Motivation Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. Results We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug–drug interaction (DDI) prediction, protein–protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks. Availability and implementation As part of our contributions in the paper, we develop an easy-to-use Python package with detailed instructions, BioNEV, available at: https://github.com/xiangyue9607/BioNEV, including all source code and datasets, to facilitate studying various graph embedding methods on biomedical tasks. Supplementary information Supplementary data are available at Bioinformatics online.

Humans have developed the perception, production and processing of sounds into the art of music. A genetic contribution to these skills of musical aptitude has long been suggested. We performed a genome-wide scan in 76 pedigrees (767 individuals) characterized for the ability to discriminate pitch (SP), duration (ST) and sound patterns (KMT), which are primary capacities for music perception. Using the Bayesian linkage and association approach implemented in program package KELVIN, especially designed for complex pedigrees, several SNPs near genes affecting the functions of the auditory pathway and neurocognitive processes were identified. The strongest association was found at 3q21.3 (rs9854612) with combined SP, ST and KMT test scores (COMB). This region is located a few dozen kilobases upstream of the GATA binding protein 2 (GATA2) gene. GATA2 regulates the development of cochlear hair cells and the inferior colliculus (IC), which are important in tonotopic mapping. The highest probability of linkage was obtained for phenotype SP at 4p14, located next to the region harboring the protocadherin 7 gene, PCDH7. Two SNPs rs13146789 and rs13109270 of PCDH7 showed strong association. PCDH7 has been suggested to play a role in cochlear and amygdaloid complexes. Functional class analysis showed that inner ear and schizophrenia related genes were enriched inside the linked regions. This study is the first to show the importance of auditory pathway genes in musical aptitude.

To prevent the spread of COVID-19 and to continue responding to healthcare needs, hospitals are rapidly adopting telehealth and other digital health tools to deliver care remotely. Intelligent conversational agents and virtual assistants, such as chatbots and voice assistants, have been utilized to augment health service capacity to screen symptoms, deliver healthcare information, and reduce exposure. In this commentary, we examined the state of voice assistants (e.g., Google Assistant, Apple Siri, Amazon Alexa) as an emerging tool for remote healthcare delivery service and discussed the readiness of the health system and technology providers to adapt voice assistants as an alternative healthcare delivery modality during a health crisis and pandemic.

Our findings suggest that SRGAP1 is a candidate gene in PTC susceptibility. SRGAP1 is likely a low-penetrant gene, possibly of a modifier type.

Objective The authors previously demonstrated significant association between markers within NOS1AP and schizophrenia in a set of Canadian families of European descent, as well as significantly increased expression in schizophrenia of NOS1AP in unrelated postmortem samples from the dorsolateral prefrontal cortex. In this study the authors sought to apply novel statistical methods and conduct additional biological experiments to isolate at least one risk allele within NOS1AP. Method Using the posterior probability of linkage disequilibrium (PPLD) to measure the probability that a single nucleotide polymorphism (SNP) is in linkage disequilibrium with schizophrenia, the authors evaluated 60 SNPs from NOS1AP in 24 Canadian families demonstrating linkage and association to this region. SNPs exhibiting strong evidence of linkage disequilibrium were tested for regulatory function by luciferase reporter assay. Two human neural cell lines (SK-N-MC and PFSK-1) were transfected with a vector containing each allelic variant of the SNP, the NOS1AP promoter, and a luciferase gene. Alleles altering expression were further assessed for binding of nuclear proteins by electrophoretic mobility shift assay. Results Three SNPs produced PPLDs >40%. One of them, rs12742393, demonstrated significant allelic expression differences in both cell lines tested. The allelic variation at this SNP altered the affinity of nuclear protein binding to this region of DNA. Conclusions The A allele of rs12742393 appears to be a risk allele associated with schizophrenia that acts by enhancing transcription factor binding and increasing gene expression.

This paper describes the software package KELVIN, which supports the PPL (posterior probability of linkage) framework for the measurement of statistical evidence in human (or more generally, diploid) genetic studies. In terms of scope, KELVIN supports two-point (trait-marker or marker-marker) and multipoint linkage analysis, based on either sex-averaged or sex-specific genetic maps, with an option to allow for imprinting; trait-marker linkage disequilibrium (LD), or association analysis, in case-control data, trio data, and/or multiplex family data, with options for joint linkage and trait-marker LD or conditional LD given linkage; dichotomous trait, quantitative trait and quantitative trait threshold models; and certain types of gene-gene interactions and covariate effects. Features and data (pedigree) structures can be freely mixed and matched within analyses. The statistical framework is specifically tailored to accumulate evidence in a mathematically rigorous way across multiple data sets or data subsets while allowing for multiple sources of heterogeneity, and KELVIN itself utilizes sophisticated software engineering to provide a powerful and robust platform for studying the genetics of complex disorders.

Digital health tools and technologies are transforming health care and making significant impacts on how health and care information are collected, used, and shared to achieve best outcomes. As most of the efforts are still focused on clinical settings, the wealth of health information generated outside of clinical settings is not being fully tapped. This is especially true for children with medical complexity (CMC) and their families, as they frequently spend significant hours providing hands-on medical care within the home setting and coordinating activities among multiple providers and other caregivers. In this paper, a multidisciplinary team of stakeholders discusses the value of health information generated at home, how technology can enhance care coordination, and challenges of technology adoption from a patient-centered perspective. Voice interactive technology has been identified to have the potential to transform care coordination for CMC. This paper shares opinions on the promises, limitations, recommended approaches, and challenges of adopting voice technology in health care, especially for the targeted patient population of CMC.

Key Points Question How frequently are adolescent patient portal accounts accessed by guardians? Findings In this cross-sectional study including 3429 adolescent accounts across 3 academic institutions, analysis of portal messages found that more than half of adolescent patient portal accounts with outbound messages were accessed by guardians. The percentage of accessed accounts was greater in children aged 13 to 14 years vs those aged 17 to 18 years. Meaning These findings may be useful in guiding health system approaches to protecting adolescent confidentiality when sharing health data via patient portals.