Mufassra Naz | Bonn Universität (original) (raw)

Papers by Mufassra Naz

Research paper thumbnail of Active and Passive Millimeter Wave Data Feature Level Fusion Based on WNN

Active and passive millimeter-wave decision fusion have expensive preprocessing and miss target i... more Active and passive millimeter-wave decision fusion have expensive preprocessing and miss target information.In order to solve this an active and passive millimeter-wave feature level fusion method is proposed based on WNN.Eiqenvalue is extracted from active and passive data and input to the WNN.Achieve the feature level fusion of active and passive data,identify the target in the WNN.Experiments and computer simulations show that,the recognition rate of feature level fusion based on WNN is higher than the recognition rate of decision level fusion based on D-S.

Research paper thumbnail of Featured Article Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis

Research paper thumbnail of Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis

Alzheimer's & dementia : the journal of the Alzheimer's Association, Jan 4, 2015

The discovery and development of new treatments for Alzheimer's disease (AD) requires a profo... more The discovery and development of new treatments for Alzheimer's disease (AD) requires a profound mechanistic understanding of the disease. Here, we propose a model-driven approach supporting the systematic identification of putative disease mechanisms. We have created a model for AD and a corresponding model for the normal physiology of neurons using biological expression language to systematically model causal and correlative relationships between biomolecules, pathways, and clinical readouts. Through model-model comparison we identify "chains of causal relationships" that lead to new insights into putative disease mechanisms. Using differential analysis of our models we identified a new mechanism explaining the effect of amyloid-beta on apoptosis via both the neurotrophic tyrosine kinase receptor, type 2 and nerve growth factor receptor branches of the neurotrophin signaling pathway. We also provide the example of a model-guided interpretation of genetic variation da...

Research paper thumbnail of GWAS genetic variant data and their integration in the context of network biology

Journal of Systems and Integrative Neuroscience

Regardless of the success of Genome Wide Association Studies (GWAS) to identify genetic variants ... more Regardless of the success of Genome Wide Association Studies (GWAS) to identify genetic variants associated with human diseases, investigating the molecular mechanisms and disease-associated genes linked to those genetic variants, is a very complex task. Specifically, where intergenic genetic variants are linked to the adjacent neighbouring genes. Consequently, the inference for the mechanistic connection between diseases and its susceptible genetic variants becomes more challenging. Functional genomics studies can support to reveal the significance of variants via intermediate molecular traits. Moreover, approaches like computational and bioinformatics predictions based on the variants location and its sequence attributes can assist to propose the candidate genes. As, the spectrum of potential functional consequences of variants is much broader; it still requires new methodologies to predict any molecular level perturbation. Thus, specialized algorithms and computable modelling approaches are essential, for the modelling and simulation of genetic regulatory networks. In this review, we are briefly summarizing all the existing methodologies for genome wide association studies, currently available algorithms and computable modelling approaches; moreover also emphasizing the required new approaches for modelling and simulations of genetic regulatory networks to predict the functional consequences of disease-associated genetic variants.

Research paper thumbnail of Systematic Analysis of GWAS Data Reveals Genomic Hotspots for Shared Mechanisms between Neurodegenerative Diseases

Journal of Alzheimers Disease & Parkinsonism, 2017

Objective: In this study, we have tried to reveal molecular mechanisms underlying “shared genetic... more Objective: In this study, we have tried to reveal molecular mechanisms underlying “shared genetic variants” and developed a strategy to identify candidate mechanisms for shared aetiology of a pair of diseases, to uncover biological relationships between quantitative traits or related neurodegenerative diseases. Methods: Genetic variants were collected from GWAS catalog, belonged to multiple disease association studies. Meta-analysis was performed by using Metal (a whole genome association analysis toolset), and normalized them for their different sample sizes. LD analysis was done with Haploreg DB V.4.0. Subsequently, the ENSEMBL variant database was used as a reference database. Additionally, these shared SNPs were interpreted with Regulome DB V.1.1 and finally ranked the variant lists according to predicted functional consequences attributes. Afterwards evidences were collected from gene expression studies, patents, knock-out studies and other literature. Results: Pair-wise analys...

