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1.
J Biomed Inform ; 139: 104303, 2023 03.
Artigo em Inglês | MEDLINE | ID: mdl-36736449

RESUMO

Expert microscopic analysis of cells obtained from frequent heart biopsies is vital for early detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare condition is prone to low levels of expert agreement due to the difficulty of identifying subtle rejection signs within biopsy samples. The rarity of pediatric heart transplant rejection also means that very few gold-standard images are available for developing machine learning models. To solve this urgent clinical challenge, we developed a deep learning model to automatically quantify rejection risk within digital images of biopsied tissue using an explainable synthetic data augmentation approach. We developed this explainable AI framework to illustrate how our progressive and inspirational generative adversarial network models distinguish between normal tissue images and those containing cellular rejection signs. To quantify biopsy-level rejection risk, we first detect local rejection features using a binary image classifier trained with expert-annotated and synthetic examples. We converted these local predictions into a biopsy-wide rejection score via an interpretable histogram-based approach. Our model significantly improves upon prior works with the same dataset with an area under the receiver operating curve (AUROC) of 98.84% for the local rejection detection task and 95.56% for the biopsy-rejection prediction task. A biopsy-level sensitivity of 83.33% makes our approach suitable for early screening of biopsies to prioritize expert analysis. Our framework provides a solution to rare medical imaging challenges currently limited by small datasets.


Assuntos
Insuficiência Cardíaca , Transplante de Coração , Humanos , Criança , Diagnóstico por Imagem , Aprendizado de Máquina , Medição de Risco , Complicações Pós-Operatórias
2.
Blood ; 120(15): 2981-9, 2012 Oct 11.
Artigo em Inglês | MEDLINE | ID: mdl-22936656

RESUMO

Increased expression of Kruppel-like factor 7 (KLF7) is an independent predictor of poor outcome in pediatric acute lymphoblastic leukemia. The contribution of KLF7 to hematopoiesis has not been previously described. Herein, we characterized the effect on murine hematopoiesis of the loss of KLF7 and enforced expression of KLF7. Long-term multilineage engraftment of Klf7(-/-) cells was comparable with control cells, and self-renewal, as assessed by serial transplantation, was not affected. Enforced expression of KLF7 results in a marked suppression of myeloid progenitor cell growth and a loss of short- and long-term repopulating activity. Interestingly, enforced expression of KLF7, although resulting in multilineage growth suppression that extended to hematopoietic stem cells and common lymphoid progenitors, spared T cells and enhanced the survival of early thymocytes. RNA expression profiling of KLF7-overexpressing hematopoietic progenitors identified several potential target genes mediating these effects. Notably, the known KLF7 target Cdkn1a (p21(Cip1/Waf1)) was not induced by KLF7, and loss of CDKN1A does not rescue the repopulating defect. These results suggest that KLF7 is not required for normal hematopoietic stem and progenitor function, but increased expression, as seen in a subset of lymphoid leukemia, inhibits myeloid cell proliferation and promotes early thymocyte survival.


Assuntos
Células-Tronco Hematopoéticas/patologia , Fatores de Transcrição Kruppel-Like/fisiologia , Células Progenitoras Linfoides/patologia , Células Progenitoras Mieloides/patologia , Células-Tronco/patologia , Linfócitos T/patologia , Animais , Apoptose , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Western Blotting , Diferenciação Celular , Proliferação de Células , Inibidor de Quinase Dependente de Ciclina p21/genética , Inibidor de Quinase Dependente de Ciclina p21/metabolismo , Feminino , Citometria de Fluxo , Perfilação da Expressão Gênica , Hematopoese , Células-Tronco Hematopoéticas/metabolismo , Células Progenitoras Linfoides/metabolismo , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Knockout , Células Progenitoras Mieloides/metabolismo , Análise de Sequência com Séries de Oligonucleotídeos , RNA Mensageiro/genética , Reação em Cadeia da Polimerase em Tempo Real , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Células-Tronco/metabolismo , Linfócitos T/metabolismo
3.
IEEE Rev Biomed Eng ; 17: 80-97, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-37824325

RESUMO

With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.


Assuntos
Inteligência Artificial , Multiômica , Humanos , Medicina de Precisão , Registros Eletrônicos de Saúde , Atenção à Saúde
4.
Sci Rep ; 13(1): 19488, 2023 11 09.
Artigo em Inglês | MEDLINE | ID: mdl-37945586

RESUMO

Recent advances in artificial intelligence (AI) have sparked interest in developing explainable AI (XAI) methods for clinical decision support systems, especially in translational research. Although using XAI methods may enhance trust in black-box models, evaluating their effectiveness has been challenging, primarily due to the absence of human (expert) intervention, additional annotations, and automated strategies. In order to conduct a thorough assessment, we propose a patch perturbation-based approach to automatically evaluate the quality of explanations in medical imaging analysis. To eliminate the need for human efforts in conventional evaluation methods, our approach executes poisoning attacks during model retraining by generating both static and dynamic triggers. We then propose a comprehensive set of evaluation metrics during the model inference stage to facilitate the evaluation from multiple perspectives, covering a wide range of correctness, completeness, consistency, and complexity. In addition, we include an extensive case study to showcase the proposed evaluation strategy by applying widely-used XAI methods on COVID-19 X-ray imaging classification tasks, as well as a thorough review of existing XAI methods in medical imaging analysis with evaluation availability. The proposed patch perturbation-based workflow offers model developers an automated and generalizable evaluation strategy to identify potential pitfalls and optimize their proposed explainable solutions, while also aiding end-users in comparing and selecting appropriate XAI methods that meet specific clinical needs in real-world clinical research and practice.


