Бидний тухай
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In this talk we introduce a novel similarity measure for categorical random variables and a statistical test for identification of Mongolian hybrid racing horses from native. There are negative facts about hybrid horses hereto it’s vulnerable against diseases and losses its native behavior. However, there is no research work about hybrid horses except Bilegdemberel Banzragchs thesis. In such research work, only phenotype of racing horses is considered. Federation of Mongolian Horse Racing Sport and Trainers is trying to classify hybrid and native racing horses which compete at the national level race such as Naadam. The classification rule is based on crest of a horse. The racing horse trainers complain about this simple rule. In population genetics, microsatellite markers are used to identify individuals and measure difference or relation between populations and individuals. Thus, we developed a test which is based on 19 microsatellite markers.
Extracting a group of genes whose activation simultaneously produces significant effects on cancer progression is of great importance in understanding the details of control mechanisms in cancer-responsive biological processes. Network structures have been often used to represent these molecular activities based on an accurate estimation of behavioral relationships that is a whole set of interacting neighbors and local patterns, responsive under investigated conditions. Here, we propose a novel method, so-called Weighted Maximum Clique Tree (WMCT), to identify a particular subset of genes forming a cancer-specific sub-network. We first estimate a gene-network using a Kullback-Leibler divergence with a sparse covariance matrix by measuring the differential co-expression signatures across physiological conditions. Then an existing protein-protein interaction network information is combined to the activation of gene-network for constructing a background cancer-responsive network. Inspired by an integer linear programming formulation, the densest part of the network is obtained to represent the cancer-specific sub-network. We applied the WMCT method to both simulated data and a real data set of prostate cancer. WMCT successfully identified a large fraction of well-known oncogenes in prostate cancer and the subset of genes were enriched in cancer-related pathways and biological processes with significant p-values. Compared with several existing methods, WMCT returned better performances in simulated data sets. These results demonstrate that the proposed method can efficiently identify a particular subset of genes relevant under investigated condition.
Сүүлийн жилүүдэд ШШҮХ-нд ялтны ДНХ-ийн 7000 гаруй биологийн дээж биетээр ба цаасан баримтаар хуримтлагдсан. Энэхүү судалгааны хүрээнд бид MonDIS нэртэй үндэсний хэмжээний анхны ДНХ-ийн мэдээллийн сан, хайлтын системийг үүсгэн байгуулсан ажлын үр дүнг тайлагнана. Мөн энэ чиглэлийн судалгаатай холбоотой хуримтлуулсан туршлага, цаашид гүйцэтгэх ажлыг танилцуулна.
Genomic activations in cancer are a mixture of driving events that promote cancer progression and passenger events that represent a large fraction of random somatic alterations. Extract- ing a group of genes whose activations simultaneously produce significant effects on cancer development is of great importance in understanding the details of control mechanisms in cancer-responsive biological processes. Here, we propose a novel method, so-called Weighted Maximum Clique Tree (WMCT), to identify a condition specific sub-network. We first con- struct a gene-network using a Kullback-Leibler divergence with a sparse covariance matrix by measuring the differential co-expression signatures across physiological conditions. Inspired by an integer linear programming formulation, the densest part of the network is obtained to rep- resent the condition specific sub-network. We applied the WMCT method to both simulated data and a real data set of prostate cancer.
