A system-level analysis of patient disease trajectories based on clinical, phenotypic and molecular similarities.

MOTIVATION: Incorporating the temporal dimension into multimorbidity studies has shown to be crucial for achieving a better understanding of the disease associations. Furthermore, due to the multifactorial nature of human disease, exploring disease associations from different perspectives can provide a holistic view to support the study of their aetiology. RESULTS: In this work, a temporal systems-medicine approach is proposed for identifying time-dependent multimorbidity patterns from patient disease trajectories, by integrating data from electronic health records with genetic and phenotypic information. Specifically, the disease trajectories are clustered using an unsupervised algorithm based on dynamic time warping and three disease similarity metrics: clinical, genetic and phenotypic. An evaluation method is also presented for quantitatively assessing, in the different disease spaces, both the cluster homogeneity and the respective similarities between the associated diseases within individual trajectories. The latter can facilitate exploring the origin(s) in the identified disease patterns. The proposed integrative methodology can be applied to any longitudinal cohort and disease of interest. In this article, prostate cancer is selected as a use case of medical interest to demonstrate, for the first time, the identification of temporal disease multimorbidities in different disease spaces. AVAILABILITY AND IMPLEMENTATION: https://gitlab.com/agiannoula/diseasetrajectories. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The DisGeNET knowledge platform for disease genomics: 2019 update.

One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment.

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/ ), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors' sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

Sources of Error When Measuring Achilles Tendon Mechanics During the Stance Phase of Running.

In recent years, the use of methods to investigate muscle-tendon unit function that combine motion capture with ultrasound (MoCapUS) has increased. Although several limitations and individual errors of these methods have been reported, the total error from all the potential sources together has not been estimated. The aim of this study was to establish the total error in the Achilles tendon (AT) measurements, specifically its length (ATL), strain (ATS), and moment arm (ATMA) acquired with MoCapUS during running. The total error from digitizing, marker movement, ultrasound calibration, and probe rotation errors caused mean ATL error of 4.2 ± 0.6 mm, mean ATMA error of 0.1 ± 0.1 mm, and could potentially alter measured ATS by a mean 2.9 ± 0.2%. Correcting both the calcaneus insertion position (CIP) and properly synchronizing ultrasound and motion capture data caused changes of up to 5.4 ± 1.7 mm in ATL and 11.6 ± 1.3 mm in ATMA. CIP correction and synchronization caused a similar amount of change in ATL, as well as ATS. However, the ATMA change was almost exclusively due to the CIP correction. Finally, if all sources of error were combined, the total ATL error could reach 13.1 mm, the total ATMA error could reach 14.4 mm, and ATS differences could reach up to ± 6.7%. The magnitude of such errors emphasizes the fact that MoCapUS-based AT measurements must be interpreted within the scope of their corresponding errors.

Effect of Changing Match Format from Halves to Quarters on the Performance Characteristics of Male University Field Hockey Players.

The study examined whether the performance characteristics of male university field hockey players differed when the match format was 2 × 35 min halves compared to 2 × 2 × 17.5 min quarters. Thirty-five male university field hockey players (age 21.2 ± 3.0 years, height 1.81 ± 0.07 m, body mass 75.1 ± 8.9 kg), competing at national level in the UK, were monitored over 52 matches played across the 2018-2019 (2 × 35 min halves) and 2019-2020 (2 × 2 × 17.5 min quarters) seasons using 15 Hz Global Positioning System units and heart rate monitors. Total distance, high-speed running distance (≥15.5 km·h-1), accelerations (≥2 m·s-1), decelerations (≤-2 m·s-1), average heart rate and percentage of time spent at >85% of maximum heart rate were recorded during both match formats. Two-level random intercept hierarchal models (Match-level 1, Player-level 2) suggested that the change in format from 2 × 35 min halves (2018-2019 season) to 2 × 2 × 17.5 min quarters (2019-2020 season) resulted in a reduction in total distance and high-speed running distance completed during a match (by 221 m and 120 m, respectively, both p < 0.001). As no significant cross-level interactions were observed (between season and half), the change from 35 min halves to 17.5 min quarters did not attenuate the reduced physical performance evident during the second half of matches (total distance: -235 m less in second half; high-speed running distance: -70 m less in second half; both p < 0.001). Overall, the findings suggest that the change in match format did alter the performance characteristics of male university field hockey players, but the quarter format actually reduced the total distance and high-speed running distance completed during matches, and did not attenuate the reduction in performance seen during the second half of matches.