Research paper thumbnail of Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): a web server for mechanism enrichment

Bioinformatics

Motivation: The concept of a 'mechanism-based taxonomy of human disease' is currently replacing t... more Motivation: The concept of a 'mechanism-based taxonomy of human disease' is currently replacing the outdated paradigm of diseases classified by clinical appearance. We have tackled the paradigm of mechanism-based patient subgroup identification in the challenging area of research on neurodegenerative diseases. Results: We have developed a knowledge base representing essential pathophysiology mechanisms of neurodegenerative diseases. Together with dedicated algorithms, this knowledge base forms the basis for a 'mechanism-enrichment server' that supports the mechanistic interpretation of multiscale, multimodal clinical data.

Research paper thumbnail of Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Alzheimer's & dementia : the journal of the Alzheimer's Association, Jun 11, 2016

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis a... more Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in…

Research paper thumbnail of Analytical Strategy to Prioritize Alzheimer’s Disease Candidate Genes in Gene Regulatory Networks Using Public Expression Data

Journal of Alzheimer's Disease

Alzheimer's disease (AD) progressively destroys cognitive abilities in the aging population with ... more Alzheimer's disease (AD) progressively destroys cognitive abilities in the aging population with tremendous effects on memory. Despite recent progress in understanding the underlying mechanisms, high drug attrition rates have put a question mark behind our knowledge about its etiology. Re-evaluation of past studies could help us to elucidate molecular-level details of this disease. Several methods to infer such networks exist, but most of them do not elaborate on context specificity and completeness of the generated networks, missing out on lesser-known candidates. In this study, we present a novel strategy that corroborates common mechanistic patterns across large scale AD gene expression studies and further prioritizes potential biomarker candidates. To infer gene regulatory networks (GRNs), we applied an optimized version of the BC3Net algorithm, named BC3Net10, capable of deriving robust and coherent patterns. In principle, this approach initially leverages the power of literature knowledge to extract AD specific genes for generating viable networks. Our findings suggest that AD GRNs show significant enrichment for key signaling mechanisms involved in neurotransmission. Among the prioritized genes, wellknown AD genes were prominent in synaptic transmission, implicated in cognitive deficits. Moreover, less intensive studied AD candidates (STX2, HLA-F, HLA-C, RAB11FIP4, ARAP3, AP2A2, ATP2B4, ITPR2, and ATP2A3) are also involved in neurotransmission, providing new insights into the underlying mechanism. To our knowledge, this is the first study to generate knowledge-instructed GRNs that demonstrates an effective way of combining literature-based knowledge and data-driven analysis to identify lesser known candidates embedded in stable and robust functional patterns across disparate datasets.

Research paper thumbnail of Reasoning over genetic variance information in cause-and-effect models of neurodegenerative diseases

Briefings in Bioinformatics, 2015

The work we present here is based on the recent extension of the syntax of the Biological Express... more The work we present here is based on the recent extension of the syntax of the Biological Expression Language (BEL), which now allows for the representation of genetic variation information in cause-and-effect models. In our article, we describe, how genetic variation information can be used to identify candidate disease mechanisms in diseases with complex aetiology such as Alzheimer's disease and Parkinson's disease. In those diseases, we have to assume that many genetic variants contribute moderately to the overall dysregulation that in the case of neurodegenerative diseases has such a long incubation time until the first clinical symptoms are detectable. Owing to the multilevel nature of dysregulation events, systems biomedicine modelling approaches need to combine mechanistic information from various levels, including gene expression, microRNA (miRNA) expression, protein-protein interaction, genetic variation and pathway. OpenBEL, the open source version of BEL, has recently been extended to match this requirement, and we demonstrate in our article, how candidate mechanisms for early dysregulation events in Alzheimer's disease can be identified based on an integrative mining approach that identifies 'chains of causation' that include single nucleotide polymorphism information in BEL models.

Research paper thumbnail of Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis

Alzheimer's & dementia : the journal of the Alzheimer's Association, Jan 4, 2015

The discovery and development of new treatments for Alzheimer's disease (AD) requires a profo... more The discovery and development of new treatments for Alzheimer's disease (AD) requires a profound mechanistic understanding of the disease. Here, we propose a model-driven approach supporting the systematic identification of putative disease mechanisms. We have created a model for AD and a corresponding model for the normal physiology of neurons using biological expression language to systematically model causal and correlative relationships between biomolecules, pathways, and clinical readouts. Through model-model comparison we identify "chains of causal relationships" that lead to new insights into putative disease mechanisms. Using differential analysis of our models we identified a new mechanism explaining the effect of amyloid-beta on apoptosis via both the neurotrophic tyrosine kinase receptor, type 2 and nerve growth factor receptor branches of the neurotrophin signaling pathway. We also provide the example of a model-guided interpretation of genetic variation da...

