The Experts below are selected from a list of 24465 Experts worldwide ranked by ideXlab platform
Rafeef Abugharbieh - One of the best experts on this subject based on the ideXlab platform.
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Coactivated Clique Based Multisource Overlapping Brain Subnetwork Extraction
arXiv: Neurons and Cognition, 2018Co-Authors: Chendi Wang, Rafeef AbugharbiehAbstract:Subnetwork extraction using community detection methods is commonly used to study the brain's modular structure. Recent studies indicated that certain brain regions are known to interact with multiple Subnetworks. However, most existing methods are mainly for non-overlapping Subnetwork extraction. In this paper, we present an approach for overlapping brain Subnetwork extraction using cliques, which we defined as co-activated node groups performing multiple tasks. We proposed a multisource Subnetwork extraction approach based on the co-activated clique, which (1) uses task co-activation and task connectivity strength information for clique identification, (2) automatically detects cliques of different sizes having more neuroscientific justifications, and (3) shares the Subnetwork membership, derived from a fusion of rest and task data, among the nodes within a clique for overlapping Subnetwork extraction. On real data, compared to the commonly used overlapping community detection techniques, we showed that our approach improved Subnetwork extraction in terms of group-level and subject-wise reproducibility. We also showed that our multisource approach identified Subnetwork overlaps within brain regions that matched well with hubs defined using functional and anatomical information, which enables us to study the interactions between the Subnetworks and how hubs play their role in information flow across different Subnetworks. We further demonstrated that the assignments of interacting/individual nodes using our approach correspond with the posterior probability derived independently from our multimodal random walker based approach.
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Hypergraph based Subnetwork Extraction using Fusion of Task and Rest Functional Connectivity
arXiv: Neurons and Cognition, 2018Co-Authors: Chendi Wang, Rafeef AbugharbiehAbstract:Functional Subnetwork extraction is commonly used to explore the brain's modular structure. However, reliable Subnetwork extraction from functional magnetic resonance imaging (fMRI) data remains challenging due to the pronounced noise in neuroimaging data. In this paper, we proposed a high order relation informed approach based on hypergraph to combine the information from multi-task data and resting state data to improve Subnetwork extraction. Our assumption is that task data can be beneficial for the Subnetwork extraction process, since the repeatedly activated nodes involved in diverse tasks might be the canonical network components which comprise pre-existing repertoires of resting state Subnetworks. Our proposed high order relation informed Subnetwork extraction based on a strength information embedded hypergraph, (1) facilitates the multisource integration for Subnetwork extraction, (2) utilizes information on relationships and changes between the nodes across different tasks, and (3) enables the study on higher order relations among brain network nodes. On real data, we demonstrated that fusing task activation, task-induced connectivity and resting state functional connectivity based on hypergraphs improves Subnetwork extraction compared to employing a single source from either rest or task data in terms of Subnetwork modularity measure, inter-subject reproducibility, along with more biologically meaningful Subnetwork assignments.
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multimodal brain Subnetwork extraction using provincial hub guided random walks
International Conference Information Processing, 2017Co-Authors: Chendi Wang, Rafeef AbugharbiehAbstract:Community detection methods have been widely used for studying the modular structure of the brain. However, few of these methods exploit the intrinsic properties of brain networks other than modularity to tackle the pronounced noise in neuroimaging data. We propose a random walker (RW) based approach that reflects how regions of a brain Subnetwork tend to be inter-linked by a provincial hub. By using provincial hubs to guide seed setting, RW provides the exact posterior probability of a brain region belonging to each given Subnetwork, which mitigates forced hard assignments of brain regions to Subnetworks as is the case in most existing methods. We further present an extension that enables multimodal integration for exploiting complementary information from functional Magnetic Resonance Imaging (fMRI) and diffusion MRI (dMRI) data. On synthetic data, our approach achieves higher accuracy in Subnetwork extraction than unimodal and existing multimodal approaches. On real data from the Human Connectome Project (HCP), our estimated Subnetworks match well with established brain systems and attain higher inter-subject reproducibility.
