Big Data Bioinformatics

Chen, Xue wen; Gao, Jean

doi:10.1016/j.ymeth.2016.11.017

Cited by 18 publications

(4 citation statements)

References 29 publications

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“…The term "Big Data" refers to the recording and analysis of data sets which are so large, multidimensional, diverse, and complex that traditional software solutions are not adequate to process them [68]. The advent of Big Data is the result of the development of novel technologies that surpass the capacity of paper-based information management [69].…”

Section: Big Datamentioning

confidence: 99%

Impact Of “eHealth” in Allergic Diseases and Allergic Patients

Alvarez-Perea¹,

Sánchez-García²,

R³

et al. 2019

J Investig Allergol Clin Immunol

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The emergence of new technology enables allergists and patients to compile data and receive feedback regarding key symptoms, risk behavior, and/or management. The term "eHealth" refers to a diverse group of tools that use computerized technologies to improve both the efficacy and the efficiency of the health care industry. eHealth comprises a variety of technologies, as follows: mobile devices (mHealth) in medical care, including electronic diaries, wearable sensors, and adherence monitoring; health informatics (eg, electronic health records, computerized physician order entry, clinical decision support); telemedicine, which is the use of information and communication technologies for the management of diseases and medical education; social media platforms, and the analysis of information acquired through these platforms using "big data" technologies. In this review, we summarize the latest findings on the use of eHealth technology and the relevance of eHealth to allergic conditions. ResumenLa aparición de nuevas tecnologías conlleva para los alergólogos y los pacientes la posibilidad de recopilar datos y recibir información directa sobre los síntomas clave de las enfermedades, los comportamientos de riesgo y/o su manejo. El término "eHealth", o salud electrónica, se refiere a un grupo diverso de herramientas que utilizan tecnologías informáticas para mejorar la eficacia y la eficiencia de la industria de la salud. La "eHealth" comprende varias tecnologías, como el uso de dispositivos móviles aplicados a la salud ("mHealth"), incluyendo diarios electrónicos, sensores ponibles o monitorización de la adherencia terapéutica; la informática biomédica (por ejemplo, la historia clínica electrónica, la prescripción electrónica o los sistemas de ayuda a las decisiones clínicas); la telemedicina, que es el uso de las tecnologías de la información y la comunicación para el manejo de enfermedades y de educación sanitaria; las plataformas de redes sociales, y el análisis de la información adquirida a través de estas plataformas, usando técnicas de "big data" o inteligencia de datos. En esta revisión, resumimos la evidencia que rodea al uso de tecnologías "eHealth" y su relevancia para las enfermedades alérgicas.

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Section: Big Datamentioning

confidence: 99%

Impact Of “eHealth” in Allergic Diseases and Allergic Patients

Alvarez-Perea¹,

Sánchez-García²,

R³

et al. 2019

J Investig Allergol Clin Immunol

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“…Based on R software, we performed classification, regression, clustering, dimension-reduction, model selection, statistical analysis, and data preprocessing, which can quickly complete the entire process of ML ( 27 ). In the present study, we considered patients whose mp-MRI examination indicated PI-RADS v2 4/5 score and used ImageJ and other software to extract the TF of all lesions.…”

Section: Discussionmentioning

confidence: 99%

Texture analysis based on PI-RADS 4/5-scored magnetic resonance images combined with machine learning to distinguish benign lesions from prostate cancer

