:59 AM10/31/13
to
清華大學資訊工程學系博士學位口試
學生姓名： 黃筌敬
指導教授： 唐傳義 老師
指導委員：
口試委員： 韓永楷 老師、盧錦隆 老師、林俊淵 老師、葉信宏 老師
日期時間： 民國 102 年 11 月 07 日 13 時 30 分
口試地點： 清華大學台達館 629 會議室
口試題目： Applying Structural Domain Information for Enzyme Reaction Annotation
and Protein-Protein Interaction Inference
應用蛋白質片段資訊進行酵素功能標註及推論蛋白質間交互作用關係
論文摘要：
Domains are fundamental building blocks of proteins which perform a variety of
functions within living organisms, including catalysis, signal transduction,
and transport of nutrients. The majority of proteins are composed of more than
two domains that recognize and bind structural units in other proteins through
protein-protein interactions. This dissertation uses the nature of domains in
the proteins to investigate two main topics including “enzyme reaction
學生姓名： 黃筌敬
指導教授： 唐傳義 老師
指導委員：
口試委員： 韓永楷 老師、盧錦隆 老師、林俊淵 老師、葉信宏 老師
日期時間： 民國 102 年 11 月 07 日 13 時 30 分
口試地點： 清華大學台達館 629 會議室
口試題目： Applying Structural Domain Information for Enzyme Reaction Annotation
and Protein-Protein Interaction Inference
應用蛋白質片段資訊進行酵素功能標註及推論蛋白質間交互作用關係
論文摘要：
Domains are fundamental building blocks of proteins which perform a variety of
functions within living organisms, including catalysis, signal transduction,
and transport of nutrients. The majority of proteins are composed of more than
two domains that recognize and bind structural units in other proteins through
protein-protein interactions. This dissertation uses the nature of domains in
prediction based on the domain architecture of an enzyme” and “inferring
protein-protein interactions (PPIs) from domain-domain interactions (DDIs)”.
The gap between novel protein sequences and characterized functions has been
widened according to the advent of high-throughput genome sequencing techniques
in the post-genomics era. To identify functions of a protein from manually
curated sequence annotation is a challenging task; therefore, automated protein
function prediction techniques are necessary. The enzyme nomenclature proposed
by the International Union of Biochemistry and Molecular Biology has provided
a well-defined four-field number on enzyme classification. The first three
numbers of an enzyme reaction describe the overtype of enzymatic reaction,
and the last number denotes the substrate specificity of a reaction. Proteins
are grouped into two data sets, comprising the 3-numerical-block set and
the 4-numerical-block set. According to whether the protein performed more than
one enzymatic reaction, each data set was further divided into single-EC cases
and multiple-EC cases. For the case of single-EC, the fractions of entries
correctly classified using the well-known association rule method reached 96%
and 91% accuracy for the 3-numerical-block set and the 4-numerical-block set,
respectively. The proposed enzyme reaction prediction (ERP) method showed
marginally higher accuracy, with 99% and 92% separately. It is more difficult
to predict multiple enzymatic activities for a single protein of the multiple-EC
case. For the case of multiple-EC, the fractions of entries correctly predicted
for 3-numerical-block set and the 4-numerical-block sets were 17% and 8%,
in the post-genomics era. To identify functions of a protein from manually
curated sequence annotation is a challenging task; therefore, automated protein
function prediction techniques are necessary. The enzyme nomenclature proposed
by the International Union of Biochemistry and Molecular Biology has provided
a well-defined four-field number on enzyme classification. The first three
numbers of an enzyme reaction describe the overtype of enzymatic reaction,
and the last number denotes the substrate specificity of a reaction. Proteins
are grouped into two data sets, comprising the 3-numerical-block set and
the 4-numerical-block set. According to whether the protein performed more than
one enzymatic reaction, each data set was further divided into single-EC cases
and multiple-EC cases. For the case of single-EC, the fractions of entries
correctly classified using the well-known association rule method reached 96%
and 91% accuracy for the 3-numerical-block set and the 4-numerical-block set,
respectively. The proposed enzyme reaction prediction (ERP) method showed
marginally higher accuracy, with 99% and 92% separately. It is more difficult
to predict multiple enzymatic activities for a single protein of the multiple-EC
case. For the case of multiple-EC, the fractions of entries correctly predicted
ぃ熬み~
Oct 31, 2013, 9:28:36 AM10/31/13
to
清華大學資訊工程學系博士學位口試
學生姓名： 黃筌敬
指導教授： 唐傳義 老師
指導委員：
口試委員： 韓永楷 老師、盧錦隆 老師、林俊淵 老師、葉信宏 老師
日期時間： 民國 102 年 11 月 07 日 13 時 30 分
口試地點： 清華大學台達館 629 會議室
口試題目： Applying Structural Domain Information for Enzyme Reaction Annotation
and Protein-Protein Interaction Inference
應用蛋白質片段資訊進行酵素功能標註及推論蛋白質間交互作用關係
論文摘要：
Domains are fundamental building blocks of proteins which perform a variety of
functions within living organisms, including catalysis, signal transduction,
and transport of nutrients. The majority of proteins are composed of more than
two domains that recognize and bind structural units in other proteins through
protein-protein interactions. This dissertation uses the nature of domains in
the proteins to investigate two main topics including “enzyme reaction
prediction based on the domain architecture of an enzyme” and “inferring
protein-protein interactions (PPIs) from domain-domain interactions (DDIs)”.