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MICCAI (1) - Modularity Reinforcement for Improving Brain Subnetwork Extraction
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, 2016Co-Authors: Chendi Wang, Rafeef AbugharbiehAbstract:Functional Subnetwork extraction is commonly employed to study the brain’s modular structure. However, reliable extraction from functional magnetic resonance imaging (fMRI) data remains challenging. As representations of brain networks, brain graph estimates are typically noisy due to the pronounced noise in fMRI data. Also, confounds, such as region size bias, motion artifacts, and signal dropout, introduce region-specific bias in connectivity, e.g. a node in a signal dropout area tends to display lower connectivity. The traditional approach of global thresholding might thus remove relevant edges that have low connectivity due to confounds, resulting in erroneous Subnetwork extraction. In this paper, we present a modularity reinforcement strategy that deals with the above two challenges. Specifically, we propose a local thresholding scheme that accounts for region-specific connectivity bias when pruning noisy edges. From the resulting thresholded graph, we derive a node similarity measure by comparing the adjacency structure of each node, i.e. its connection fingerprint, with that of other nodes. Drawing on the intuition that nodes belonging to the same Subnetwork should have similar connection fingerprints, we refine the brain graph with this similarity measure to reinforce its modularity structure. On synthetic data, our strategy achieves higher accuracy in Subnetwork extraction compared to using standard brain graph estimates. On real data, Subnetworks extracted with our strategy attain higher overlaps with well-established brain systems and higher Subnetwork reproducibility across a range of graph densities. Our results thus demonstrate that modularity reinforcement with our strategy provides a clear gain in Subnetwork extraction.
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stable overlapping replicator dynamics for brain community detection
IEEE Transactions on Medical Imaging, 2016Co-Authors: Burak Yoldemir, Rafeef AbugharbiehAbstract:A fundamental means for understanding the brain's organizational structure is to group its spatially disparate regions into functional Subnetworks based on their interactions. Most community detection techniques are designed for generating partitions, but certain brain regions are known to interact with multiple Subnetworks. Thus, the brain's underlying Subnetworks necessarily overlap. In this paper, we propose a technique for identifying overlapping Subnetworks from weighted graphs with statistical control over false node inclusion. Our technique improves upon the replicator dynamics formulation by incorporating a graph augmentation strategy to enable Subnetwork overlaps, and a graph incrementation scheme for merging Subnetworks that might be falsely split by replicator dynamics due to its stringent mutual similarity criterion in defining Subnetworks. To statistically control for inclusion of false nodes into the detected Subnetworks, we further present a procedure for integrating stability selection into our Subnetwork identification technique. We refer to the resulting technique as stable overlapping replicator dynamics (SORD). Our experiments on synthetic data show significantly higher accuracy in Subnetwork identification with SORD than several state-of-the-art techniques. We also demonstrate higher test-retest reliability in multiple network measures on the Human Connectome Project data. Further, we illustrate that SORD enables identification of neuroanatomically-meaningful Subnetworks and network hubs.
Byungjun Yoon - One of the best experts on this subject based on the ideXlab platform.
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characterizing co expression networks underpinning maize stalk rot virulence in fusarium verticillioides through computational Subnetwork module analyses
Scientific Reports, 2018Co-Authors: Man S Kim, Huan Zhang, Huijuan Yan, Byungjun Yoon, Wonbo ShimAbstract:Fusarium verticillioides is recognized as an important stalk rot pathogen of maize worldwide, but our knowledge of genetic mechanisms underpinning this pathosystem is limited. Previously, we identified a striatin-like protein Fsr1 that plays an important role in stalk rot. To further characterize transcriptome networks downstream of Fsr1, we performed next-generation sequencing (NGS) to investigate relative read abundance and also to infer co-expression networks utilizing the preprocessed expression data through partial correlation. We used a probabilistic pathway activity inference strategy to identify functional Subnetwork modules likely involved in virulence. Each Subnetwork modules consisted of multiple correlated genes with coordinated expression patterns, but the collective activation levels were significantly different in F. verticillioides wild type versus fsr1 mutant. We also identified putative hub genes from predicted Subnetworks for functional validation and network robustness studies through mutagenesis, virulence and qPCR assays. Our results suggest that these genes are important virulence genes that regulate the expression of closely correlated genes, demonstrating that these are important hubs of their respective Subnetworks. Lastly, we used key F. verticillioides virulence genes to computationally predict a Subnetwork of maize genes that potentially respond to fungal genes by applying cointegration-correlation-expression strategy.