Measures of Strength and Jump Performance Can Predict 30-m Sprint Time in Rugby Union Players.

ABSTRACT: Furlong, L-AM, Harrison, AJ, and Jensen, RL. Measures of strength and jump performance can predict 30-m sprint time in Rugby Union players. J Strength Cond Res 35(9): 2579-2583, 2021-Performance and fitness monitoring in Rugby Union often include jumping, sprinting, and strength tests, but repeatability of and relationships between these measures are unclear. The level of interindividual variability in these relationships and their sprint time predictive capabilities are also unknown. This study examined the reliability of, and relationship between, countermovement (CMJJH), squat (SJJH), and rebound (RBJJH) jump heights, rebound jump contact time (RBJCT), estimated 1 repetition maximum back squat relative to body mass (SQBM), and reactive strength index (RSI) to 30-m sprint time of subelite, semiprofessional Rugby Union players. Measurement reliability was very good, with high average intraclass correlation coefficients (≥0.9) and low coefficient of variation (<10.1%). All variables were significantly (p < 0.01) correlated to each other (r > 0.575), except for SQBM (only related to CMJJH, r = 0.621) and RBJCT (only related to RSI, r = -0.727). SJJH and SQBM were the strongest and most consistent predictors of time to 30 m (R = 0.754 ± 0.081; SEE = 0.166 ± 0.025), but variability in SEE magnitude was observed across the group during bootstrapping. Cross-validation showed a mean difference between actual and predicted 30 m times equivalent to 0.22% of the group average time to 30 m. These results support the importance of multiple aspects of fitness training in Rugby Union players for improving performance in short-duration sprinting activities, but highlight the individual nature of their relative importance. Measures of strength and power can be used to predict short sprint performance by the strength and conditioning professional.

Comorbidity4j: a tool for interactive analysis of disease comorbidities over large patient datasets.

SUMMARY: Pushed by the growing availability of Electronic Health Records for data mining, the identification of relevant patterns of co-occurring diseases over a population of individuals-referred to as comorbidity analysis-has become a common practice due to its great impact on life expectancy, quality of life and healthcare costs. In this scenario, the availability of scalable, easy-to-use software frameworks tailored to support the study of comorbidities over large datasets of patients is essential. We introduce Comorbidity4j, an open-source Java tool to perform systematic analyses of comorbidities by generating interactive Web visualizations to explore and refine results. Comorbidity4j processes user-provided clinical data by identifying significant disease co-occurrences and computing a comprehensive set of comorbidity indices. Patients can be stratified by sex, age and user-defined criteria. Comorbidity4j supports the analysis of the temporal directionality and the sex ratio of diseases. The incremental upload and validation of clinical input data and the customization of comorbidity analyses are performed by an interactive Web interface. With a Web browser, the results of such analyses can be filtered with respect to comorbidity indexes and disease names and explored by means of heat maps and network charts of disease associations. Comorbidity4j is optimized to efficiently process large datasets of clinical data. Besides a software tool for local execution, we provide Comorbidity4j as a Web service to enable users to perform online comorbidity analyses. AVAILABILITY AND IMPLEMENTATION: Doc: http://comorbidity4j.readthedocs.io/; Source code: https://github.com/fra82/comorbidity4j, Web tool: http://comorbidity.eu/comorbidity4web/.

Quantitative Systems Toxicology Modeling To Address Key Safety Questions in Drug Development: A Focus of the TransQST Consortium.

Omics data have been increasingly generated with limited demonstrated value in drug safety assessment. The TransQST consortium was launched to use omics and other data in mechanistic-based quantitative systems toxicology (QST) models to evaluate their potential use in species translation.

Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study.

