6,687 research outputs found
Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs
Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks
CAMELLIA SPHAMII (THEACEAE, SECT. PIQUETIA), A NEW TAXON OF YELLOW FLOWER FROM LANGBIANG BIOSPHERE RESERVE, VIETNAM
Camellia sphamii is described and illustrated as a new species of section Piquetia from Hamasin village, D’ran town, Don Duong district, Lam Dong province, Vietnam. C. sphamii is similar to C. proensis (Quach, Luong et al., 2021) but differs from it in several morphological features: mature leaves cordate at base, young leaves purple; pericarp 7–8 mm thick with dense hair on the outer surface, flower buds ovate, ferruginous; sepals 5, hemisphere, concave, finely hairy on the outer surface, sparsely hairy on the inside, petals 7, finely hairy on the outer surface, with translucent margin, concave; style 5, ½ basally united; capsule 5 locular. Information on its phenology, distribution, ecology, and conservation status is also provided
CAMELLIA PYRIFORMIS (THEACEAE, SECTION CALPANDRIA), A NEW SPECIES FROM NORTHERN VIETNAM
Camellia pyriformis is described, illustrated, and placed in section Calpandria. Morphological features of this new species are young branches villous; leaves above pubescent, a long midrib, below pubescent; petiole falcate, densely villous; flowers solitary or geminate; pedicel very short, pubescent; bracteoles sparsely pubescent on both sides; sepals, pubescent on both sides; petals, white, glabrous; androecium 5–6 stamens, filaments completely united to form a truncated cone, glabrous, basal adnate to the petal, shallowly dentate at the apex, each filament bearing an anther; gynoecium 3-locular, densely white silky strigose tomentose, styles glabrous; capsule pyriform, pubescent; seed broad pyriform, densely villou
THE DIVERSITY OF YELLOW CAMELLIAS IN THE CENTRAL HIGHLANDS, VIETNAM
The Central Highlands (Tây Nguyên) is a center of yellow camellia diversity in Vietnam and the world. The Central Highlands contains 18 of Vietnam’s yellow camellia species, accounting for 37% of yellow camellia species in Vietnam and 28% of yellow camellia species worldwide. Moreover, all 18 yellow camellia species in the Central Highlands are endemic to Vietnam. The camellias of the Central Highlands belong to nine sections, accounting for 75% of the world. The yellow colors occur in three groups: pale yellow, yellow, and yellow with compound colors. The yellow camellia distribution is dispersed at 500–1600 m elevation in evergreen broadleaf forests and mixed wood-bamboo forests
Calculating the volume of Tan Mieu lake in Thanh Noi area, Hue City, for the urban stormwater drainage system
Detention lakes, which are effective means of flood control, are highly effective in sustainable drainage solutions. Numerous factors diminish the regulating reservoir's function for urban drainages, such as the lake's small volume, an inadequate drainage system, and restricted outflow from the lake. Therefore, this study applies the method of limited rainfall intensity to calculate the stormwater flow into the lake and determine the volume of the detention lakes according to TCVN 7957: 2008 [1] to the sub-basin of Tan Mieu. It is confirmed that the volume of the lake in the region and the level of outflow from the lake, which are essential factors in flood mitigation, is not suitable for the rainfall characteristics of the area. The results show that with the planned outflow of Qx = 1/3 × Qt, the lake's actual capacity is roughly a half of the required lake capacity. With the option Qx = 1/2 × Qt, the actual volume of the lake is 1.2 times smaller than the calculated capacity. Therefore, increasing the lake's volume and outflow volume from the lake minimizes local flooding
- …