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characterizing co expression networks underpinning maize stalk rot virulence in fusarium verticillioides through computational Subnetwork module analyses
bioRxiv, 2017Co-Authors: Man S Kim, Huan Zhang, Huijuan Yan, Byungjun Yoon, Wonbo ShimAbstract:Fusarium verticillioides is recognized as an important stalk rot pathogen of maize worldwide, but our knowledge of genetic mechanisms underpinning this pathosystem is limited. Previously, we identified a striatin-like protein Fsr1 that plays an important role in stalk rot. To further characterize transcriptome networks downstream of Fsr1, we performed next-generation sequencing (NGS) to investigate relative read abundance and also to infer co-expression networks utilizing the preprocessed expression data through partial correlation. We used a probabilistic pathway activity inference strategy to identify functional Subnetwork modules likely involved in virulence. Each Subnetwork modules consisted of multiple correlated genes with coordinated expression patterns, but the collective activation levels were significantly different in F. verticillioides wild type versus the mutant. We also identified putative hub genes from predicted Subnetworks for functional validation and network robustness studies through mutagenesis, virulence and qPCR studies. Our results suggest that these genes are important virulence genes that regulate the expression of closely correlated genes, demonstrating that these are important hubs of their respective Subnetworks. Lastly, we used key F. verticillioides virulence genes to computationally predict a Subnetwork of maize genes that potentially respond to fungal genes by applying cointegration-correlation-expression strategy.
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Simultaneous identification of robust synergistic Subnetwork markers for effective cancer prognosis
EURASIP journal on bioinformatics & systems biology, 2014Co-Authors: Navadon Khunlertgit, Byungjun YoonAbstract:Accurate prediction of cancer prognosis based on gene expression data is generally difficult, and identifying robust prognostic markers for cancer remains a challenging problem. Recent studies have shown that modular markers, such as pathway markers and Subnetwork markers, can provide better snapshots of the underlying biological mechanisms by incorporating additional biological information, thereby leading to more accurate cancer classification. In this paper, we propose a novel method for simultaneously identifying robust synergistic Subnetwork markers that can accurately predict cancer prognosis. The proposed method utilizes an efficient message-passing algorithm called affinity propagation, based on which we identify groups – or Subnetworks – of discriminative and synergistic genes, whose protein products are closely located in the protein-protein interaction (PPI) network. Unlike other existing Subnetwork marker identification methods, our proposed method can simultaneously identify multiple nonoverlapping Subnetwork markers that can synergistically predict cancer prognosis. Evaluation results based on multiple breast cancer datasets demonstrate that the proposed message-passing approach can identify robust Subnetwork markers in the human PPI network, which have higher discriminative power and better reproducibility compared to those identified by previous methods. The identified Subnetwork makers can lead to better cancer classifiers with improved overall performance and consistency across independent cancer datasets.
Wonbo Shim - One of the best experts on this subject based on the ideXlab platform.
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characterizing co expression networks underpinning maize stalk rot virulence in fusarium verticillioides through computational Subnetwork module analyses
Scientific Reports, 2018Co-Authors: Man S Kim, Huan Zhang, Huijuan Yan, Byungjun Yoon, Wonbo ShimAbstract:Fusarium verticillioides is recognized as an important stalk rot pathogen of maize worldwide, but our knowledge of genetic mechanisms underpinning this pathosystem is limited. Previously, we identified a striatin-like protein Fsr1 that plays an important role in stalk rot. To further characterize transcriptome networks downstream of Fsr1, we performed next-generation sequencing (NGS) to investigate relative read abundance and also to infer co-expression networks utilizing the preprocessed expression data through partial correlation. We used a probabilistic pathway activity inference strategy to identify functional Subnetwork modules likely involved in virulence. Each Subnetwork modules consisted of multiple correlated genes with coordinated expression patterns, but the collective activation levels were significantly different in F. verticillioides wild type versus fsr1 mutant. We also identified putative hub genes from predicted Subnetworks for functional validation and network robustness studies through mutagenesis, virulence and qPCR assays. Our results suggest that these genes are important virulence genes that regulate the expression of closely correlated genes, demonstrating that these are important hubs of their respective Subnetworks. Lastly, we used key F. verticillioides virulence genes to computationally predict a Subnetwork of maize genes that potentially respond to fungal genes by applying cointegration-correlation-expression strategy.
