An article published in BMCBioInformatics presents SVcnn: a more accurate deep learning-based method for detecting structural variations by using long-read sequencing data.
The third criterion means the distance between the two regions is less than 1000bp. After merging the two regions into a new region, we record the new region as , max,type1,2). Because the regions on the two reads are merged together, the last value changes from 1 to 2. We repeat this step until the remaining regions cannot be merged, and finally, we get a list of candidate SV regions. To remove the effects of noise, we only retain candidate SV regions with more than 3 supporting reads .
We use a five-color image to represent each candidate region. For a region described by , all alignments near the region are checked and each character of the corresponding CIGAR string is converted into a pixel. The alignments of each read occupy a row in the image. The following rules govern the conversion process :The Match of alignment with a minus strand is represented as a blue pixel.The INS of alignment is represented as a red pixel.
Because the LetNet model requires a fixed input image size, it is necessary to normalize previously obtained images. This is achieved by utilizing the resize function in the Python library which resizes the image to a uniform size of 224Ö224 pixels. Subsequently, the training dataset becomes an essential component required for training the LetNet model. HG002_SVs_Tier1_v0.6 is currently the widely accepted benchmark dataset utilized by researchers.
After obtaining the trained LeNet model, we input the candidate SV region images obtained in the previous steps into the model. We use the model to determine the probability of an SV occurring in this region and identify the type of SV. We select the label with the highest probability as the judgment result. If the model determines that there is no SV in this region, then we discard the candidate SV region.
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KATZNCP: a miRNA–disease association prediction model integrating KATZ algorithm and network consistency projection - BMC BioinformaticsBackground Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA–disease associations predicted by computational methods are the best complement to biological experiments. Results In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA–disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA–disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA–disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP. Conclusion A new computational model KATZNCP was proposed for predicting potential miRNA–drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA–disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.
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Infusing wellness opportunities into integrated youth services - BMC PsychiatryBackground Appropriate health services and health promotion strategies for young people with mental health and substance use (MHSU) concerns are critical for recovery. Foundry, an integrated youth services (IYS) initiative for young people ages 12-24 in British Columbia (BC), Canada, has recently added leisure and recreational activities (referred to as the Wellness Program) into its services. The objectives of this study were to: (1) describe how the Wellness Program was implemented over a two-year period into IYS (2) provide an overview of what the Wellness Program is, who accessed the program since inception and initial evaluation results. Methods This study was part of the developmental evaluation of Foundry. A phased approach was used to implement the program at nine centres. Data was accessed from Foundry’s centralized platform ‘Toolbox’ and included activity type, number of unique youth and visits, additional services sought, information about how youth found out about the centre, and demographics. Qualitative data was also accessed from focus groups (n=2) conducted with young people (n=9). Results Over the two-year period, 355 unique youth accessed the Wellness Program, with 1319 unique visits. Almost half (40%) of youth identified the Wellness Program as the first point of access to Foundry. A total of 384 different programs were offered targeting five wellness domains (physical, mental/emotional, social, spiritual, and cognitive/intellectual). The majority of youth identified as young girls/women (58.2%), 22.6% as gender diverse, and 19.2% as young men/boys. The mean age was 19 years, and most participants were between the ages of 19-24 years (43.6%). From the thematic analysis of focus groups, we found young people enjoyed the social aspect of the program with peers and facilitators, and identified program improvements that are being considered as the program grows. Conclusions This study provides insight into the development and implementation of leisure
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