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Precision medicine has the potential to provide more accurate diagnosis, appropriate treatment and timely prevention strategies by considering patients' biological makeup. However, this cannot be realized without integrating clinical and omics data in a data-sharing framework that achieves large sample sizes. Systems that integrate clinical and genetic data from multiple sources are scarce due to their distinct data types, interoperability, security and data ownership issues. Here we present a secure framework that allows immutable storage, querying and analysis of clinical and genetic data using blockchain technology. Our platform allows clinical and genetic data to be harmonized by combining them under a unified framework. It supports combined genotype-phenotype queries and analysis, gives institutions control of their data and provides immutable user access logs, improving transparency into how and when health information is used. We demonstrate the value of our framework for precision medicine by creating genotype-phenotype cohorts and examining relationships within them. We show that combining data across institutions using our secure platform increases statistical power for rare disease analysis. By offering an integrated, secure and decentralized framework, we aim to enhance reproducibility and encourage broader participation from communities and patients in data sharing.
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OBJECTIVE: Linear and logistic regression are widely used statistical techniques in population genetics for analyzing genetic data and uncovering patterns and associations in large genetic datasets, such as identifying genetic variations linked to specific diseases or traits. However, obtaining statistically significant results from these studies requires large amounts of sensitive genotype and phenotype information from thousands of patients, which raises privacy concerns. Although cryptographic techniques such as homomorphic encryption offers a potential solution to the privacy concerns as it allows computations on encrypted data, previous methods leveraging homomorphic encryption have not addressed the confidentiality of shared models, which can leak information about the training data. METHODS: In this work, we present a secure model evaluation method for linear and logistic regression using homomorphic encryption for six prediction tasks, where input genotypes, output phenotypes, and model parameters are all encrypted. RESULTS: Our method ensures no private information leakage during inference and achieves high accuracy (≥93% for all outcomes) with each inference taking less than ten seconds for â¼200 genomes. CONCLUSION: Our study demonstrates that it is possible to perform linear and logistic regression model evaluation while protecting patient confidentiality with theoretical security guarantees. Our implementation and test data are available at https://github.com/G2Lab/privateML/.
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Segurança Computacional , Genótipo , Modelos Logísticos , Humanos , Modelos Lineares , Fenótipo , Confidencialidade , Privacidade , Algoritmos , Bases de Dados GenéticasRESUMO
Suicide is the second leading cause of death in U.S. adolescents and young adults and is generally associated with a psychiatric disorder. Suicidal behavior has a complex etiology and pathogenesis. Moderate heritability suggests genetic causes. Associations between childhood and recent life adversity indicate contributions from epigenetic factors. Genomic contributions to suicide pathogenesis remain largely unknown. This article is based on a workshop held to design strategies to identify molecular drivers of suicide neurobiology that would be putative new treatment targets. The panel determined that while bulk tissue studies provide comprehensive information, single-nucleus approaches that identify cell type-specific changes are needed. While single-nuclei techniques lack information on cytoplasm, processes, spines, and synapses, spatial multiomic technologies on intact tissue detect cell alterations specific to brain tissue layers and subregions. Because suicide has genetic and environmental drivers, multiomic approaches that combine cell type-specific epigenome, transcriptome, and proteome provide a more complete picture of pathogenesis. To determine the direction of effect of suicide risk gene variants on RNA and protein expression and how these interact with epigenetic marks, single-nuclei and spatial multiomics quantitative trait loci maps should be integrated with whole-genome sequencing and genome-wide association databases. The workshop concluded with a recommendation for the formation of an international suicide biology consortium that will bring together brain banks and investigators with expertise in cutting-edge omics technologies to delineate the biology of suicide and identify novel potential treatment targets to be tested in cellular and animal models for drug and biomarker discovery to guide suicide prevention.
