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A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure. With the exponential growth in the number of potential miRNA-disease association combinations, traditional biological experimental methods face difficulties in processing massive amounts of data. Hence, developing more efficient computational methods to predict possible miRNA-disease associations and prioritize them is particularly necessary. In recent years, numerous deep learning-based computational methods have been developed and have demonstrated excellent performance. However, most of these methods rely on external databases or tools to compute various auxiliary information. Unfortunately, these external databases or tools often cover only a limited portion of miRNAs and diseases, resulting in many miRNAs and diseases being unable to match with these computational methods. Therefore, there are certain limitations associated with the practical application of these methods. To overcome the above limitations, this study proposes a multi-view computational model called MVNMDA, which predicts potential miRNA-disease associations by integrating features of miRNA and diseases from local views, global views, and semantic views. Specifically, MVNMDA utilizes known association information to construct node initial features. Then, multiple networks are constructed based on known association to extract low-dimensional feature embedding of all nodes. Finally, a cascaded attention classifier is proposed to fuse features from coarse to fine, suppressing noise within the features and making precise predictions. To validate the effectiveness of the proposed method, extensive experiments were conducted on the HMDD v2.0 and HMDD v3.2 datasets. The experimental results demonstrate that MVNMDA achieves better performance compared to other computational methods. Additionally, the case study results further demonstrate the reliable predictive performance of MVNMDA. [ABSTRACT FROM AUTHOR]

A novel actinomycetes producing lycopene, designated HBU208002 T , was isolated from a lakeside soil sample collected in Baiyangdian, located in Xiong'an New Area of China, and its taxonomic position was investigated by a polyphasic approach. Phylogenetic analysis based on 16S rRNA gene sequence comparisons revealed that the strain HBU208002 T fell within the genus Polymorphospora and was closely related to Polymorphospora rubra JCM 14904 T (99.73% identity). However, the average nucleotide identity (ANI), digital DNA-DNA hybridization (dDDH) and average amino acid identity (AAI) values between the strain HBU208002 T and P. rubra JCM 14904 T were 91.78, 44.7 and 91.6%, respectively, which were lower than the ANI (95-96%), DDH (>70%) and AAI (>95%) thresholds of prokaryotic microbial defined species. The predominant fatty acids of the strain HBU208002 T were iso-C 16:0 , C 17:1 ω8c. The menaquinones of the strain HBU208002 T were MK-8(H 8 ) and MK9(H 4 ), while those of P. rubra JCM 14904 T were MK-10(H 6 ), MK-10(H 4 ), MK-9(H 6 ) and MK-9(H 4 ). Meanwhile, some phenotypic characterizations and antibacterial activities distinguished the strain HBU208002 T from P. rubra JCM 14904 T . The strain HBU208002 T exhibited inhibitory effects on Fusarium graminearum , Fusarium verticillioides and Botrytis cinerea , but P. rubra JCM 14904 T had no activity. Therefore, the strain HBU208002 T should be assigned as representing a novel species of the genus Polymorphospora , for which the name Polymorphospora lycopeni was proposed. The type strain is HBU208002 T (=KCTC49833 T = GDMCC4.236 T ).