Abhishek Tiwari:SYSTEMS BIOLOGY - Revision history

Abhishektiwari: /* SYSTEMS BIOLOGY */

Abhishektiwari — Sun, 08 Jun 2008 08:22:05 GMT

SYSTEMS BIOLOGY

←Older revision		Revision as of 08:22, 8 June 2008
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	Mining the hereditary disease-genes from human genome is one of the most important tasks in bioinformatics research. A variety of sequence features and functional similarities between known human hereditary disease-genes and those not known to be involved in disease have been systematically examined and efficient classifiers have been constructed based on the identified common patterns. The availability of human genome-wide protein–protein interactions (PPIs) provides us with new opportunity for discovering hereditary disease-genes by topological features in PPIs network.Analysis reveals that the hereditary disease-genes ascertained from OMIM in the literature-curated (LC) PPIs network are characterized by a larger degree, tendency to interact with other disease-genes, more common neighbors and quick communication to each other whereas those properties could not be detected from the network identified from high-throughput yeast two-hybrid mapping approach (EXP) and predicted interactions (PDT) PPIs network. KNN classifier based on those features was created and on average gained overall prediction accuracy of 0.76 in cross-validation test. Then the classifier was applied to 5262 genes on human genome and predicted 178 novel disease-genes. Some of the predictions have been validated by biological experiments.		Mining the hereditary disease-genes from human genome is one of the most important tasks in bioinformatics research. A variety of sequence features and functional similarities between known human hereditary disease-genes and those not known to be involved in disease have been systematically examined and efficient classifiers have been constructed based on the identified common patterns. The availability of human genome-wide protein–protein interactions (PPIs) provides us with new opportunity for discovering hereditary disease-genes by topological features in PPIs network.Analysis reveals that the hereditary disease-genes ascertained from OMIM in the literature-curated (LC) PPIs network are characterized by a larger degree, tendency to interact with other disease-genes, more common neighbors and quick communication to each other whereas those properties could not be detected from the network identified from high-throughput yeast two-hybrid mapping approach (EXP) and predicted interactions (PDT) PPIs network. KNN classifier based on those features was created and on average gained overall prediction accuracy of 0.76 in cross-validation test. Then the classifier was applied to 5262 genes on human genome and predicted 178 novel disease-genes. Some of the predictions have been validated by biological experiments.
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Abhishektiwari at 13:19, 2 October 2007

Abhishektiwari — Tue, 02 Oct 2007 13:19:32 GMT

←Older revision		Revision as of 13:19, 2 October 2007
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Abhishektiwari at 10:08, 24 April 2007

Abhishektiwari — Tue, 24 Apr 2007 10:08:35 GMT

←Older revision		Revision as of 10:08, 24 April 2007
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	= SYSTEMS BIOLOGY =		= SYSTEMS BIOLOGY =

Abhishektiwari at 10:07, 24 April 2007

Abhishektiwari — Tue, 24 Apr 2007 10:07:31 GMT

←Older revision		Revision as of 10:07, 24 April 2007
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	<center>		<center>
	[[Abhishek Tiwari \| <font face="trebuchet ms" style="color:#ffffff"> '''Home''' </font>]]		[[Abhishek Tiwari \| <font face="trebuchet ms" style="color:#ffffff"> '''Home''' </font>]]
-	[[Abhishek Tiwari:~~Contact~~ \| <font face="trebuchet ms" style="color:#ffffff"> '''~~Contact~~''' </font>]]	+	[[Abhishek Tiwari:About \| <font face="trebuchet ms" style="color:#ffffff"> '''About''' </font>]]
-	[[Abhishek Tiwari:~~iCODONS~~ \| <font face="trebuchet ms" style="color:#ffffff"> '''~~iCODONS~~''' </font>]]	+	[[Abhishek Tiwari:Resources\| <font face="trebuchet ms" style="color:#ffffff"> '''Resources''' </font>]]
-	[[Abhishek Tiwari:~~Reprints~~ \| <font face="trebuchet ms" style="color:#ffffff"> '''~~Publications''' </font>]]       ~~	+	[[Abhishek Tiwari:Projects \| <font face="trebuchet ms" style="color:#ffffff"> '''Research & Projects''' </font>]]
-	~~[[Abhishek Tiwari:~~Research ~~\| <font face="trebuchet ms" style="color:#ffffff"> '''Research''' </font>]]~~ &~~nbsp;     ~~	+
-	~~[[Abhishek Tiwari:Projects \| <font face="trebuchet ms" style="color:#ffffff"> '''~~Projects''' </font>]]	+