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Cloud computing provides the opportunity to store the ever-growing genotype-phenotype data sets needed to achieve the full potential of precision medicine. However, due to the sensitive nature of this data and the patchwork of data privacy laws across states and countries, additional security protections are proving necessary to ensure data privacy and security. Here we present SQUiD, a secure queryable database for storing and analyzing genotype-phenotype data. With SQUiD, genotype-phenotype data can be stored in a low-security, low-cost public cloud in the encrypted form, which researchers can securely query without the public cloud ever being able to decrypt the data. We demonstrate the usability of SQUiD by replicating various commonly used calculations such as polygenic risk scores, cohort creation for GWAS, MAF filtering, and patient similarity analysis both on synthetic and UK Biobank data. Our work represents a new and scalable platform enabling the realization of precision medicine without security and privacy concerns.
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The three-dimensional organization of genomes plays a crucial role in essential biological processes. The segregation of chromatin into A and B compartments highlights regions of activity and inactivity, providing a window into the genomic activities specific to each cell type. Yet, the steep costs associated with acquiring Hi-C data, necessary for studying this compartmentalization across various cell types, pose a significant barrier in studying cell type specific genome organization. To address this, we present a prediction tool called compartment prediction using recurrent neural networks (CoRNN), which predicts compartmentalization of 3D genome using histone modification enrichment. CoRNN demonstrates robust cross-cell-type prediction of A/B compartments with an average AuROC of 90.9%. Cell-type-specific predictions align well with known functional elements, with H3K27ac and H3K36me3 identified as highly predictive histone marks. We further investigate our mispredictions and found that they are located in regions with ambiguous compartmental status. Furthermore, our model's generalizability is validated by predicting compartments in independent tissue samples, which underscores its broad applicability.
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BACKGROUND: Narrative nursing notes are a valuable resource in informatics research with unique predictive signals about patient care. The open sharing of these data, however, is appropriately constrained by rigorous regulations set by the Health Insurance Portability and Accountability Act (HIPAA) for the protection of privacy. Several models have been developed and evaluated on the open-source i2b2 dataset. A focus on the generalizability of these models with respect to nursing notes remains understudied. OBJECTIVES: The study aims to understand the generalizability of pretrained transformer models and investigate the variability of personal protected health information (PHI) distribution patterns between discharge summaries and nursing notes with a goal to inform the future design for model evaluation schema. METHODS: Two pretrained transformer models (RoBERTa, ClinicalBERT) fine-tuned on i2b2 2014 discharge summaries were evaluated on our data inpatient nursing notes and compared with the baseline performance. Statistical testing was deployed to assess differences in PHI distribution across discharge summaries and nursing notes. RESULTS: RoBERTa achieved the optimal performance when tested on an external source of data, with an F1 score of 0.887 across PHI categories and 0.932 in the PHI binary task. Overall, discharge summaries contained a higher number of PHI instances and categories of PHI compared with inpatient nursing notes. CONCLUSION: The study investigated the applicability of two pretrained transformers on inpatient nursing notes and examined the distinctions between nursing notes and discharge summaries concerning the utilization of personal PHI. Discharge summaries presented a greater quantity of PHI instances and types when compared with narrative nursing notes, but narrative nursing notes exhibited more diversity in the types of PHI present, with some pertaining to patient's personal life. The insights obtained from the research help improve the design and selection of algorithms, as well as contribute to the development of suitable performance thresholds for PHI.