Research paper thumbnail of Active and Passive Millimeter Wave Data Feature Level Fusion Based on WNN

Active and passive millimeter-wave decision fusion have expensive preprocessing and miss target i... more Active and passive millimeter-wave decision fusion have expensive preprocessing and miss target information.In order to solve this an active and passive millimeter-wave feature level fusion method is proposed based on WNN.Eiqenvalue is extracted from active and passive data and input to the WNN.Achieve the feature level fusion of active and passive data,identify the target in the WNN.Experiments and computer simulations show that,the recognition rate of feature level fusion based on WNN is higher than the recognition rate of decision level fusion based on D-S.

Research paper thumbnail of Featured Article Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis

Research paper thumbnail of Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis

Alzheimer's & dementia : the journal of the Alzheimer's Association, Jan 4, 2015

The discovery and development of new treatments for Alzheimer's disease (AD) requires a profo... more The discovery and development of new treatments for Alzheimer's disease (AD) requires a profound mechanistic understanding of the disease. Here, we propose a model-driven approach supporting the systematic identification of putative disease mechanisms. We have created a model for AD and a corresponding model for the normal physiology of neurons using biological expression language to systematically model causal and correlative relationships between biomolecules, pathways, and clinical readouts. Through model-model comparison we identify "chains of causal relationships" that lead to new insights into putative disease mechanisms. Using differential analysis of our models we identified a new mechanism explaining the effect of amyloid-beta on apoptosis via both the neurotrophic tyrosine kinase receptor, type 2 and nerve growth factor receptor branches of the neurotrophin signaling pathway. We also provide the example of a model-guided interpretation of genetic variation da...

Research paper thumbnail of GWAS genetic variant data and their integration in the context of network biology

Journal of Systems and Integrative Neuroscience

Regardless of the success of Genome Wide Association Studies (GWAS) to identify genetic variants ... more Regardless of the success of Genome Wide Association Studies (GWAS) to identify genetic variants associated with human diseases, investigating the molecular mechanisms and disease-associated genes linked to those genetic variants, is a very complex task. Specifically, where intergenic genetic variants are linked to the adjacent neighbouring genes. Consequently, the inference for the mechanistic connection between diseases and its susceptible genetic variants becomes more challenging. Functional genomics studies can support to reveal the significance of variants via intermediate molecular traits. Moreover, approaches like computational and bioinformatics predictions based on the variants location and its sequence attributes can assist to propose the candidate genes. As, the spectrum of potential functional consequences of variants is much broader; it still requires new methodologies to predict any molecular level perturbation. Thus, specialized algorithms and computable modelling approaches are essential, for the modelling and simulation of genetic regulatory networks. In this review, we are briefly summarizing all the existing methodologies for genome wide association studies, currently available algorithms and computable modelling approaches; moreover also emphasizing the required new approaches for modelling and simulations of genetic regulatory networks to predict the functional consequences of disease-associated genetic variants.

Research paper thumbnail of Systematic Analysis of GWAS Data Reveals Genomic Hotspots for Shared Mechanisms between Neurodegenerative Diseases

Journal of Alzheimers Disease & Parkinsonism, 2017

Objective: In this study, we have tried to reveal molecular mechanisms underlying “shared genetic... more Objective: In this study, we have tried to reveal molecular mechanisms underlying “shared genetic variants” and developed a strategy to identify candidate mechanisms for shared aetiology of a pair of diseases, to uncover biological relationships between quantitative traits or related neurodegenerative diseases. Methods: Genetic variants were collected from GWAS catalog, belonged to multiple disease association studies. Meta-analysis was performed by using Metal (a whole genome association analysis toolset), and normalized them for their different sample sizes. LD analysis was done with Haploreg DB V.4.0. Subsequently, the ENSEMBL variant database was used as a reference database. Additionally, these shared SNPs were interpreted with Regulome DB V.1.1 and finally ranked the variant lists according to predicted functional consequences attributes. Afterwards evidences were collected from gene expression studies, patents, knock-out studies and other literature. Results: Pair-wise analys...

Research paper thumbnail of Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): a web server for mechanism enrichment

Bioinformatics

Motivation: The concept of a 'mechanism-based taxonomy of human disease' is currently replacing t... more Motivation: The concept of a 'mechanism-based taxonomy of human disease' is currently replacing the outdated paradigm of diseases classified by clinical appearance. We have tackled the paradigm of mechanism-based patient subgroup identification in the challenging area of research on neurodegenerative diseases. Results: We have developed a knowledge base representing essential pathophysiology mechanisms of neurodegenerative diseases. Together with dedicated algorithms, this knowledge base forms the basis for a 'mechanism-enrichment server' that supports the mechanistic interpretation of multiscale, multimodal clinical data.

