Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond DOI Creative Commons
Ivo C. Leist,

María Rivas-Torrubia,

Marta E. Alarcón‐Riquelme

et al.

BMC Bioinformatics, Journal Year: 2024, Volume and Issue: 25(1)

Published: Dec. 4, 2024

Abstract Background Phenotypic data comparison is essential for disease association studies, patient stratification, and genotype–phenotype correlation analysis. To support these efforts, the Global Alliance Genomics Health (GA4GH) established Phenopackets v2 Beacon standards storing, sharing, discovering genomic phenotypic data. These provide a consistent framework organizing biological data, simplifying their transformation into computer-friendly formats. However, matching participants using GA4GH-based formats remains challenging, as current methods are not fully compatible, limiting effectiveness. Results Here, we introduce Pheno-Ranker, an open-source software toolkit individual-level of As input, it accepts JSON/YAML exchange from models, well any structure encoded in JSON, YAML, or CSV Internally, hierarchical flattened to one dimension then transformed through one-hot encoding. This allows efficient pairwise (all-to-all) comparisons within cohorts patient’s profile cohorts. Users have flexibility refine by including excluding terms, applying weights variables, obtaining statistical significance Z-scores p -values. The output consists text files, which can be further analyzed unsupervised learning techniques, such clustering multidimensional scaling (MDS), with graph analytics. Pheno-Ranker’s performance has been validated simulated synthetic showing its accuracy, robustness, efficiency across various health scenarios. A real use case PRECISESADS study highlights practical utility clinical research. Conclusions Pheno-Ranker user-friendly, lightweight semantic similarity analysis formats, extendable other types. It enables wide range variables beyond HPO OMIM terms while preserving full context. designed command-line tool additional utilities import, simulation, summary statistics plotting, QR code generation. For interactive analysis, also includes web-based user interface built R Shiny. Links online documentation, Google Colab tutorial, tool’s source available on project home page: https://github.com/CNAG-Biomedical-Informatics/pheno-ranker .

Language: Английский

Artificial Intelligence (AI) and Internet of Things (IoT) - based sensors for monitoring and controlling in architecture, engineering, and construction: applications, challenges, and opportunities DOI

Nitin Rane,

Saurabh Choudhary, Jayesh Rane

et al.

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

The fusion of Artificial Intelligence (AI) and the Internet Things (IoT) has brought about a paradigm shift in realm architecture, engineering, construction (AEC), introducing intelligent sensing technologies that significantly enhance monitoring control. This study delves into varied applications, hurdles, prospects emerging from collaborative deployment AI IoT-based sensors within AEC domain. AI-equipped smart enable real-time structural health, energy consumption, environmental conditions both buildings infrastructure. These empower predictive maintenance, ensuring durability structures while minimizing downtime. Additionally, AI-driven analytics optimize resource allocation, improve safety protocols, streamline processes, thereby enhancing overall project efficiency. Through ongoing analysis data collected by integrated HVAC systems, elevators, lighting, maintenance teams can pre-emptively tackle potential malfunctions. Furthermore, synergy between IoT enables development with adaptive features. Sensors examine occupancy patterns, lighting preferences, temperature fluctuations play pivotal role crafting energy-efficient occupant-centric building designs. security privacy concerns associated sensor-generated give rise to critical issues necessitate robust cybersecurity measures. Interoperability challenges among diverse sensor networks platforms also present obstacles seamless integration. adoption these demands substantial investments infrastructure workforce training, requiring strategic approach for widespread acceptance. paper explores how capabilities contribute risk mitigation cost reduction across entire lifecycle. Moreover, ability collect analyze vast amounts empowers stakeholders make well-informed decisions, fostering innovation sustainability industry. By addressing underscoring benefits, it provides invaluable insights industry professionals, researchers, policymakers eager harness transformative construction.

Language: Английский

Citations

26
Consensus reporting guidelines to address gaps in descriptions of ultra-rare genetic conditions DOI Creative Commons
Ali AlMail, Ahmed Jamjoom, Amy Pan

et al.

npj Genomic Medicine, Journal Year: 2024, Volume and Issue: 9(1)

Published: April 6, 2024

Abstract Genome-wide sequencing and genetic matchmaker services are propelling a new era of genotype-driven ascertainment novel conditions. The degree to which reported phenotype data in discovery-focused studies address informational priorities for clinicians families is unclear. We identified reports published from 2017 2021 10 genetics journals Mendelian disorders. adjudicated the quality detail via 46 questions pertaining six priority domains: (I) Development, cognition, mental health; (II) Feeding growth; (III) Medication use treatment history; (IV) Pain, sleep, life; (V) Adulthood; (VI) Epilepsy. For subset articles, all subsequent follow-up case descriptions were assessed similar manner. A modified Delphi approach was used develop consensus reporting guidelines, with input content experts across four countries. In total, 200 3243 screened publications met inclusion criteria. Relevant phenotypic details each 6 domains rated superficial or deficient >87% papers. example, less than 10% provided regarding neuropsychiatric diagnoses “behavioural issues”, about type/nature feeding problems. Follow-up ( n = 95) rarely contributed this additional data. summary, information relevant clinical management, counselling, stated patients lacking many newly described diseases. PHELIX (PHEnotype LIsting fiX) guideline checklists developed improve genomic era.

Language: Английский

Citations

4
A corpus of GA4GH phenopackets: case-level phenotyping for genomic diagnostics and discovery DOI Creative Commons
Daniel Daniš,

Michael J Bamshad,

Yasemin Bridges

et al.

