Dijkstra’s based identification of lung cancer related genes using PPI networks
نویسندگان
چکیده
منابع مشابه
Identification and prioritization genes related to Hypercholesterolemia QTLs using gene ontology and protein interaction networks
Gene identification represents the first step to a better understanding of the physiological role of the underlying protein and disease pathways, which in turn serves as a starting point for developing therapeutic interventions. Familial hypercholesterolemia is a hereditary metabolic disorder characterized by high low-density lipoprotein cholesterol levels. Hypercholesterolemia is a quantitativ...
متن کاملidentification and prioritization genes related to hypercholesterolemia qtls using gene ontology and protein interaction networks
gene identification represents the first step to a better understanding of the physiological role of the underlying protein and disease pathways, which in turn serves as a starting point for developing therapeutic interventions. familial hypercholesterolemia is a hereditary metabolic disorder characterized by high low-density lipoprotein cholesterol levels. hypercholesterolemia is a quantitativ...
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MOTIVATION The large, complex networks of interactions between proteins provide a lens through which one can examine the structure and function of biological systems. Previous analyses of these continually growing networks have primarily followed either of two approaches: large-scale statistical analysis of holistic network properties, or small-scale analysis of local topological features. Mean...
متن کاملLung cancer-related genes in the blood.
Tumor-related genes can be found circulating in the blood of cancer patients. These genes may be derived from circulating cancer cells or from the patient's primary tumor directly by a process referred to as "gene shedding." Selective and sensitive detection of tumor-related genes in the blood of cancer patients has been made possible by the advent of polymerase chain reaction-based technology ...
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We describe IsoBase, a database identifying functionally related proteins, across five major eukaryotic model organisms: Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus and Homo Sapiens. Nearly all existing algorithms for orthology detection are based on sequence comparison. Although these have been successful in orthology prediction to some extent, we se...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2017
ISSN: 0975-8887
DOI: 10.5120/ijca2017913496