	[[Abhishek Tiwari:Softwares \| <font face="trebuchet ms" style="color:#ffffff"> '''Softwares''' </font>]]		[[Abhishek Tiwari:Softwares \| <font face="trebuchet ms" style="color:#ffffff"> '''Softwares''' </font>]]
-	[[Abhishek Tiwari:~~Links~~ \| <font face="trebuchet ms" style="color:#ffffff"> '''Links''' </font>]]	+	[[Abhishek Tiwari:Reprints \| <font face="trebuchet ms" style="color:#ffffff"> '''Publications''' </font>]]
		+	[[Abhishek Tiwari:Links \| <font face="trebuchet ms" style="color:#ffffff"> '''ImpLinks''' </font>]]
		+	[[Abhishek Tiwari:Contact \| <font face="trebuchet ms" style="color:#ffffff"> '''Contact''' </font>]]
	</center>		</center>
	</div>		</div>
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	= SYSTEMS BIOLOGY =		= SYSTEMS BIOLOGY =

Abhishektiwari: /* SYSTEMS BIOLOGY */

Abhishektiwari — Fri, 23 Feb 2007 08:38:06 GMT

SYSTEMS BIOLOGY

←Older revision		Revision as of 08:38, 23 February 2007
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	'''Synopsis'''		'''Synopsis'''

-	Authors identify linear Gaussian dynamic Bayesian networks and variable selection based on F-statistics as suitable methods for the reconstruction of gene-regulatory networks from time series data. Commonly used discrete dynamic Bayesian networks perform inferior and this result can be attributed to the inevitable information loss by discretization of expression data. It is shown that short time series generated under transcription factor knock-out are optimal experiments in order to reveal the structure of gene regulatory networks. The benefit of using of prior knowledge within a Bayesian learning framework is found to be limited to conditions of small gene expression data size. Unobserved processes, like protein-protein interactions, induce dependencies between gene expression levels similar to direct transcriptional regulation. Authors show that these dependencies cannot be distinguished from transcription factor mediated gene regulation on the basis of gene expression data alone.	+	This study focusses on time series gene expression and gene knock-out data in order to identify the underlying network structure.Authors identify linear Gaussian dynamic Bayesian networks and variable selection based on F-statistics as suitable methods for the reconstruction of gene-regulatory networks from time series data. Commonly used discrete dynamic Bayesian networks perform inferior and this result can be attributed to the inevitable information loss by discretization of expression data. It is shown that short time series generated under transcription factor knock-out are optimal experiments in order to reveal the structure of gene regulatory networks. The benefit of using of prior knowledge within a Bayesian learning framework is found to be limited to conditions of small gene expression data size. Unobserved processes, like protein-protein interactions, induce dependencies between gene expression levels similar to direct transcriptional regulation. Authors show that these dependencies cannot be distinguished from transcription factor mediated gene regulation on the basis of gene expression data alone.

Abhishektiwari: /* SYSTEMS BIOLOGY */

Abhishektiwari — Fri, 23 Feb 2007 08:37:08 GMT

SYSTEMS BIOLOGY

←Older revision		Revision as of 08:37, 23 February 2007
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	A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which include several complementary computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, have become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in neurons.A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical formulas. Dynamics on several levels, from molecular pathways to clinical observations, can be seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.		A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which include several complementary computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, have become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in neurons.A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical formulas. Dynamics on several levels, from molecular pathways to clinical observations, can be seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.
		+
		+
		+	'''''BMC Systems Biology''''' Volume 1 \| FEBRUARY 2007
		+	*'''Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge'''
		+
		+	'''Synopsis'''
		+
		+	Authors identify linear Gaussian dynamic Bayesian networks and variable selection based on F-statistics as suitable methods for the reconstruction of gene-regulatory networks from time series data. Commonly used discrete dynamic Bayesian networks perform inferior and this result can be attributed to the inevitable information loss by discretization of expression data. It is shown that short time series generated under transcription factor knock-out are optimal experiments in order to reveal the structure of gene regulatory networks. The benefit of using of prior knowledge within a Bayesian learning framework is found to be limited to conditions of small gene expression data size. Unobserved processes, like protein-protein interactions, induce dependencies between gene expression levels similar to direct transcriptional regulation. Authors show that these dependencies cannot be distinguished from transcription factor mediated gene regulation on the basis of gene expression data alone.