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Narração , Humanos , Registros Eletrônicos de Saúde , Modelos TeóricosRESUMO
Single nucleotide polymorphisms (SNPs) from omics data create a reidentification risk for individuals and their relatives. Although the ability of thousands of SNPs (especially rare ones) to identify individuals has been repeatedly shown, the availability of small sets of noisy genotypes, from environmental DNA samples or functional genomics data, motivated us to quantify their informativeness. We present a computational tool suite, termed Privacy Leakage by Inference across Genotypic HMM Trajectories (PLIGHT), using population-genetics-based hidden Markov models (HMMs) of recombination and mutation to find piecewise alignment of small, noisy SNP sets to reference haplotype databases. We explore cases in which query individuals are either known to be in the database, or not, and consider several genotype queries, including those from environmental sample swabs from known individuals and from simulated "mosaics" (two-individual composites). Using PLIGHT on a database with â¼5000 haplotypes, we find for common, noise-free SNPs that only ten are sufficient to identify individuals, â¼20 can identify both components in two-individual mosaics, and 20-30 can identify first-order relatives. Using noisy environmental-sample-derived SNPs, PLIGHT identifies individuals in a database using â¼30 SNPs. Even when the individuals are not in the database, local genotype matches allow for some phenotypic information leakage based on coarse-grained SNP imputation. Finally, by quantifying privacy leakage from sparse SNP sets, PLIGHT helps determine the value of selectively sanitizing released SNPs without explicit assumptions about population membership or allele frequency. To make this practical, we provide a sanitization tool to remove the most identifying SNPs from genomic data.
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Genótipo , Haplótipos , Polimorfismo de Nucleotídeo Único , Humanos , Bases de Dados Genéticas , Cadeias de Markov , Software , Privacidade Genética , Algoritmos , Alinhamento de Sequência , Genética Populacional/métodosRESUMO
OBJECTIVE: The aim of this study was to evaluate the relationship between personal traits, dental anxiety level and dental appearance of the individuals. STUDY DESIGN: The study included 431 individuals who completed State Trait Anxiety Inventory-Trait Form (STAI-T) and Corah's Dental Anxiety Scale (CDAS) questionnaires during their first appointment at the orthodontic clinic. The Index of Complexity, Outcome and Need (ICON) index scoring was performed using intraoral frontal photographs by an orthodontist. According to the STAI-T scores, three anxiety groups were formed: mild, moderate, and severe. The Kruskal-Wallis H test was used for intergroup comparisons. Spearman's correlation analysis was performed to evaluate the relationship between STAI-T, CDAS, and ICON scores. RESULTS: It was found that 38.28% of the participants had mild, 34.1% had severe, and 27.62% had moderate anxiety levels. CDAS score was significantly lower in the mild anxiety group (p ≤ 0.0001) compared to the groups showing moderate and severe anxiety. There was no significant difference between the moderate and severe anxiety groups. ICON score was significantly higher in the severe anxiety group (p ≤ 0.0001) than the other groups. It was also significantly higher in the moderate anxiety group (p ≤ 0.0001) than in the mild anxiety group. There was a significant positive correlation between STAI-T and both CDAS and ICON scores. There was no significant correlation between CDAS and ICON scores. CONCLUSION: Dental appearance had a significant effect on the general anxiety of individuals. Improving the dental appearance with orthodontic treatments can have positive effects on reducing anxiety. The low level of dental anxiety in individuals with a high need for treatment will facilitate the work of the orthodontist in the procedures to be applied.
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Ansiedade , Assistência Odontológica , Humanos , Inquéritos e Questionários , Ansiedade ao Tratamento OdontológicoRESUMO
Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (â¼30 tissues × â¼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.