BACKGROUND: Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed drugs for the treatment of depressive disorders. Some people share information about their experiences with antidepressants on social media platforms such as Twitter. Analysis of the messages posted by Twitter users under SSRI treatment can yield useful information on how these antidepressants affect users' behavior. OBJECTIVE: This study aims to compare the behavioral and linguistic characteristics of the tweets posted while users were likely to be under SSRI treatment, in comparison to the tweets posted by the same users when they were less likely to be taking this medication. METHODS: In the first step, the timelines of Twitter users mentioning SSRI antidepressants in their tweets were selected using a list of 128 generic and brand names of SSRIs. In the second step, two datasets of tweets were created, the in-treatment dataset (made up of the tweets posted throughout the 30 days after mentioning an SSRI) and the unknown-treatment dataset (made up of tweets posted more than 90 days before or more than 90 days after any tweet mentioning an SSRI). For each user, the changes in behavioral and linguistic features between the tweets classified in these two datasets were analyzed. 186 users and their timelines with 668,842 tweets were finally included in the study. RESULTS: The number of tweets generated per day by the users when they were in treatment was higher than it was when they were in the unknown-treatment period (P=.001). When the users were in treatment, the mean percentage of tweets posted during the daytime (from 8 AM to midnight) increased in comparison to the unknown-treatment period (P=.002). The number of characters and words per tweet was higher when the users were in treatment (P=.03 and P=.02, respectively). Regarding linguistic features, the percentage of pronouns that were first-person singular was higher when users were in treatment (P=.008). CONCLUSIONS: Behavioral and linguistic changes have been detected when users with depression are taking antidepressant medication. These features can provide interesting insights for monitoring the evolution of this disease, as well as offering additional information related to treatment adherence. This information may be especially useful in patients who are receiving long-term treatments such as people suffering from depression.

Pancreatic cancer and autoimmune diseases: An association sustained by computational and epidemiological case-control approaches.

Deciphering the underlying genetic basis behind pancreatic cancer (PC) and its associated multimorbidities will enhance our knowledge toward PC control. The study investigated the common genetic background of PC and different morbidities through a computational approach and further evaluated the less explored association between PC and autoimmune diseases (AIDs) through an epidemiological analysis. Gene-disease associations (GDAs) of 26 morbidities of interest and PC were obtained using the DisGeNET public discovery platform. The association between AIDs and PC pointed by the computational analysis was confirmed through multivariable logistic regression models in the PanGen European case-control study population of 1,705 PC cases and 1,084 controls. Fifteen morbidities shared at least one gene with PC in the DisGeNET database. Based on common genes, several AIDs were genetically associated with PC pointing to a potential link between them. An epidemiologic analysis confirmed that having any of the nine AIDs studied was significantly associated with a reduced risk of PC (Odds Ratio (OR) = 0.74, 95% confidence interval (CI) 0.58-0.93) which decreased in subjects having ≥2 AIDs (OR = 0.39, 95%CI 0.21-0.73). In independent analyses, polymyalgia rheumatica, and rheumatoid arthritis were significantly associated with low PC risk (OR = 0.40, 95%CI 0.19-0.89, and OR = 0.73, 95%CI 0.53-1.00, respectively). Several inflammatory-related morbidities shared a common genetic component with PC based on public databases. These molecular links could shed light into the molecular mechanisms underlying PC development and simultaneously generate novel hypotheses. In our study, we report sound findings pointing to an association between AIDs and a reduced risk of PC.

comoRbidity: an R package for the systematic analysis of disease comorbidities.

Motivation: The study of comorbidities is a major priority due to their impact on life expectancy, quality of life and healthcare cost. The availability of electronic health records (EHRs) for data mining offers the opportunity to discover disease associations and comorbidity patterns from the clinical history of patients gathered during routine medical care. This opens the need for analytical tools for detection of disease comorbidities, including the investigation of their underlying genetic basis. Results: We present comoRbidity, an R package aimed at providing a systematic and comprehensive analysis of disease comorbidities from both the clinical and molecular perspectives. comoRbidity leverages from (i) user provided clinical data from EHR databases (the clinical comorbidity analysis) and (ii) genotype-phenotype information of the diseases under study (the molecular comorbidity analysis) for a comprehensive analysis of disease comorbidities. The clinical comorbidity analysis enables identifying significant disease comorbidities from clinical data, including sex and age stratification and temporal directionality analyses, while the molecular comorbidity analysis supports the generation of hypothesis on the underlying mechanisms of the disease comorbidities by exploring shared genes among disorders. The open-source comoRbidity package is a software tool aimed at expediting the integrative analysis of disease comorbidities by incorporating several analytical and visualization functions. Availability and implementation: https://bitbucket.org/ibi_group/comorbidity. Supplementary information: Supplementary data are available at Bioinformatics online.

Rcupcake: an R package for querying and analyzing biomedical data through the BD2K PIC-SURE RESTful API.