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characterizing co expression networks underpinning maize stalk rot virulence in fusarium verticillioides through computational Subnetwork module analyses
bioRxiv, 2017Co-Authors: Man S Kim, Huan Zhang, Huijuan Yan, Byungjun Yoon, Wonbo ShimAbstract:Fusarium verticillioides is recognized as an important stalk rot pathogen of maize worldwide, but our knowledge of genetic mechanisms underpinning this pathosystem is limited. Previously, we identified a striatin-like protein Fsr1 that plays an important role in stalk rot. To further characterize transcriptome networks downstream of Fsr1, we performed next-generation sequencing (NGS) to investigate relative read abundance and also to infer co-expression networks utilizing the preprocessed expression data through partial correlation. We used a probabilistic pathway activity inference strategy to identify functional Subnetwork modules likely involved in virulence. Each Subnetwork modules consisted of multiple correlated genes with coordinated expression patterns, but the collective activation levels were significantly different in F. verticillioides wild type versus the mutant. We also identified putative hub genes from predicted Subnetworks for functional validation and network robustness studies through mutagenesis, virulence and qPCR studies. Our results suggest that these genes are important virulence genes that regulate the expression of closely correlated genes, demonstrating that these are important hubs of their respective Subnetworks. Lastly, we used key F. verticillioides virulence genes to computationally predict a Subnetwork of maize genes that potentially respond to fungal genes by applying cointegration-correlation-expression strategy.
Burak Yoldemir - One of the best experts on this subject based on the ideXlab platform.
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stable overlapping replicator dynamics for brain community detection
IEEE Transactions on Medical Imaging, 2016Co-Authors: Burak Yoldemir, Rafeef AbugharbiehAbstract:A fundamental means for understanding the brain's organizational structure is to group its spatially disparate regions into functional Subnetworks based on their interactions. Most community detection techniques are designed for generating partitions, but certain brain regions are known to interact with multiple Subnetworks. Thus, the brain's underlying Subnetworks necessarily overlap. In this paper, we propose a technique for identifying overlapping Subnetworks from weighted graphs with statistical control over false node inclusion. Our technique improves upon the replicator dynamics formulation by incorporating a graph augmentation strategy to enable Subnetwork overlaps, and a graph incrementation scheme for merging Subnetworks that might be falsely split by replicator dynamics due to its stringent mutual similarity criterion in defining Subnetworks. To statistically control for inclusion of false nodes into the detected Subnetworks, we further present a procedure for integrating stability selection into our Subnetwork identification technique. We refer to the resulting technique as stable overlapping replicator dynamics (SORD). Our experiments on synthetic data show significantly higher accuracy in Subnetwork identification with SORD than several state-of-the-art techniques. We also demonstrate higher test-retest reliability in multiple network measures on the Human Connectome Project data. Further, we illustrate that SORD enables identification of neuroanatomically-meaningful Subnetworks and network hubs.
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coupled stable overlapping replicator dynamics for multimodal brain Subnetwork identification
Information Processing in Medical Imaging, 2015Co-Authors: Burak Yoldemir, Rafeef AbugharbiehAbstract:Combining imaging modalities to synthesize their inherent strengths provides a promising means for improving brain Subnetwork identification. We propose a multimodal integration technique based on a sex-differentiated formulation of replicator dynamics for identifying Subnetworks of brain regions that exhibit high inter-connectivity both functionally and structurally. Our method has a number of desired properties, namely, it can operate on weighted graphs derived from functional magnetic resonance imaging (fMRI) and diffusion MRI (dMRI) data, allows for Subnetwork overlaps, has an intrinsic criterion for setting the number of Subnetworks, and provides statistical control on false node inclusion in the identified Subnetworks via the incorporation of stability selection. We thus refer to our technique as coupled stable overlapping replicator dynamics (CSORD). On synthetic data, we demonstrate that CSORD achieves significantly higher Subnetwork identification accuracy than state-of-the-art techniques. On real data from the Human Connectome Project (HCP), we show that CSORD attains improved test-retest reliability on multiple network measures and superior task classification accuracy.
