نتایج جستجو برای: grn reverse engineering models
تعداد نتایج: 1233163 فیلتر نتایج به سال:
While programming is modeling the reality, reverse engineering is concerned with recovering it from the code. Parts of this reality can be formalized as concepts and relations among them. As previous research suggests, the identification of these concepts is a key issue in automating program analysis. Their central role requires advance reverse engineering tasks to consider them first-class cit...
The success of system modernization depends on the existence of technical frameworks for information integration and tool interoperation like the Model Driven Architecture (MDA). Reverse engineering techniques play a crucial role in system modernization. This paper describes how to reverse engineering activity diagrams from object oriented code in the MDA context focusing on transformations at ...
Reverse engineering (RE) is a research field where physical models are measured or digitized in order to be virtually rebuilt to obtain a CAD model. Today, rebuilt CAD models using current geometrical approaches are generally “frozen” or barely re-usable. This paper deals with a new reverse engineering approach. This one improves the classical geometrical approach with a knowledge extraction ph...
Modelling frameworks for biological networks are used to reason on the models and their properties. One of the main problems with such modelling frameworks is to determine the dynamics of gene regulatory networks (GRN). Recently, it has been observed in in vivo experiments and in genomic and transcriptomic studies, that spatial information is useful to better understand both the mechanisms and ...
BACKGROUND The co-occurrence of the c.709-1G>A GRN mutation and the p.A152T MAPT variant has been identified in 18 Basque families affected by frontotemporal dementia (FTD). We aimed to investigate the influence of the p.A152T MAPT variant on the clinical and neuropathological features of these Basque GRN families. METHODS AND FINDINGS We compared clinical characteristics of 14 patients who c...
The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these lar...
BACKGROUND Gene expression time series data are usually in the form of high-dimensional arrays. Unfortunately, the data may sometimes contain missing values: for either the expression values of some genes at some time points or the entire expression values of a single time point or some sets of consecutive time points. This significantly affects the performance of many algorithms for gene expre...
Granulin (Grn) mutations were identified in familial frontotemporal lobar degeneration (FTLD) patients with TAR DNA-binding protein of 43 kd (TDP-43) pathology. Grn transcript haploinsufficiency is proposed as a disease mechanism that leads to the loss of functional progranulin (PGRN) protein. Thus, these mutations are strongly involved in FTLD pathogenesis. Moreover, recent findings indicate t...
Loss-of-function mutations in progranulin (GRN) cause ubiquitin- and TAR DNA-binding protein 43 (TDP-43)-positive frontotemporal dementia (FTLD-U), a progressive neurodegenerative disease affecting approximately 10% of early-onset dementia patients. Here we expand the role of GRN in FTLD-U and demonstrate that a common genetic variant (rs5848), located in the 3'-untranslated region (UTR) of GRN...
Gene Regulatory Network (GRN) plays an important role in knowing insight of cellular life cycle. It gives information about at which different environmental conditions genes of particular interest get over expressed or under expressed. Modelling of GRN is nothing but finding interactive relationships between genes. Interaction can be positive or negative. For inference of GRN, time series data ...
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