Motivation: In the era of big data and precision medicine, the number of databases containing clinical, environmental, self-reported and biochemical variables is increasing exponentially. Enabling the experts to focus on their research questions rather than on computational data management, access and analysis is one of the most significant challenges nowadays. Results: We present Rcupcake, an R package that contains a variety of functions for leveraging different databases through the BD2K PIC-SURE RESTful API and facilitating its query, analysis and interpretation. The package offers a variety of analysis and visualization tools, including the study of the phenotype co-occurrence and prevalence, according to multiple layers of data, such as phenome, exposome or genome. Availability and implementation: The package is implemented in R and is available under Mozilla v2 license from GitHub (https://github.com/hms-dbmi/Rcupcake). Two reproducible case studies are also available (https://github.com/hms-dbmi/Rcupcake-case-studies/blob/master/SSCcaseStudy_v01.ipynb, https://github.com/hms-dbmi/Rcupcake-case-studies/blob/master/NHANEScaseStudy_v01.ipynb). Contact: paul_avillach@hms.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants.

The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis.

BACKGROUND: Mental disorders have become a major concern in public health, and they are one of the main causes of the overall disease burden worldwide. Social media platforms allow us to observe the activities, thoughts, and feelings of people's daily lives, including those of patients suffering from mental disorders. There are studies that have analyzed the influence of mental disorders, including depression, in the behavior of social media users, but they have been usually focused on messages written in English. OBJECTIVE: The study aimed to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users who generate them, which could suggest signs of depression. METHODS: This study was developed in 2 steps. In the first step, the selection of users and the compilation of tweets were performed. A total of 3 datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mentioned that they suffer from depression), a depressive tweets dataset (a manual selection of tweets from the previous users, which included expressions indicative of depression), and a control dataset (made up of the timeline of 450 randomly selected users). In the second step, the comparison and analysis of the 3 datasets of tweets were carried out. RESULTS: In comparison with the control dataset, the depressive users are less active in posting tweets, doing it more frequently between 23:00 and 6:00 (P<.001). The percentage of nouns used by the control dataset almost doubles that of the depressive users (P<.001). By contrast, the use of verbs is more common in the depressive users dataset (P<.001). The first-person singular pronoun was by far the most used in the depressive users dataset (80%), and the first- and the second-person plural pronouns were the least frequent (0.4% in both cases), this distribution being different from that of the control dataset (P<.001). Emotions related to sadness, anger, and disgust were more common in the depressive users and depressive tweets datasets, with significant differences when comparing these datasets with the control dataset (P<.001). As for negation words, they were detected in 34% and 46% of tweets in among depressive users and in depressive tweets, respectively, which are significantly different from the control dataset (P<.001). Negative polarity was more frequent in the depressive users (54%) and depressive tweets (65%) datasets than in the control dataset (43.5%; P<.001). CONCLUSIONS: Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. On the basis of these changes, these users can be monitored and supported, thus introducing new opportunities for studying depression and providing additional health care services to people with this disorder.

psygenet2r: a R/Bioconductor package for the analysis of psychiatric disease genes.

MOTIVATION: Psychiatric disorders have a great impact on morbidity and mortality. Genotype-phenotype resources for psychiatric diseases are key to enable the translation of research findings to a better care of patients. PsyGeNET is a knowledge resource on psychiatric diseases and their genes, developed by text mining and curated by domain experts. RESULTS: We present psygenet2r, an R package that contains a variety of functions for leveraging PsyGeNET database and facilitating its analysis and interpretation. The package offers different types of queries to the database along with variety of analysis and visualization tools, including the study of the anatomical structures in which the genes are expressed and gaining insight of gene's molecular function. Psygenet2r is especially suited for network medicine analysis of psychiatric disorders. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and is available under MIT license from Bioconductor (http://bioconductor.org/packages/release/bioc/html/psygenet2r.html). CONTACT: juanr.gonzalez@isglobal.org or laura.furlong@upf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of a Novel Human E-Cadherin Splice Variant and Assessment of Its Effects Upon EMT-Related Events.