Ma¹,

Zhou²,

Yin³

et al. 2022

Transl Cancer Res TCR

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Background The global morbidity and mortality of prostate cancer (PCa) increase sharply every year. Early diagnosis is essential; it determines survival and outcome. So, this study extracted the texture features of apparent diffusion coefficient images in multiparametric magnetic resonance imaging (mp-MRI) and built machine learning models based on radiomics texture analysis (TA) to determine its ability to distinguish benign from PCa lesions using the Prostate Imaging Reporting and Data System (PI-RADS) 4/5 score. Methods We enrolled 103 patients who underwent mp-MRI examinations and transrectal ultrasound and magnetic resonance fusion imaging (TRUS-MRI) targeted prostate biopsy and obtained pathological confirmation at our hospital from August 2017 to January 2020. We used ImageJ software to obtain texture feature parameters based on apparent diffusion coefficient (ADC) images, then standardized texture feature parameters, and used LASSO regression to reduce multiple feature parameters; 70% of the cases were randomly selected from the PCa group and the benign prostate hyperplasia group as the training set. The remaining 30% was used as the test set. The machine learning classification model for identifying benign and malignant prostate lesions was constructed using the feature parameters after dimensionality reduction. The clinical indicators were statistically analyzed, and we constructed a machine learning classification model based on clinical indicators of benign and malignant prostate lesions. Finally, we compared the model’s performance based on radiomics texture features and clinical indicators to identify benign and malignant prostate lesions in PI-RADS 4/5 score. Results The area under the curve (AUC) of the R-logistic model test set was 0.838, higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-logistic classification model formula is as follow: Y_radiomics=9.396-7.464*median ADC-0.584*kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the corresponding C-logistic classification model formula is as follows: Y_clinical =-2.608+0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set was 0.860, smaller than the training set R-logistic classification model AUC value of 0.936. Conclusions Radiomics combined with the machine learning classifier model has strong classification performance in identifying benign and PCa in PI-RADS 4/5 score. Various treatments and outcomes for PCa patients can be applied clinically.

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“…The intragroup data repeatability of each group was verified by Pearson's correlation. R programming language provides operating environment and software for drawing of graphs and statistical analysis [ 15 ]. Use heatmaps to visualize correlations between samples in the same dataset and using R to draw the heatmap.…”

Section: Methodsmentioning

confidence: 99%

Autophagy-Related Genes in Atherosclerosis

Chen

Zeng

et al. 2021

Journal of Healthcare Engineering

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Background. Atherosclerosis (AS) is a common chronic vascular inflammatory disease and one of the main causes of cardiovascular/cerebrovascular diseases (CVDs). Autophagy-related genes (ARGs) play a crucial part in pathophysiological processes of AS. However, the expression profile of ARGs has rarely been adopted to explore the relationship between autophagy and AS. Therefore, using the expression profile of ARGs to explore the relationship between autophagy and AS may provide new insights for the treatment of CVDs. Methods. The differentially expressed ARGs of the GSE57691 dataset were obtained from the Human Autophagy Database (HADb) and the Gene Expression Omnibus (GEO) database, and the GSE57691 dataset contains 9 aortic atheroma tissues and 10 normal aortic tissues. The differentially expressed ARGs of the GSE57691 dataset were analyzed by protein-protein interaction (PPI), gene ontology analysis (GO), and Kyoto Encyclopedia of Genes and Genomes analysis (KEGG) and were chosen to explore related miRNAs/transcriptional factors. Results. The GSE57691 dataset had a total of 41 differentially expressed ARGs. The GO analysis results revealed that ARGs were mainly enriched in autophagy, autophagosome, and protein serine/threonine kinase activity. KEGG analysis results showed that ARGs were mainly enriched in autophagy-animal and longevity regulating signaling pathways. Expressions of ATG5, MAP1LC3B, MAPK3, MAPK8, and RB1CC1 were regarded as focus in the PPI regulatory networks. Furthermore, 11 related miRNAs and 6 related transcription factors were obtained by miRNAs/transcription factor target network analysis. Conclusions. Autophagy and ARGs may play a vital role in regulating the pathophysiology of AS.

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Big Data Bioinformatics

Cited by 18 publications

References 29 publications

Impact Of “eHealth” in Allergic Diseases and Allergic Patients

Impact Of “eHealth” in Allergic Diseases and Allergic Patients

Texture analysis based on PI-RADS 4/5-scored magnetic resonance images combined with machine learning to distinguish benign lesions from prostate cancer

Autophagy-Related Genes in Atherosclerosis

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