Zobrazeno 1 - 10
of 174
pro vyhledávání: '"network modules"'
Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis
Autor:
Shihuan Li, Suqin Li, Qingjie Li, Qiaofeng Zhou, Wenli Liao, Liangzhu Yu, Changhan Ouyang, Hongli Xia, Chao Liu, Mincai Li
Publikováno v:
BMC Medical Genomics, Vol 16, Iss 1, Pp 1-14 (2023)
Abstract Background Atherosclerosis (AS) is a chronic inflammatory disease that might induce severe cardiovascular events, such as myocardial infarction and cerebral infarction. These risk factors in the pathogenesis of AS remain uncertain and furthe
Externí odkaz:
https://doaj.org/article/f6a840995aef45fea75ef1c2c3224dd9
Publikováno v:
Frontiers in Genetics, Vol 14 (2023)
The exploration of important biomarkers associated with cancer development is crucial for diagnosing cancer, designing therapeutic interventions, and predicting prognoses. The analysis of gene co-expression provides a systemic perspective on gene net
Externí odkaz:
https://doaj.org/article/b0d48a27065741cf94140e75d9f81984
Publikováno v:
NeuroImage, Vol 272, Iss , Pp 120036- (2023)
Modules in brain functional connectomes are essential to balancing segregation and integration of neuronal activity. Connectomes are the complete set of pairwise connections between brain regions. Non-invasive Electroencephalography (EEG) and Magneto
Externí odkaz:
https://doaj.org/article/680d65375bf8443da6e2319f760b247f
Publikováno v:
Journal of Historical Network Research, Vol 5, Iss 1, Pp 154-186 (2021)
Liang Qichao (1873 – 1929) and Chen Duxiu (1879 – 1942) were two of the most brilliant writers and influential public intellectuals in the late nineteenth, early twentieth century China. Born six years apart, both men electrified the country with
Externí odkaz:
https://doaj.org/article/8c016acd064a461d83645382a1e3945d
Autor:
Tejaswi V. S. Badam, Hendrik A. de Weerd, David Martínez-Enguita, Tomas Olsson, Lars Alfredsson, Ingrid Kockum, Maja Jagodic, Zelmina Lubovac-Pilav, Mika Gustafsson
Publikováno v:
BMC Genomics, Vol 22, Iss 1, Pp 1-13 (2021)
Abstract Background There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies i
Externí odkaz:
https://doaj.org/article/a8a51bf9cef74d5e9df46fd05e3c45e9
Autor:
Mykola Ivanovych Fedorenko
Publikováno v:
Радіоелектронні і комп'ютерні системи, Vol 0, Iss 4, Pp 116-122 (2019)
The subject of the research presented in the article is neural network modules (NNMs), which are used to solve problems in the practice of diagnosing diseases in urology. This work aims to develop a mathematical model for generating a multitude of ur
Externí odkaz:
https://doaj.org/article/1849bdfcf03b454886beae80cd240d69
Akademický článek
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Publikováno v:
Journal of Translational Medicine, Vol 17, Iss 1, Pp 1-13 (2019)
Abstract Background Kidney renal clear cell carcinoma (KIRC) is the most common type of kidney cancer in adults, and it is responsible for approximately 90–95% of cases. Although extensive evidence has suggested that many immune- and inflammation-r
Externí odkaz:
https://doaj.org/article/f0dceeecb5a3494f82bff4ab67554543
Autor:
Xiang-Zhen Kong, Yu Song, Jin-Xing Liu, Chun-Hou Zheng, Sha-Sha Yuan, Juan Wang, Ling-Yun Dai
Publikováno v:
Frontiers in Genetics, Vol 12 (2021)
The dimensionality reduction method accompanied by different norm constraints plays an important role in mining useful information from large-scale gene expression data. In this article, a novel method named Lp-norm and L2,1-norm constrained graph La
Externí odkaz:
https://doaj.org/article/6f852855d6d44c64999db785db217b35
Publikováno v:
Frontiers in Genetics, Vol 11 (2020)
Diabetes-related diseases (DRDs), especially cancers pose a big threat to public health. Although people have explored pathological pathways of a few common DRDs, there is a lack of systematic studies on important biological processes (BPs) connectin
Externí odkaz:
https://doaj.org/article/99652590560b47c2b2d91d454a48cbf7