Abhishektiwari: /* SYSTEMS BIOLOGY */

Abhishektiwari — Fri, 23 Feb 2007 08:34:48 GMT

SYSTEMS BIOLOGY

←Older revision		Revision as of 08:34, 23 February 2007
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	= SYSTEMS BIOLOGY =		= SYSTEMS BIOLOGY =

-	'''''BMC ~~Bioinformatics~~''''' Volume 7 \| FEBRUARY 2007	+	'''''BMC Systems Biology''''' Volume 1 \| FEBRUARY 2007
	*'''Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery'''		*'''Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery'''

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-	'''''BMC ~~Bioinformatics~~''''' Volume 7 \| FEBRUARY 2007	+	'''''BMC Systems Biology''''' Volume 1 \| FEBRUARY 2007
	*'''Classification of microarray data using gene networks'''		*'''Classification of microarray data using gene networks'''

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-	'''''BMC ~~Bioinformatics~~''''' Volume 7 \| JANUARY 2007	+	'''''BMC Systems Biology''''' Volume 1 \| JANUARY 2007
	*'''Identification of functional modules using network topology and high-throughput data'''		*'''Identification of functional modules using network topology and high-throughput data'''

Abhishektiwari: /* SYSTEMS BIOLOGY */

Abhishektiwari — Fri, 23 Feb 2007 08:33:40 GMT

SYSTEMS BIOLOGY

←Older revision		Revision as of 08:33, 23 February 2007
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	A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which include several complementary computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, have become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in neurons.A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical formulas. Dynamics on several levels, from molecular pathways to clinical observations, can be seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.		A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which include several complementary computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, have become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in neurons.A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical formulas. Dynamics on several levels, from molecular pathways to clinical observations, can be seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.
		+

	'''''BMC Bioinformatics''''' Volume 7 \| FEBRUARY 2007		'''''BMC Bioinformatics''''' Volume 7 \| FEBRUARY 2007

Abhishektiwari: /* SYSTEMS BIOLOGY */

Abhishektiwari — Fri, 23 Feb 2007 08:33:24 GMT

SYSTEMS BIOLOGY

←Older revision		Revision as of 08:33, 23 February 2007
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	= SYSTEMS BIOLOGY =		= SYSTEMS BIOLOGY =
		+
		+	'''''BMC Bioinformatics''''' Volume 7 \| FEBRUARY 2007
		+	*'''Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery'''
		+
		+	'''Synopsis'''
		+
		+	A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which include several complementary computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, have become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in neurons.A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical formulas. Dynamics on several levels, from molecular pathways to clinical observations, can be seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.

	'''''BMC Bioinformatics''''' Volume 7 \| FEBRUARY 2007		'''''BMC Bioinformatics''''' Volume 7 \| FEBRUARY 2007