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Epigenoma , Locos de Características Quantitativas , Estudo de Associação Genômica Ampla , Genômica , Fenótipo , Polimorfismo de Nucleotídeo ÚnicoRESUMO
MOTIVATION: Linkage disequilibrium (LD) matrices derived from large populations are widely used in population genetics in fine-mapping, LD score regression, and linear mixed models for Genome-wide Association Studies (GWAS). However, these matrices can reach large sizes when they are derived from millions of individuals; hence, moving, sharing and extracting granular information from this large amount of data can be cumbersome. RESULTS: We sought to address the need for compressing and easily querying large LD matrices by developing LDmat. LDmat is a standalone tool to compress large LD matrices in an HDF5 file format and query these compressed matrices. It can extract submatrices corresponding to a sub-region of the genome, a list of select loci, and loci within a minor allele frequency range. LDmat can also rebuild the original file formats from the compressed files. AVAILABILITY AND IMPLEMENTATION: LDmat is implemented in python, and can be installed on Unix systems with the command 'pip install ldmat'. It can also be accessed through https://github.com/G2Lab/ldmat and https://pypi.org/project/ldmat/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Compressão de Dados , Software , Humanos , Desequilíbrio de Ligação , Estudo de Associação Genômica Ampla , GenomaRESUMO
Cancer genomics tailors diagnosis and treatment based on an individual's genetic information and is the crux of precision medicine. However, analysis and maintenance of high volume of genetic mutation data to build a machine learning (ML) model to predict the cancer type is a computationally expensive task and is often outsourced to powerful cloud servers, raising critical privacy concerns for patients' data. Homomorphic encryption (HE) enables computation on encrypted data, thus, providing cryptographic guarantees to protect privacy. But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer type prediction using a dataset consisting of more than 2 million genetic mutations from 2713 patients for several cancer types by building a highly accurate ML model and then implementing its privacy preserving version in HE. Our solution for cancer type inference encodes somatic mutations based on their impact on the cancer genomes into the feature space and then uses statistical tests for feature selection. We propose a fast matrix multiplication algorithm for HE-based model. Our final model achieves 0.98 micro-average area under curve improving accuracy from 70.08 to 83.61% , being 550 times faster than the standard matrix multiplication-based privacy-preserving models. Our tool can be found at https://github.com/momalab/octal-candet .
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Neoplasias , Privacidade , Humanos , Segurança Computacional , Algoritmos , Genômica , Neoplasias/genéticaRESUMO
Expansion of a single repetitive DNA sequence, termed a tandem repeat (TR), is known to cause more than 50 diseases1,2. However, repeat expansions are often not explored beyond neurological and neurodegenerative disorders. In some cancers, mutations accumulate in short tracts of TRs, a phenomenon termed microsatellite instability; however, larger repeat expansions have not been systematically analysed in cancer3-8. Here we identified TR expansions in 2,622 cancer genomes spanning 29 cancer types. In seven cancer types, we found 160 recurrent repeat expansions (rREs), most of which (155/160) were subtype specific. We found that rREs were non-uniformly distributed in the genome with enrichment near candidate cis-regulatory elements, suggesting a potential role in gene regulation. One rRE, a GAAA-repeat expansion, located near a regulatory element in the first intron of UGT2B7 was detected in 34% of renal cell carcinoma samples and was validated by long-read DNA sequencing. Moreover, in preliminary experiments, treating cells that harbour this rRE with a GAAA-targeting molecule led to a dose-dependent decrease in cell proliferation. Overall, our results suggest that rREs may be an important but unexplored source of genetic variation in human cancer, and we provide a comprehensive catalogue for further study.
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Expansão das Repetições de DNA , Genoma Humano , Neoplasias , Humanos , Sequência de Bases , Expansão das Repetições de DNA/genética , Genoma Humano/genética , Neoplasias/classificação , Neoplasias/genética , Neoplasias/patologia , Análise de Sequência de DNA , Regulação da Expressão Gênica , Elementos Reguladores de Transcrição/genética , Íntrons/genética , Carcinoma de Células Renais/genética , Carcinoma de Células Renais/patologia , Proliferação de Células/efeitos dos fármacos , Reprodutibilidade dos TestesRESUMO
Innovations in human-centered biomedical informatics are often developed with the eventual goal of real-world translation. While biomedical research questions are usually answered in terms of how a method performs in a particular context, we argue that it is equally important to consider and formally evaluate the ethical implications of informatics solutions. Several new research paradigms have arisen as a result of the consideration of ethical issues, including but not limited for privacy-preserving computation and fair machine learning. In the spirit of the Pacific Symposium on Biocomputing, we discuss broad and fundamental principles of ethical biomedical informatics in terms of Olelo Noeau, or Hawaiian proverbs and poetical sayings that capture Hawaiian values. While we emphasize issues related to privacy and fairness in particular, there are a multitude of facets to ethical biomedical informatics that can benefit from a critical analysis grounded in ethics.
