SchizConnect (www.schizconnect.org) is built to address the issues of multiple data repositories in schizophrenia neuroimaging studies. It includes a level of mediation--translating across data sources--so that the user can place one query, e.g. for diffusion images from male individuals with schizophrenia, and find out from across participating data sources how many datasets there are, as well as downloading the imaging and related data. The current version handles the Data Usage Agreements across different studies, as well as interpreting database-specific terminologies into a common framework. New data repositories can also be mediated to bring immediate access to existing datasets. Compared with centralized, upload data sharing models, SchizConnect is a unique, virtual database with a focus on schizophrenia and related disorders that can mediate live data as information is being updated at each data source. It is our hope that SchizConnect can facilitate testing new hypotheses through aggregated datasets, promoting discovery related to the mechanisms underlying schizophrenic dysfunction.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4651768PMC
http://dx.doi.org/10.1016/j.neuroimage.2015.06.065DOI Listing

Publication Analysis

Top Keywords

data
9
schizophrenia disorders
8
data repositories
8
schizconnect
4
schizconnect mediating
4
mediating neuroimaging
4
neuroimaging databases
4
schizophrenia
4
databases schizophrenia
4
disorders large-scale
4

Similar Publications

Background/aims: Rare disease drug development faces unique challenges, such as genotypic and phenotypic heterogeneity within small patient populations and a lack of established outcome measures for conditions without previously successful drug development programs. These challenges complicate the process of selecting the appropriate trial endpoints and conducting clinical trials in rare diseases. In this descriptive study, we examined novel drug approvals for non-oncologic rare diseases by the U.

View Article and Find Full Text PDF

In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.

View Article and Find Full Text PDF

Background: To compare plateletcount (PC), mean platelet volume (MPV), and platelet distribution width (PDW)between women with preeclampsia (PE) and normotensive pregnant women, andevaluate their effectiveness as predictors of PE.

Research Design Andmethods: This cross-sectionalstudy at Nishtar Hospital, Multan, included 141 women: 74 normotensive and 67preeclamptic. Data was collected using an automated hematology analyzer andanalyzed with SPSS version 26 and ROC curves.

View Article and Find Full Text PDF

Background/aims: Certain sociodemographic groups are routinely underrepresented in clinical trials, limiting generalisability. Here, we describe the extent to which enriched enrolment approaches yielded a diverse trial population enriched for older age in a randomised controlled trial of a blood-based multi-cancer early detection test (NCT05611632).

Methods: Participants aged 50-77 years were recruited from eight Cancer Alliance regions in England.

View Article and Find Full Text PDF

Unveiling the role of miRNAs in Diminished Ovarian Reserve: an in silico network approach.

Syst Biol Reprod Med

December 2025

Department of Biosciences and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy.

MicroRNAs (miRNAs) have acquired an increased recognition to unravel the complex molecular mechanisms underlying Diminished Ovarian Reserve (DOR), one of the main responsible for infertility. To investigate the impact of miRNA profiles in granulosa cells and follicular fluid, crucial players in follicle development, this study employed a computational network theory approach to reconstruct potential pathways regulated by miRNAs in granulosa cells and follicular fluid of women suffering from DOR. Available data from published research were collected to create the FGC_MiRNome_MC, a representation of miRNA target genes and their interactions.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!