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Hood Holst posted an update 6 months, 2 weeks ago
rom COVID-19 patients.
Droplet digital polymerase chain reaction was the most sensitive and highly specific test to identify SARS-CoV-2 in lung specimens from COVID-19 patients.
Many experimental approaches have been developed to identify transcription start sites (TSS) from genomic scale data. However, experiment specific biases lead to large numbers of false positive calls. Here, we present our integrative approach iTiSS, which is an accurate and generic TSS caller for any TSS profiling experiment in eukaryotes, and substantially reduces the number of false positives by a joint analysis of several complementary data sets.
iTiSS is platform independent and implemented in Java (v1.8) and is freely available at https//www.erhard-lab.de/software and https//github.com/erhard-lab/iTiSS.
Supplementary data are available at Bioinformatics online. The raw data as well as the scripts to reproduce all analyses in this study are available on Zenodo (https//doi.org/10.5281/zenodo.3860525).
Supplementary data are available at Bioinformatics online. The raw data as well as the scripts to reproduce all analyses in this study are available on Zenodo (https//doi.org/10.5281/zenodo.3860525).
Adverse drug-drug interactions (DDIs) are crucial for drug research and mainly cause morbidity and mortality. Thus, the identification of potential DDIs is essential for doctors, patients, and the society. Existing traditional machine learning models rely heavily on handcraft features and lack generalization. Recently, the deep learning approaches that can automatically learn drug features from the molecular graph or drug-related network have improved the ability of computational models to predict unknown DDIs. However, previous works utilized large labeled data and merely considered the structure or sequence information of drugs without considering the relations or topological information between drug and other biomedical objects (e.g., gene, disease, and pathway), or considered knowledge graph (KG) without considering the information from the drug molecular structure.
Accordingly, to effectively explore the joint effect of drug molecular structure and semantic information of drugs in knowledge graph for DDI prediction, we propose a multi-scale feature fusion deep learning model named MUFFIN. MUFFIN can jointly learn the drug representation based on both the drug-self structure information and the KG with rich bio-medical information. In MUFFIN, we designed a bi-level cross strategy that includes cross- and scalar-level components to fuse multi-modal features well. MUFFIN can alleviate the restriction of limited labeled data on deep learning models by crossing the features learned from large-scale KG and drug molecular graph. We evaluated our approach on three datasets and three different tasks including binary-class, multi-class, and multi-label DDI prediction tasks. The results showed that MUFFIN outperformed other state-of-the-art baselines.
The source code and data are available at https//github.com/xzenglab/MUFFIN.
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. CTPI-2 cost In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.
Behavior problems are one of the most common mental health disorders in childhood and can undermine children’s health, education, and employment outcomes into adulthood. There are few effective interventions for early childhood.
To test the clinical effectiveness of a brief parenting intervention, the Video-feedback Intervention to promote Positive Parenting and Sensitive Discipline (VIPP-SD), in reducing behavior problems in children aged 12 to 36 months.
The Healthy Start, Happy Start study was a 2-group, parallel-group, researcher-blind, multisite randomized clinical trial conducted via health visiting services in 6 National Health Service trusts in England. Baseline and 5-month follow-up data were collected between July 30, 2015, and April 27, 2018. Of 818 eligible families, 227 declined to participate, and 300 were randomized into the trial. Target participants were caregivers of children who scored in the top 20% for behavior problems on the Strengths and Difficulties Questionnaire. Participants wThere was a mean difference in the total Preschool Parental Account of Children’s Symptoms score of 2.03 (95% CI, 0.06-4.01; P = .04; Cohen d = 0.20 ) between trial groups, with fewer behavior problems in the VIPP-SD group, particularly conduct symptoms (mean difference, 1.61 ; P = .007; d = 0.30 ). Other child behavior outcomes showed similar evidence favoring VIPP-SD. No treatment or trial-related adverse events were reported.
This study found that VIPP-SD was effective in reducing symptoms of early behavior problems in young children when delivered in a routine health service context.
isrctn.org Identifier ISRCTN58327365.
isrctn.org Identifier ISRCTN58327365.
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