The gap between novel protein sequences and characterized protein functions
學生姓名： 黃筌敬
指導教授： 唐傳義 老師
指導委員：
口試委員： 韓永楷 老師、盧錦隆 老師、林俊淵 老師、葉信宏 老師
日期時間： 民國 102 年 11 月 07 日 13 時 30 分
口試地點： 清華大學台達館 629 會議室
口試題目： Applying Structural Domain Information for Enzyme Reaction Annotation
and Protein-Protein Interaction Inference
應用蛋白質片段資訊進行酵素功能標註及推論蛋白質間交互作用關係
論文摘要：
Domains are fundamental building blocks of proteins which perform a variety of
functions within living organisms, including catalysis, signal transduction,
and transport of nutrients. The majority of proteins are composed of more than
two domains that recognize and bind structural units in other proteins through
protein-protein interactions. This dissertation uses the nature of domains in
the proteins to investigate two main topics including “enzyme reaction
prediction based on the domain architecture of an enzyme” and “inferring
protein-protein interactions (PPIs) from domain-domain interactions (DDIs)”.
has been widened according to the advent of high-throughput genome sequencing
techniques in the post-genomics era. To identify functions of a protein from
manually curated sequence annotation is a challenging task; therefore,
automated protein function prediction techniques are necessary. The enzyme
nomenclature proposed by the International Union of Biochemistry and Molecular
Biology has provided a well-defined four-field number on enzyme classification.
The first three numbers of an enzyme reaction describe the overtype of enzymatic
reaction, and the last number denotes the substrate specificity of a reaction.
Proteins are grouped into two data sets, comprising the 3-numerical-block set
and the 4-numerical-block set. According to whether the protein performed
more than one enzymatic reaction, each data set was further divided into
single-EC cases and multiple-EC cases. For the case of single-EC, the fractions
of entries correctly classified using the well-known association rule method
reached 96% and 91% accuracy for the 3-numerical-block set and
the 4-numerical-block set, respectively. The proposed enzyme reaction prediction
(ERP) method showed marginally higher accuracy, with 99% and 92% separately.
It is more difficult to predict multiple enzymatic activities for a single
protein. For the case of multiple-EC, the fractions of entries correctly
techniques in the post-genomics era. To identify functions of a protein from
manually curated sequence annotation is a challenging task; therefore,
automated protein function prediction techniques are necessary. The enzyme
nomenclature proposed by the International Union of Biochemistry and Molecular
Biology has provided a well-defined four-field number on enzyme classification.
The first three numbers of an enzyme reaction describe the overtype of enzymatic
reaction, and the last number denotes the substrate specificity of a reaction.
Proteins are grouped into two data sets, comprising the 3-numerical-block set
and the 4-numerical-block set. According to whether the protein performed
more than one enzymatic reaction, each data set was further divided into
single-EC cases and multiple-EC cases. For the case of single-EC, the fractions
of entries correctly classified using the well-known association rule method
reached 96% and 91% accuracy for the 3-numerical-block set and
the 4-numerical-block set, respectively. The proposed enzyme reaction prediction
(ERP) method showed marginally higher accuracy, with 99% and 92% separately.
It is more difficult to predict multiple enzymatic activities for a single
predicted for the 3-numerical-block set and the 4-numerical-block set were 17%
[;32m◆ From: WillJor @ allan.cs.nthu.edu.tw [m
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