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Biologia Computacional , Informática , Humanos , Havaí , PrivacidadeRESUMO
Federated Learning (FL) is a machine learning framework that enables multiple organizations to train a model without sharing their data with a central server. However, it experiences significant performance degradation if the data is non-identically independently distributed (non-IID). This is a problem in medical settings, where variations in the patient population contribute significantly to distribution differences across hospitals. Personalized FL addresses this issue by accounting for site-specific distribution differences. Clustered FL, a Personalized FL variant, was used to address this problem by clustering patients into groups across hospitals and training separate models on each group. However, privacy concerns remained as a challenge as the clustering process requires exchange of patient-level information. This was previously solved by forming clusters using aggregated data, which led to inaccurate groups and performance degradation. In this study, we propose Privacy-preserving Community-Based Federated machine Learning (PCBFL), a novel Clustered FL framework that can cluster patients using patient-level data while protecting privacy. PCBFL uses Secure Multiparty Computation, a cryptographic technique, to securely calculate patient-level similarity scores across hospitals. We then evaluate PCBFL by training a federated mortality prediction model using 20 sites from the eICU dataset. We compare the performance gain from PCBFL against traditional and existing Clustered FL frameworks. Our results show that PCBFL successfully forms clinically meaningful cohorts of low, medium, and high-risk patients. PCBFL outperforms traditional and existing Clustered FL frameworks with an average AUC improvement of 4.3% and AUPRC improvement of 7.8%.
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Delayed cerebral ischemia (DCI) is a complication seen in patients with subarachnoid hemorrhage stroke. It is a major predictor of poor outcomes and is detected late. Machine learning models are shown to be useful for early detection, however training such models suffers from small sample sizes due to rarity of the condition. Here we propose a Federated Learning approach to train a DCI classifier across three institutions to overcome challenges of sharing data across hospitals. We developed a framework for federated feature selection and built a federated ensemble classifier. We compared the performance of FL model to that obtained by training separate models at each site. FL significantly improved performance at only two sites. We found that this was due to feature distribution differences across sites. FL improves performance in sites with similar feature distributions, however, FL can worsen performance in sites with heterogeneous distributions. The results highlight both the benefit of FL and the need to assess dataset distribution similarity before conducting FL.
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Background: The tooth movements were generally analyzed in two dimensions on cephalometric radiographs. Nowaday, 3D digital model analysis, which does not have any harmful effects on patients, can be used to evaluate the palatal morphology and coronal tooth movements in a very comfortable and easy way. Aims: To investigate the effect of palatal morphology on anchorage reinforcement during intraoral molar distalization with pendulum appliance using 3D model analysis. Materials and Methods: The material consisted of before (T0) and after (T1) dental plaster models of Class II malocclusion patients (17 females, 3 males) treated with pendulum appliance for molar distalization and Nance appliance for anchorage. T0 and T1 digital models were superimposed using the palatal area as a reference via three points and surface-matching software, and the changes in teeth movement were calculated for left and right central incisors, first premolars, and first and second molars. Palatal morphology was evaluated at T0 on digital models as palatal inclination, palatal depth angles, and anterior hard palate area. Wilcoxon test was used to evaluate the treatment results and Spearman's correlation analysis was performed to evaluate the relationship between palatal morphology and dental movement. The upper limit for the level of significance was taken as 0.05. Results: Mesial movement of first premolars and distal movement of first and second molars were found to be statistically significant (P < 0.001). A weak negative correlation was found between the palatal inclination and the movement of first premolars (P < 0.045 and P < 0.003). Palatal depth angles and anterior hard palate area had no correlation with dental movements. Conclusion: Presented results supported that the mesial movement of premolar teeth decreased as the inclination of the palate increased.
