The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Yuanfang Guan - One of the best experts on this subject based on the ideXlab platform.
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Brain-specific Functional Relationship networks inform autism spectrum disorder gene prediction.
Translational psychiatry, 2018Co-Authors: Marlena Duda, Hongjiu Zhang, Dennis P. Wall, Margit Burmeister, Yuanfang GuanAbstract:Autism spectrum disorder (ASD) is a neuropsychiatric disorder with strong evidence of genetic contribution, and increased research efforts have resulted in an ever-growing list of ASD candidate genes. However, only a fraction of the hundreds of nominated ASD-related genes have identified de novo or transmitted loss of function (LOF) mutations that can be directly attributed to the disorder. For this reason, a means of prioritizing candidate genes for ASD would help filter out false-positive results and allow researchers to focus on genes that are more likely to be causative. Here we constructed a machine learning model by leveraging a brain-specific Functional Relationship network (FRN) of genes to produce a genome-wide ranking of ASD risk genes. We rigorously validated our gene ranking using results from two independent sequencing experiments, together representing over 5000 simplex and multiplex ASD families. Finally, through Functional enrichment analysis on our highly prioritized candidate gene network, we identified a small number of pathways that are key in early neural development, providing further support for their potential role in ASD.
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Algorithms for modeling global and context-specific Functional Relationship networks
Briefings in bioinformatics, 2015Co-Authors: Zhu Fan, Bharat Panwar, Yuanfang GuanAbstract:Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling Functional Relationship networks, which summarize the probability of gene co-Functionality Relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the Functional Relationship networks, while most of them target the global (non-context-specific) Functional Relationship networks. However, it is expected that Functional Relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art Functional Relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for Functional Relationship networks.
Zhu Fan - One of the best experts on this subject based on the ideXlab platform.
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Algorithms for modeling global and context-specific Functional Relationship networks
Briefings in bioinformatics, 2015Co-Authors: Zhu Fan, Bharat Panwar, Yuanfang GuanAbstract:Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling Functional Relationship networks, which summarize the probability of gene co-Functionality Relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the Functional Relationship networks, while most of them target the global (non-context-specific) Functional Relationship networks. However, it is expected that Functional Relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art Functional Relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for Functional Relationship networks.
Bharat Panwar - One of the best experts on this subject based on the ideXlab platform.
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Algorithms for modeling global and context-specific Functional Relationship networks
Briefings in bioinformatics, 2015Co-Authors: Zhu Fan, Bharat Panwar, Yuanfang GuanAbstract:Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling Functional Relationship networks, which summarize the probability of gene co-Functionality Relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the Functional Relationship networks, while most of them target the global (non-context-specific) Functional Relationship networks. However, it is expected that Functional Relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art Functional Relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for Functional Relationship networks.
Marlena Duda - One of the best experts on this subject based on the ideXlab platform.
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Brain-specific Functional Relationship networks inform autism spectrum disorder gene prediction.
Translational psychiatry, 2018Co-Authors: Marlena Duda, Hongjiu Zhang, Dennis P. Wall, Margit Burmeister, Yuanfang GuanAbstract:Autism spectrum disorder (ASD) is a neuropsychiatric disorder with strong evidence of genetic contribution, and increased research efforts have resulted in an ever-growing list of ASD candidate genes. However, only a fraction of the hundreds of nominated ASD-related genes have identified de novo or transmitted loss of function (LOF) mutations that can be directly attributed to the disorder. For this reason, a means of prioritizing candidate genes for ASD would help filter out false-positive results and allow researchers to focus on genes that are more likely to be causative. Here we constructed a machine learning model by leveraging a brain-specific Functional Relationship network (FRN) of genes to produce a genome-wide ranking of ASD risk genes. We rigorously validated our gene ranking using results from two independent sequencing experiments, together representing over 5000 simplex and multiplex ASD families. Finally, through Functional enrichment analysis on our highly prioritized candidate gene network, we identified a small number of pathways that are key in early neural development, providing further support for their potential role in ASD.
Yerko Moreno - One of the best experts on this subject based on the ideXlab platform.
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Biosynthesis of Methoxypyrazines: Elucidating the Structural/ Functional Relationship of Two Vitis vinifera O-Methyltransferases Capable of Catalyzing the Putative Final Step of the Biosynthesis of 3-Alkyl-2-Methoxypyrazine
Journal of agricultural and food chemistry, 2011Co-Authors: José G. Vallarino, Xaviera A. López-cortés, Jake D. Dunlevy, Paul K. Boss, Fernando D. González-nilo, Yerko MorenoAbstract:3-Alkyl-2-methoxypyrazines (MPs) are an important food constituent and have been associated with detrimental herbaceous flavors in red wines by consumers and the wine industry. The Vitis vinifera genes O-methyltransferase 1 and 2 (VvOMT1 and VvOMT2) have been isolated in the grapevine cultivar Carmenere. These genes encode S-adenosyl-l-methionine (SAM)-dependent O-methyltransferases, which have the ability to methylate 3-alkyl-2-hydroxypyrazines (HPs)—the putative final step in MPs production. Atomic studies were performed in order to explain the differences in these VvOMT activities through their structural/Functional Relationship in MPs biosynthesis. Differences in enthalpy energy observed between the proteins may be due to changes of equivalent residues in the active sites of VvOMT1 (F319, L322) and VvOMT2 (L319, V322). However, docking simulations and QM/MM analyses described how residues H272 and M182 could explain the main Functional differentiation observed between VvOMT1 and VvOMT2 through steric ...