Assuntos
COVID-19 , Sistemas de Apoio a Decisões Clínicas , Humanos , Inteligência Artificial , COVID-19/diagnóstico por imagem , Raios X , Benchmarking
5.
IEEE Rev Biomed Eng ; 16: 5-21, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-35737637

RESUMO

Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users. Thus, we have conducted a comprehensive study of Explainable Artificial Intelligence (XAI) using PRISMA technology. Our findings suggest that XAI can improve model performance, instill trust in the users, and assist users in decision-making. In this systematic review, we introduce common XAI techniques and their utility with specific examples of their application. We discuss the evaluation of XAI results because it is an important step for maximizing the value of AI-based clinical decision support systems. Additionally, we present the traditional, modern, and advanced XAI models to demonstrate the evolution of novel techniques. Finally, we provide a best practice guideline that developers can refer to during the model experimentation. We also offer potential solutions with specific examples for common challenges in AI model experimentation. This comprehensive review, hopefully, can promote AI adoption in biomedicine and healthcare.


Assuntos
Inteligência Artificial , COVID-19 , Humanos , Pandemias , Atenção à Saúde
6.
Sci Rep ; 13(1): 18981, 2023 11 03.
Artigo em Inglês | MEDLINE | ID: mdl-37923795

RESUMO

Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.


Assuntos
COVID-19 , Humanos , Medição de Risco , Avaliação de Resultados em Cuidados de Saúde , Prognóstico , Aprendizado de Máquina , Estudos Retrospectivos
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 4687-4690, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36085809

RESUMO

Shriners Children's (SHC) is a hospital system whose mission is to advance the treatment and research of pediatric diseases. SHC success has generated a wealth of clinical data. Unfortunately, barriers to healthcare data access often limit data-driven clinical research. We decreased this burden by allowing access to clinical data via the standardized data access standard called FHIR (Fast Healthcare Interoperability Resources). Specifically, we converted existing data in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standard into FHIR data elements using a technology called OMOP-on-FHIR. In addition, we developed two applications leveraging the FHIR data elements to facilitate patient cohort curation to advance research into pediatric musculoskeletal diseases. Our work enables clinicians and clinical researchers to use hundreds of currently available open-sourced FHIR applications. Our successful implementation of OMOP-on-FHIR within a large hospital system will accelerate advancements in pediatric disease treatment and research.


Assuntos
Informática Médica , Doenças Musculoesqueléticas , Criança , Instalações de Saúde , Hospitais , Humanos , Tecnologia
8.
Front Oncol ; 10: 937, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32676453

RESUMO

MRI in combination with genomic markers are critical in the management of gliomas. Radiomics and radiogenomics analysis facilitate the quantitative assessment of tumor properties which can be used to model both molecular subtype and predict disease progression. In this work, we report on the Drosophila gene capicua (CIC) mutation biomarker effects alongside radiomics features on the predictive ability of CIC mutation status in lower-grade gliomas (LGG). Genomic data of lower grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA) (n = 509) and corresponding MR images from TCIA (n = 120) were utilized. Following tumor segmentation, radiomics features were extracted from T1, T2, T2 Flair, and T1 contrast enhanced (CE) images. Lasso feature reduction was used to obtain the most important MR image features and then logistic regression used to predict CIC mutation status. In our study, CIC mutation rarely occurred in Astrocytoma but has a high probability of occurrence in Oligodendroglioma. The presence of CIC mutation was found to be associated with better survival of glioma patients (p < 1e-4, HR: 0.2445), even with co-occurrence of IDH mutation and 1p/19q co-deletion (p = 0.0362, HR: 0.3674). An eleven-feature model achieved glioma prediction accuracy of 94.2% (95% CI, 94.03-94.38%), a six-feature model achieved oligodendroglioma prediction accuracy of 92.3% (95% CI, 91.70-92.92%). MR imaging and its derived image of gliomas with CIC mutation appears more complex and non-uniform but are associated with lower malignancy. Our study identified CIC as a potential prognostic factor in glioma which has close associations with survival. MRI radiomic features could predict CIC mutation, and reflect less malignant manifestations such as milder necrosis and larger tumor volume in MRI and its derived images that could help clinical judgment.