Abhishektiwari: /* SYSTEMS BIOLOGY */

Abhishektiwari — Fri, 23 Feb 2007 08:10:47 GMT

SYSTEMS BIOLOGY

←Older revision		Revision as of 08:10, 23 February 2007
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	Authors have reported a novel computational technique for the integrated analysis of network and similarity data. The method is aimed to dissect together topological properties of gene or protein networks and other high-throughput data. Method was used to analyze large-scale protein interaction networks and genome-wide transcription profiles in yeast and human. The method was shown to identify functionally sound modules, i.e., connected subnetworks with highly coherent expression showing significant functional enrichment. In comparison to the extant Co-clustering method, which aims to integrate similar data, our method demonstrated substantial improvement in solution quality. Comparison to solutions produced by clustering highlights the advantage of utilizing topological connectivity in the hunt for functionally sound modules. By construction, our method is specifically powerful in detection of regulatory modules, and less fit for detection of metabolic modules. Our technique, implemented in the program MATISSE, is efficient and can analyze genome-scale interaction and expression data within minutes.The proposed algorithm is very flexible and – unlike Co-clustering – can handle situations where not all genes in the network have similarity information or expression patterns. In particular, MATISSE can determine the subset on which similarity is computed using various criteria, e.g., initial probe filtering, differential expression confidence values, etc.		Authors have reported a novel computational technique for the integrated analysis of network and similarity data. The method is aimed to dissect together topological properties of gene or protein networks and other high-throughput data. Method was used to analyze large-scale protein interaction networks and genome-wide transcription profiles in yeast and human. The method was shown to identify functionally sound modules, i.e., connected subnetworks with highly coherent expression showing significant functional enrichment. In comparison to the extant Co-clustering method, which aims to integrate similar data, our method demonstrated substantial improvement in solution quality. Comparison to solutions produced by clustering highlights the advantage of utilizing topological connectivity in the hunt for functionally sound modules. By construction, our method is specifically powerful in detection of regulatory modules, and less fit for detection of metabolic modules. Our technique, implemented in the program MATISSE, is efficient and can analyze genome-scale interaction and expression data within minutes.The proposed algorithm is very flexible and – unlike Co-clustering – can handle situations where not all genes in the network have similarity information or expression patterns. In particular, MATISSE can determine the subset on which similarity is computed using various criteria, e.g., initial probe filtering, differential expression confidence values, etc.
		+

	'''''Oxford Bioinformatics''''' Volume 22 \| Number 18 \| 15 November 2006		'''''Oxford Bioinformatics''''' Volume 22 \| Number 18 \| 15 November 2006

←Older revision		Revision as of 10:07, 24 April 2007
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	<center>		<center>
	[[Abhishek Tiwari \| <font face="trebuchet ms" style="color:#ffffff"> '''Home''' </font>]]		[[Abhishek Tiwari \| <font face="trebuchet ms" style="color:#ffffff"> '''Home''' </font>]]
-	[[Abhishek Tiwari:~~Contact~~ \| <font face="trebuchet ms" style="color:#ffffff"> '''~~Contact~~''' </font>]]	+	[[Abhishek Tiwari:About \| <font face="trebuchet ms" style="color:#ffffff"> '''About''' </font>]]
-	[[Abhishek Tiwari:~~iCODONS~~ \| <font face="trebuchet ms" style="color:#ffffff"> '''~~iCODONS~~''' </font>]]	+	[[Abhishek Tiwari:Resources\| <font face="trebuchet ms" style="color:#ffffff"> '''Resources''' </font>]]
-	[[Abhishek Tiwari:~~Reprints~~ \| <font face="trebuchet ms" style="color:#ffffff"> '''~~Publications''' </font>]]       ~~	+	[[Abhishek Tiwari:Projects \| <font face="trebuchet ms" style="color:#ffffff"> '''Research & Projects''' </font>]]
-	~~[[Abhishek Tiwari:~~Research ~~\| <font face="trebuchet ms" style="color:#ffffff"> '''Research''' </font>]]~~ &~~nbsp;     ~~	+
-	~~[[Abhishek Tiwari:Projects \| <font face="trebuchet ms" style="color:#ffffff"> '''~~Projects''' </font>]]	+

	[[Abhishek Tiwari:Softwares \| <font face="trebuchet ms" style="color:#ffffff"> '''Softwares''' </font>]]		[[Abhishek Tiwari:Softwares \| <font face="trebuchet ms" style="color:#ffffff"> '''Softwares''' </font>]]
-	[[Abhishek Tiwari:~~Links~~ \| <font face="trebuchet ms" style="color:#ffffff"> '''Links''' </font>]]	+	[[Abhishek Tiwari:Reprints \| <font face="trebuchet ms" style="color:#ffffff"> '''Publications''' </font>]]
		+	[[Abhishek Tiwari:Links \| <font face="trebuchet ms" style="color:#ffffff"> '''ImpLinks''' </font>]]
		+	[[Abhishek Tiwari:Contact \| <font face="trebuchet ms" style="color:#ffffff"> '''Contact''' </font>]]
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	= SYSTEMS BIOLOGY =		= SYSTEMS BIOLOGY =