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Má Oclusão Classe II de Angle , Maxila , Masculino , Feminino , Humanos , Maxila/diagnóstico por imagem , Técnicas de Movimentação Dentária , Dente Molar/diagnóstico por imagem , Má Oclusão Classe II de Angle/diagnóstico por imagem , Má Oclusão Classe II de Angle/terapia , Dente Pré-Molar , Cefalometria , Palato Duro/diagnóstico por imagemRESUMO
Machine learning is playing an increasingly critical role in health science with its capability of inferring valuable information from high-dimensional data. More training data provides greater statistical power to generate better models that can help decision-making in healthcare. However, this often requires combining research and patient data across institutions and hospitals, which is not always possible due to privacy considerations. In this paper, we outline a simple federated learning algorithm implementing differential privacy to ensure privacy when training a machine learning model on data spread across different institutions. We tested our model by predicting breast cancer status from gene expression data. Our model achieves a similar level of accuracy and precision as a single-site non-private neural network model when we enforce privacy. This result suggests that our algorithm is an effective method of implementing differential privacy with federated learning, and clinical data scientists can use our general framework to produce differentially private models on federated datasets. Our framework is available at https://github.com/gersteinlab/idash20FL.
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Aprendizado de Máquina , Privacidade , Algoritmos , HumanosRESUMO
OBJECTIVE: Hereditary angioedema (HAE) is clinically characterized by recurrent attacks of angioedema. This study evaluated the clinical findings and examination results of patients admitted due to angioedema who then underwent a diagnostic test for HAE. The study aimed to assess the contribution of laboratory findings to the diagnostic process and to determine clinicians' level of awareness regarding the differential diagnosis of angioedema and the appropriate laboratory tests. METHODS: Pediatric patients suspected to have HAE based on the presence of angioedema and screened for C1 esterase inhibitor levels and/or function were included in the study. RESULTS: A total of 136 patients were evaluated for a preliminary diagnosis of HAE in the presence of angioedema. Angioedema was accompanied by urticaria in 65 patients (47.7%) and itching in 24 patients (17.6%). Patients were evaluated using laboratory tests, C4 levels were studied in 124 patients (91.1%) and were found to be within normal reference limits. C1 esterase inhibitor levels were studied in all patients and were found to be within normal limits. C1 esterase inhibitor function was also studied in 101 patients (74.2%) and was found to be within normal limits. DISCUSSION: It was concluded that clinicians keep HAE in mind when encountering angioedema, but that increasing their knowledge of clinical findings that assist in differential diagnosis among angioedema types would be useful. The study authors would like to emphasize that this topic should be included in the specialty training curriculum to raise the awareness of clinicians, especially pediatricians, about clinical HAE findings and the algorithmic approach to the differential diagnosis of angioedema.
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Angioedema , Angioedemas Hereditários , Angioedema/diagnóstico , Angioedemas Hereditários/diagnóstico , Criança , Proteína Inibidora do Complemento C1 , Diagnóstico Diferencial , Testes Diagnósticos de Rotina , HumanosRESUMO
There are major efforts underway to make genome sequencing a routine part of clinical practice. A critical barrier to these is achieving practical solutions for data ownership and integrity. Blockchain provides solutions to these challenges in other realms, such as finance. However, its use in genomics is stymied due to the difficulty in storing large-scale data on-chain, slow transaction speeds, and limitations on querying. To overcome these roadblocks, we developed a private blockchain network to store genomic variants and reference-aligned reads on-chain. It uses nested database indexing with an accompanying tool suite to rapidly access and analyze the data.
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Blockchain , Genoma , GenômicaRESUMO
Genomics data are important for advancing biomedical research, improving clinical care, and informing other disciplines such as forensics and genealogy. However, privacy concerns arise when genomic data are shared. In particular, the identifying nature of genetic information, its direct relationship to health status, and the potential financial harm and stigmatization posed to individuals and their blood relatives call for a survey of the privacy issues related to sharing genetic and related data and potential solutions to overcome these issues. In this work, we provide an overview of the importance of genomic privacy, the information gleaned from genomics data, the sources of potential private information leakages in genomics, and ways to preserve privacy while utilizing the genetic information in research. We discuss the relationship between trust in the scientific community and protecting privacy, illuminating a future roadmap for data sharing and study participation.