9.
Semin Cutan Med Surg ; 38(1): E43-E48, 2019 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-31051023

RESUMO

In this chapter, we present the use of whole slide imaging (WSI) and dermoscopy in the field of dermatology. Image digitization has allowed for increasing computer-assisted clinical decision-making. An introduction to common digital imaging data sources such as WSI and dermoscopy is provided. We also review some commonly used image quantification methods and their potential applications in dermatology. Finally, we review how machine learning approaches utilize novel large dermatology image datasets.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Dermoscopia , Neoplasias Cutâneas/diagnóstico , Humanos , Interpretação de Imagem Assistida por Computador , Processamento de Imagem Assistida por Computador , Aprendizado de Máquina , Neoplasias Cutâneas/diagnóstico por imagem
10.
Cancers (Basel) ; 10(10)2018 Oct 11.
Artigo em Inglês | MEDLINE | ID: mdl-30314329

RESUMO

BACKGROUND: Patients with locally advanced or recurrent prostate cancer typically undergo androgen deprivation therapy (ADT), but the benefits are often short-lived and the responses variable. ADT failure results in castration-resistant prostate cancer (CRPC), which inevitably leads to metastasis. We hypothesized that differences in tumor transcriptional programs may reflect differential responses to ADT and subsequent metastasis. RESULTS: We performed whole transcriptome analysis of 20 patient-matched Pre-ADT biopsies and 20 Post-ADT prostatectomy specimens, and identified two subgroups of patients (high impact and low impact groups) that exhibited distinct transcriptional changes in response to ADT. We found that all patients lost the AR-dependent subtype (PCS2) transcriptional signatures. The high impact group maintained the more aggressive subtype (PCS1) signal, while the low impact group more resembled an AR-suppressed (PCS3) subtype. Computational analyses identified transcription factor coordinated groups (TFCGs) enriched in the high impact group network. Leveraging a large public dataset of over 800 metastatic and primary samples, we identified 33 TFCGs in common between the high impact group and metastatic lesions, including SOX4/FOXA2/GATA4, and a TFCG containing JUN, JUNB, JUND, FOS, FOSB, and FOSL1. The majority of metastatic TFCGs were subsets of larger TFCGs in the high impact group network, suggesting a refinement of critical TFCGs in prostate cancer progression. CONCLUSIONS: We have identified TFCGs associated with pronounced initial transcriptional response to ADT, aggressive signatures, and metastasis. Our findings suggest multiple new hypotheses that could lead to novel combination therapies to prevent the development of CRPC following ADT.

11.
PLoS One ; 12(1): e0170339, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28118365

RESUMO

Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal.


Assuntos
Antineoplásicos/farmacologia , Mineração de Dados/métodos , Descoberta de Drogas , Proteínas de Neoplasias/metabolismo , Software , Adenocarcinoma/genética , Adenocarcinoma/mortalidade , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/mortalidade , Análise por Conglomerados , Técnicas de Silenciamento de Genes , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/mortalidade , Terapia de Alvo Molecular , Mutação , Proteínas de Neoplasias/antagonistas & inibidores , Proteínas de Neoplasias/genética , Redes Neurais de Computação , Mapeamento de Interação de Proteínas , Interferência de RNA , RNA Interferente Pequeno/farmacologia , Transdução de Sinais/efeitos dos fármacos , Transdução de Sinais/genética
12.
Prog Retin Eye Res ; 55: 1-31, 2016 11.
Artigo em Inglês | MEDLINE | ID: mdl-27297499

RESUMO

The advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well.


Assuntos
Biologia Computacional/métodos , Estudo de Associação Genômica Ampla , Genômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Doenças Retinianas/genética , Humanos
13.
Cell Rep ; 17(9): 2460-2473, 2016 11 22.
Artigo em Inglês | MEDLINE | ID: mdl-27880916

RESUMO

Gene regulatory networks (GRNs) guiding differentiation of cell types and cell assemblies in the nervous system are poorly understood because of inherent complexities and interdependence of signaling pathways. Here, we report transcriptome dynamics of differentiating rod photoreceptors in the mammalian retina. Given that the transcription factor NRL determines rod cell fate, we performed expression profiling of developing NRL-positive (rods) and NRL-negative (S-cone-like) mouse photoreceptors. We identified a large-scale, sharp transition in the transcriptome landscape between postnatal days 6 and 10 concordant with rod morphogenesis. Rod-specific temporal DNA methylation corroborated gene expression patterns. De novo assembly and alternative splicing analyses revealed previously unannotated rod-enriched transcripts and the role of NRL in transcript maturation. Furthermore, we defined the relationship of NRL with other transcriptional regulators and downstream cognate effectors. Our studies provide the framework for comprehensive system-level analysis of the GRN underlying the development of a single sensory neuron, the rod photoreceptor.


Assuntos
Fatores de Transcrição de Zíper de Leucina Básica/metabolismo , Proteínas do Olho/metabolismo , Regulação da Expressão Gênica no Desenvolvimento , Células Fotorreceptoras Retinianas Cones/metabolismo , Transcriptoma/genética , Processamento Alternativo/genética , Animais , Animais Recém-Nascidos , Diferenciação Celular/genética , Simulação por Computador , Metilação de DNA/genética , Redes Reguladoras de Genes , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Knockout , Anotação de Sequência Molecular , Regiões Promotoras Genéticas/genética , RNA Mensageiro/genética , RNA Mensageiro/metabolismo
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