Human Genetics and Genomics Advances, Journal Year: 2024, Volume and Issue: 6(1), P. 100371 - 100371

Published: Oct. 11, 2024

Language: Английский

Citations

4
GA4GH Phenopacket-Driven Characterization of Genotype-Phenotype Correlations in Mendelian Disorders DOI Creative Commons
Lauren Rekerle, Daniel Daniš, Filip Rehburg

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: March 6, 2025

ABSTRACT Comprehensively characterizing genotype-phenotype correlations (GPCs) in Mendelian disease would create new opportunities for improving clinical management and understanding biology. However, heterogeneous approaches to data sharing, reuse, analysis have hindered progress the field. We developed Genotype Phenotype Evaluation of Statistical Association (GPSEA), a software package that leverages Global Alliance Genomics Health (GA4GH) Phenopacket Schema represent case-level genetic about individuals. GPSEA applies an independent filtering strategy boost statistical power detect categorical GPCs represented by Human Ontology terms. additionally enables visualization continuous phenotypes, severity scores, survival such as age onset or manifestations. applied 85 cohorts with 6613 previously published individuals variants one 80 genes associated 122 diseases identified 225 significant GPCs, 48 having at least statistically GPC. These results highlight standardized representations scalable discovery disease.

Language: Английский

Citations

0
A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery DOI Creative Commons
Daniel Daniš,

Michael J Bamshad,

Yasemin Bridges

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 29, 2024

Summary The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 approved by ISO as a standard sharing clinical genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, treatments. A phenopacket can be used input file software that supports phenotype-driven diagnostics algorithms facilitate patient classification stratification identifying new diseases There has been great need collection of phenopackets to test pipelines algorithms. Here, we present phenopacket-store. Version 0.1.12 phenopacket-store includes 4916 representing 277 Mendelian chromosomal associated with 236 genes, 2872 unique pathogenic alleles curated from 605 different publications. This represents the first large-scale case-level, standardized derived case reports literature detailed descriptions data will useful many purposes, development testing prioritizing genes diagnostic genomics, machine learning analysis phenotype data, stratification, genotype-phenotype correlations. corpus also provides best-practice examples curating literature-derived using GA4GH Schema.

Language: Английский

Citations

3
Convert-Pheno: A software toolkit for the interconversion of standard data models for phenotypic data DOI Creative Commons
Manuel Rueda, Ivo C. Leist, Marta Gut

et al.

Journal of Biomedical Informatics, Journal Year: 2023, Volume and Issue: 149, P. 104558 - 104558

Published: Nov. 29, 2023

Efficient sharing and integration of phenotypic data is crucial for advancing biomedical research enhancing patient outcomes in precision medicine public health. To achieve this, the health community has developed standards to promote harmonization variable names values. However, use diverse across different centers can hinder progress. Here we present Convert-Pheno, an open-source software toolkit that enables interconversion common models such as Beacon v2 Models, CDISC-ODM, OMOP-CDM, Phenopackets v2, REDCap. Along with software, have created a detailed documentation includes information on deployment installation.

Language: Английский

Citations

4
Converting OMOP CDM to phenopackets: A model alignment and patient data representation evaluation DOI
Kayla Schiffer-Kane, Cong Liu, Tiffany J Callahan

et al.

Journal of Biomedical Informatics, Journal Year: 2024, Volume and Issue: 155, P. 104659 - 104659

Published: May 21, 2024

Language: Английский

Citations

1
Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond DOI Creative Commons
Ivo C. Leist,

María Rivas-Torrubia,

Marta E. Alarcón‐Riquelme

et al.

BMC Bioinformatics, Journal Year: 2024, Volume and Issue: 25(1)

Published: Dec. 4, 2024

Abstract Background Phenotypic data comparison is essential for disease association studies, patient stratification, and genotype–phenotype correlation analysis. To support these efforts, the Global Alliance Genomics Health (GA4GH) established Phenopackets v2 Beacon standards storing, sharing, discovering genomic phenotypic data. These provide a consistent framework organizing biological data, simplifying their transformation into computer-friendly formats. However, matching participants using GA4GH-based formats remains challenging, as current methods are not fully compatible, limiting effectiveness. Results Here, we introduce Pheno-Ranker, an open-source software toolkit individual-level of As input, it accepts JSON/YAML exchange from models, well any structure encoded in JSON, YAML, or CSV Internally, hierarchical flattened to one dimension then transformed through one-hot encoding. This allows efficient pairwise (all-to-all) comparisons within cohorts patient’s profile cohorts. Users have flexibility refine by including excluding terms, applying weights variables, obtaining statistical significance Z-scores p -values. The output consists text files, which can be further analyzed unsupervised learning techniques, such clustering multidimensional scaling (MDS), with graph analytics. Pheno-Ranker’s performance has been validated simulated synthetic showing its accuracy, robustness, efficiency across various health scenarios. A real use case PRECISESADS study highlights practical utility clinical research. Conclusions Pheno-Ranker user-friendly, lightweight semantic similarity analysis formats, extendable other types. It enables wide range variables beyond HPO OMIM terms while preserving full context. designed command-line tool additional utilities import, simulation, summary statistics plotting, QR code generation. For interactive analysis, also includes web-based user interface built R Shiny. Links online documentation, Google Colab tutorial, tool’s source available on project home page: https://github.com/CNAG-Biomedical-Informatics/pheno-ranker .

Language: Английский

Citations

1