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MICCAI (2) - Overlapping replicator dynamics for functional Subnetwork identification.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2013Co-Authors: Burak Yoldemir, Rafeef AbugharbiehAbstract:Functional magnetic resonance imaging (fMRI) has been widely used for inferring brain regions that tend to work in tandem and grouping them into Subnetworks. Despite that certain brain regions are known to interact with multiple Subnetworks, few existing techniques support identification of Subnetworks with overlaps. To address this limitation, we propose a novel approach based on replicator dynamics that facilitates detection of sparse overlapping Subnetworks. We refer to our approach as overlapping replicator dynamics (RDOL). On synthetic data, we show that RDOL achieves higher accuracy in Subnetwork identification than state-of-the-art methods. On real data, we demonstrate that RDOL is able to identify major functional hubs that are known to serve as communication channels between brain regions, in addition to detecting commonly observed functional Subnetworks. Moreover, we illustrate that knowing the Subnetwork overlaps enables inference of functional pathways, e.g. from primary sensory areas to the integration hubs.
Man S Kim - One of the best experts on this subject based on the ideXlab platform.
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characterizing co expression networks underpinning maize stalk rot virulence in fusarium verticillioides through computational Subnetwork module analyses
Scientific Reports, 2018Co-Authors: Man S Kim, Huan Zhang, Huijuan Yan, Byungjun Yoon, Wonbo ShimAbstract:Fusarium verticillioides is recognized as an important stalk rot pathogen of maize worldwide, but our knowledge of genetic mechanisms underpinning this pathosystem is limited. Previously, we identified a striatin-like protein Fsr1 that plays an important role in stalk rot. To further characterize transcriptome networks downstream of Fsr1, we performed next-generation sequencing (NGS) to investigate relative read abundance and also to infer co-expression networks utilizing the preprocessed expression data through partial correlation. We used a probabilistic pathway activity inference strategy to identify functional Subnetwork modules likely involved in virulence. Each Subnetwork modules consisted of multiple correlated genes with coordinated expression patterns, but the collective activation levels were significantly different in F. verticillioides wild type versus fsr1 mutant. We also identified putative hub genes from predicted Subnetworks for functional validation and network robustness studies through mutagenesis, virulence and qPCR assays. Our results suggest that these genes are important virulence genes that regulate the expression of closely correlated genes, demonstrating that these are important hubs of their respective Subnetworks. Lastly, we used key F. verticillioides virulence genes to computationally predict a Subnetwork of maize genes that potentially respond to fungal genes by applying cointegration-correlation-expression strategy.
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characterizing co expression networks underpinning maize stalk rot virulence in fusarium verticillioides through computational Subnetwork module analyses
bioRxiv, 2017Co-Authors: Man S Kim, Huan Zhang, Huijuan Yan, Byungjun Yoon, Wonbo ShimAbstract:Fusarium verticillioides is recognized as an important stalk rot pathogen of maize worldwide, but our knowledge of genetic mechanisms underpinning this pathosystem is limited. Previously, we identified a striatin-like protein Fsr1 that plays an important role in stalk rot. To further characterize transcriptome networks downstream of Fsr1, we performed next-generation sequencing (NGS) to investigate relative read abundance and also to infer co-expression networks utilizing the preprocessed expression data through partial correlation. We used a probabilistic pathway activity inference strategy to identify functional Subnetwork modules likely involved in virulence. Each Subnetwork modules consisted of multiple correlated genes with coordinated expression patterns, but the collective activation levels were significantly different in F. verticillioides wild type versus the mutant. We also identified putative hub genes from predicted Subnetworks for functional validation and network robustness studies through mutagenesis, virulence and qPCR studies. Our results suggest that these genes are important virulence genes that regulate the expression of closely correlated genes, demonstrating that these are important hubs of their respective Subnetworks. Lastly, we used key F. verticillioides virulence genes to computationally predict a Subnetwork of maize genes that potentially respond to fungal genes by applying cointegration-correlation-expression strategy.