Research paper thumbnail of Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Alzheimer's & dementia : the journal of the Alzheimer's Association, Jun 11, 2016

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis a... more Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in…

Research paper thumbnail of Analytical Strategy to Prioritize Alzheimer’s Disease Candidate Genes in Gene Regulatory Networks Using Public Expression Data

Journal of Alzheimer's Disease

Alzheimer's disease (AD) progressively destroys cognitive abilities in the aging population with ... more Alzheimer's disease (AD) progressively destroys cognitive abilities in the aging population with tremendous effects on memory. Despite recent progress in understanding the underlying mechanisms, high drug attrition rates have put a question mark behind our knowledge about its etiology. Re-evaluation of past studies could help us to elucidate molecular-level details of this disease. Several methods to infer such networks exist, but most of them do not elaborate on context specificity and completeness of the generated networks, missing out on lesser-known candidates. In this study, we present a novel strategy that corroborates common mechanistic patterns across large scale AD gene expression studies and further prioritizes potential biomarker candidates. To infer gene regulatory networks (GRNs), we applied an optimized version of the BC3Net algorithm, named BC3Net10, capable of deriving robust and coherent patterns. In principle, this approach initially leverages the power of literature knowledge to extract AD specific genes for generating viable networks. Our findings suggest that AD GRNs show significant enrichment for key signaling mechanisms involved in neurotransmission. Among the prioritized genes, wellknown AD genes were prominent in synaptic transmission, implicated in cognitive deficits. Moreover, less intensive studied AD candidates (STX2, HLA-F, HLA-C, RAB11FIP4, ARAP3, AP2A2, ATP2B4, ITPR2, and ATP2A3) are also involved in neurotransmission, providing new insights into the underlying mechanism. To our knowledge, this is the first study to generate knowledge-instructed GRNs that demonstrates an effective way of combining literature-based knowledge and data-driven analysis to identify lesser known candidates embedded in stable and robust functional patterns across disparate datasets.

Research paper thumbnail of Reasoning over genetic variance information in cause-and-effect models of neurodegenerative diseases

Briefings in Bioinformatics, 2015

The work we present here is based on the recent extension of the syntax of the Biological Express... more The work we present here is based on the recent extension of the syntax of the Biological Expression Language (BEL), which now allows for the representation of genetic variation information in cause-and-effect models. In our article, we describe, how genetic variation information can be used to identify candidate disease mechanisms in diseases with complex aetiology such as Alzheimer's disease and Parkinson's disease. In those diseases, we have to assume that many genetic variants contribute moderately to the overall dysregulation that in the case of neurodegenerative diseases has such a long incubation time until the first clinical symptoms are detectable. Owing to the multilevel nature of dysregulation events, systems biomedicine modelling approaches need to combine mechanistic information from various levels, including gene expression, microRNA (miRNA) expression, protein-protein interaction, genetic variation and pathway. OpenBEL, the open source version of BEL, has recently been extended to match this requirement, and we demonstrate in our article, how candidate mechanisms for early dysregulation events in Alzheimer's disease can be identified based on an integrative mining approach that identifies 'chains of causation' that include single nucleotide polymorphism information in BEL models.

Research paper thumbnail of Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis

Alzheimer's & dementia : the journal of the Alzheimer's Association, Jan 4, 2015

The discovery and development of new treatments for Alzheimer's disease (AD) requires a profo... more The discovery and development of new treatments for Alzheimer's disease (AD) requires a profound mechanistic understanding of the disease. Here, we propose a model-driven approach supporting the systematic identification of putative disease mechanisms. We have created a model for AD and a corresponding model for the normal physiology of neurons using biological expression language to systematically model causal and correlative relationships between biomolecules, pathways, and clinical readouts. Through model-model comparison we identify "chains of causal relationships" that lead to new insights into putative disease mechanisms. Using differential analysis of our models we identified a new mechanism explaining the effect of amyloid-beta on apoptosis via both the neurotrophic tyrosine kinase receptor, type 2 and nerve growth factor receptor branches of the neurotrophin signaling pathway. We also provide the example of a model-guided interpretation of genetic variation da...