Epithelial Cadherin (E-cadherin) is involved in calcium-dependent cell-cell adhesion and signal transduction. The E-cadherin decrease/loss is a hallmark of Epithelial to Mesenchymal Transition (EMT), a key event in tumor progression. The underlying molecular mechanisms that trigger E-cadherin loss and consequent EMT have not been completely elucidated. This study reports the identification of a novel human E-cadherin variant mRNA produced by alternative splicing. A bioinformatics evaluation of the novel mRNA sequence and biochemical verifications suggest its regulation by Nonsense-Mediated mRNA Decay (NMD). The novel E-cadherin variant was detected in 29/42 (69%) human tumor cell lines, expressed at variable levels (E-cadherin variant expression relative to the wild type mRNA = 0.05-11.6%). Stable transfection of the novel E-cadherin variant in MCF-7 cells (MCF7Ecadvar) resulted in downregulation of wild type E-cadherin expression (transcript/protein) and EMT-related changes, among them acquisition of a fibroblastic-like cell phenotype, increased expression of Twist, Snail, Zeb1, and Slug transcriptional repressors and decreased expression of ESRP1 and ESRP2 RNA binding proteins. Moreover, loss of cytokeratins and gain of vimentin, N-cadherin and Dysadherin/FXYD5 proteins was observed. Dramatic changes in cell behavior were found in MCF7Ecadvar, as judged by the decreased cell-cell adhesion (Hanging-drop assay), increased cell motility (Wound Healing) and increased cell migration (Transwell) and invasion (Transwell w/Matrigel). Some changes were found in MCF-7 cells incubated with culture medium supplemented with conditioned medium from HEK-293 cells transfected with the E-cadherin variant mRNA. Further characterization of the novel E-cadherin variant will help understanding the molecular basis of tumor progression and improve cancer diagnosis. J. Cell. Physiol. 232: 1368-1386, 2017. © 2016 Wiley Periodicals, Inc.

DisGeNET-RDF: harnessing the innovative power of the Semantic Web to explore the genetic basis of diseases.

MOTIVATION: DisGeNET-RDF makes available knowledge on the genetic basis of human diseases in the Semantic Web. Gene-disease associations (GDAs) and their provenance metadata are published as human-readable and machine-processable web resources. The information on GDAs included in DisGeNET-RDF is interlinked to other biomedical databases to support the development of bioinformatics approaches for translational research through evidence-based exploitation of a rich and fully interconnected linked open data. AVAILABILITY AND IMPLEMENTATION: http://rdf.disgenet.org/ CONTACT: support@disgenet.org.

Human diseases through the lens of network biology.

One of the challenges raised by next generation sequencing (NGS) is the identification of clinically relevant mutations among all the genetic variation found in an individual. Network biology has emerged as an integrative and systems-level approach for the interpretation of genome data in the context of health and disease. Network biology can provide insightful models for genetic phenomena such as penetrance, epistasis, and modes of inheritance, all of which are integral aspects of Mendelian and complex diseases. Moreover, it can shed light on disease mechanisms via the identification of modules perturbed in those diseases. Current challenges include understanding disease as a result of the interplay between environmental and genetic perturbations and assessing the impact of personal sequence variations in the context of networks. Full realization of the potential of personal genomics will benefit from network biology approaches that aim to uncover the mechanisms underlying disease pathogenesis, identify new biomarkers, and guide personalized therapeutic interventions.

PsyGeNET: a knowledge platform on psychiatric disorders and their genes.

UNLABELLED: PsyGeNET (Psychiatric disorders and Genes association NETwork) is a knowledge platform for the exploratory analysis of psychiatric diseases and their associated genes. PsyGeNET is composed of a database and a web interface supporting data search, visualization, filtering and sharing. PsyGeNET integrates information from DisGeNET and data extracted from the literature by text mining, which has been curated by domain experts. It currently contains 2642 associations between 1271 genes and 37 psychiatric disease concepts. In its first release, PsyGeNET is focused on three psychiatric disorders: major depression, alcohol and cocaine use disorders. PsyGeNET represents a comprehensive, open access resource for the analysis of the molecular mechanisms underpinning psychiatric disorders and their comorbidities. AVAILABILITY AND IMPLEMENTATION: The PysGeNET platform is freely available at http://www.psygenet.org/. The PsyGeNET database is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). CONTACT: lfurlong@imim.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Personalized respiratory medicine: exploring the horizon, addressing the issues. Summary of a BRN-AJRCCM workshop held in Barcelona on June 12, 2014.

This Pulmonary Perspective summarizes the content and main conclusions of an international workshop on personalized respiratory medicine coorganized by the Barcelona Respiratory Network ( www.brn.cat ) and the AJRCCM in June 2014. It discusses (1) its definition and historical, social, legal, and ethical aspects; (2) the view from different disciplines, including basic science, epidemiology, bioinformatics, and network/systems medicine; (3) the bottlenecks and opportunities identified by some currently ongoing projects; and (4) the implications for the individual, the healthcare system and the pharmaceutical industry. The authors hope that, although it is not a systematic review on the subject, this document can be a useful reference for researchers, clinicians, healthcare managers, policy-makers, and industry parties interested in personalized respiratory medicine.