Nonhuman primates (NHPs) are widely studied across many scientific disciplines using a variety of techniques in diverse environments. Due to the wide scope of NHP research, substantial overlap in research topics and questions can occur, whose resulting scientific evidence is synthesized by literature reviews. Identifying all relevant research on a particular topic involving NHPs can be difficult and time consuming. By adopting objective search development techniques from systematic reviews, we developed search filters to detect all scientific publications involving NHPs in PubMed, PsycINFO (via EBSCOhost), and Web of Science. We compared the performance of our comprehensive NHP search filters to search strings typical of a novice database user (i.e., NHP simple search strings) and validated their sensitivity by combining these searches with a topic search of cortisol related studies. For all comparisons, our comprehensive NHP search filters retrieved considerably more scientific publications than the NHP simple search strings. Importantly, our comprehensive NHP search filters are easy to use (text can be copied and pasted into the database search engine) and detect the most recent publications that have yet to be indexed by the bibliographic databases queried. Additionally, we developed filterNHP, an R package and web-based application (https://filterNHP.dpz.eu), for researchers interested in literature searches involving a taxonomic sub-group of NHPs. filterNHP alleviates time necessary for adapting our comprehensive NHP search filters for a particular NHP sub-group by automatizing the creation of these search filters. Altogether, our comprehensive NHP search filters and those for taxonomic sub-groups generated by filterNHP will enable swift and easy retrieval of the available scientific literature involving NHPs, and thereby help enhance the quality of new NHP literature reviews that guide future scientific research (new experiments) and public policy (e.g., on welfare and conservation).
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http://dx.doi.org/10.1002/ajp.23287 | DOI Listing |
JMIR Res Protoc
December 2024
Endocrine and Metabolic Unit, Nutrition, Metabolism and Cardiovascular Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Selangor, Malaysia.
Background: Obesity presents a growing challenge to public health, and its intricate association with genetics continues to be a compelling field of study. In countries such as Malaysia, where diverse genetic backgrounds converge, exploring the molecular genetics of obesity is even more imperative.
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Alzheimers Dement
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Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: People leverage single-variant association test to systematically evaluate common genetic variants (minor allele frequency 0.5% < [MAF) < 5%) for complex disease, such as Alzheimer's disease (AD). Rare variants (MAF < 1%) could explain additional disease risk and are known to play an important role in the diseases.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: Recent genetic studies have implicated >70 genomic loci associated with the risk for Alzheimer's Disease. However, the underlying functional mechanisms remain unclear. Several functional genomics (FG) methods such as chromosome conformation (CC) capture technologies and expression quantitative trait loci (eQTLs) have been developed to study the genetic targets.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Penn Neurodegeneration Genomics Center, Dept of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: NIAGADS is a national data repository that offers qualified investigators access to genomic data for Alzheimer's disease (AD) and related dementia. In addition, NIAGADS has made substantial effort to curate, harmonize, standardize, and disseminate AD-relevant variant, gene, and sequence annotations from publications, functional genomics datasets, and summary statistics deposited at NIAGADS. These results are made available to the public in a collection of interactive knowledgebases (AD Variant Portal, FILER Functional Genomics Repository, VariXam, Alzheimer's GenomicsDB & Genome Browser), all of which are accessible programmatically via the NIAGADS API.
View Article and Find Full Text PDFNat Commun
January 2025
Innovative Genomics Institute; University of California, Berkeley, CA, USA.
Molecular structure prediction and homology detection offer promising paths to discovering protein function and evolutionary relationships. However, current approaches lack statistical reliability assurances, limiting their practical utility for selecting proteins for further experimental and in-silico characterization. To address this challenge, we introduce a statistically principled approach to protein search leveraging principles from conformal prediction, offering a framework that ensures statistical guarantees with user-specified risk and provides calibrated probabilities (rather than raw ML scores) for any protein search model.
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