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1.
Zhen Ci Yan Jiu ; 46(3): 240-7, 2021 Mar 25.
Artículo en Chino | MEDLINE | ID: mdl-33798299

RESUMEN

OBJECTIVE: To investigate the application characteristics of electroacupuncture (EA) in the treatment of peripheral neuropathy, so as to provide a basis for clinical use of EA therapy. METHODS: Keywords of "electroacupuncture""peripheral neuropathy" "facial paralysis" "trigeminal neuralgia" "sciatica" "common peroneal nerve injury" "diabetic peripheral neuropathy" "intercostal neuralgia" "gluteal epithelial neuritis" "ulnar nerve injury" "median nerve paralysis" "postherpetic neuralgia", and "great occipital neuralgia" were used to search articles in both English and Chinese published from 1999 to 2019 in databases of CNKI, Wanfang, VIP, CBM, Ovid, PubMed and Embase and related books such as electroacupuncture, and neurology, followed by establishing a database of "Electroacupuncture Treatment of Peripheral Neuropathy". Then, the collected articles were put into statistical analysis after sorting, screening, input, checking, and data extracting by using data mining technology and statistical software EpiData. RESULTS: Of the searched 1 528 papers, 778 were eligible, involving 13 types of peripheral neuropathy which the facial paralysis and facial spasm were most frequently seen, followed by trigeminal neuralgia and sciatica, with an effective rate being above 90% for nearly all the 13 diseases. The acupoints employed were chiefly those close to the affected area and distribute along the nerve trunk.In addition, about the needling techniques, the penetration needling was frequently used, and the triple needling, quintuple needling and accompanied needling were also used. Regarding the related needle manipulations, the uniform reinforcing-reduction technique was most frequently used. The duration of EA was 30 min, with a highest stimulating frequency of 50 Hz. The acupoint injection was frequently supplemented, followed by moxibustion, and the treatment sessions were usually about 30 times. CONCLUSION: EA therapy is frequently used in the treatment of peripheral neuropathy, and has some characteristics in acupoint selection, stimulating parameters and some additional needling techniques.


Asunto(s)
Terapia por Acupuntura , Electroacupuntura , Moxibustión , Puntos de Acupuntura , Minería de Datos
2.
Zhongguo Zhen Jiu ; 41(3): 355-8, 2021 Mar 12.
Artículo en Chino | MEDLINE | ID: mdl-33798325

RESUMEN

OBJECTIVE: To explore the rule of point selection in treatment of cerebral palsy with acupuncture in preschool children. METHODS: Based on the electronic medical records of Xi'an Encephalopathy Hospital of TCM, through structuring medical record text, acupuncture prescriptions were extracted. Using the data mining tools of the ancient and modern medical record cloud platform V2.2.3 and the clinical effective prescription and molecular mechanism analysis system of traditional Chinese medicine V2.0, the cluster analysis and complex network analysis were conducted on acupuncture prescriptions. RESULTS: Of 1584 acupuncture prescriptions for cerebral palsy in children, there were 84 acupoints and stimulating areas of scalp acupuncture, of which, foot-motor-sensory area, balance area and Sanyinjiao (SP 6) were the top 3 acupoints with the highest use rate. With cluster analysis, 5 groups of common supplementary acupoints and stimulating areas were found, named, Weizhong (BL 40) and Waiguan (TE 5), Shousanli (LI 10), Xingjian (LR 2), Xuanzhong (GB 39) and Chengfu (BL 36), foot-motor-sensory area, balance area and Sanyinjiao (SP 6), Xuehai (SP 10) and Fenglong (ST 40), Pishu (BL 20), motor area and Yanglingquan (GB 34). With complex network analysis on core prescriptions, 13 core acupoints and stimulating areas of scalp acupuncture were obtained, including 3 core main points, i.e. Sanyinjiao (SP 6), balance area and foot-motor-sensory area and 10 sub-core points, i.e. Taichong (LR 3), motor area, Xuehai (SP 10), Ganshu (BL 18), Pishu (BL 20), Yanglingquan (GB 34), Sishencong (EX-HN 1), Baihui (GV 20), Fengchi (GB 20) and Shenshu (BL 23). CONCLUSION: In treatment of acupuncture for cerebral palsy in preschool children, the core prescriptions reveal the simultaneous treatment of exterior and interior, the mutual regulation of yin and yang and the combination of acupoints with stimulating ares of scalp acupuncture for both encephalopathy and paralysis.


Asunto(s)
Terapia por Acupuntura , Parálisis Cerebral , Puntos de Acupuntura , Parálisis Cerebral/terapia , Preescolar , Minería de Datos , Registros Electrónicos de Salud , Humanos
3.
BMC Bioinformatics ; 22(1): 176, 2021 Apr 03.
Artículo en Inglés | MEDLINE | ID: mdl-33812384

RESUMEN

BACKGROUND: For multivariate data analysis involving only two input matrices (e.g., X and Y), the previously published methods for variable influence on projection (e.g., VIPOPLS or VIPO2PLS) are widely used for variable selection purposes, including (i) variable importance assessment, (ii) dimensionality reduction of big data and (iii) interpretation enhancement of PLS, OPLS and O2PLS models. For multiblock analysis, the OnPLS models find relationships among multiple data matrices (more than two blocks) by calculating latent variables; however, a method for improving the interpretation of these latent variables (model components) by assessing the importance of the input variables was not available up to now. RESULTS: A method for variable selection in multiblock analysis, called multiblock variable influence on orthogonal projections (MB-VIOP) is explained in this paper. MB-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of more than two data matrices according to their importance for both simplification and interpretation of the total multiblock model, and also of the unique, local and global model components separately. MB-VIOP has been tested using three datasets: a synthetic four-block dataset, a real three-block omics dataset related to plant sciences, and a real six-block dataset related to the food industry. CONCLUSIONS: We provide evidence for the usefulness and reliability of MB-VIOP by means of three examples (one synthetic and two real-world cases). MB-VIOP assesses in a trustable and efficient way the importance of both isolated and ranges of variables in any type of data. MB-VIOP connects the input variables of different data matrices according to their relevance for the interpretation of each latent variable, yielding enhanced interpretability for each OnPLS model component. Besides, MB-VIOP can deal with strong overlapping of types of variation, as well as with many data blocks with very different dimensionality. The ability of MB-VIOP for generating dimensionality reduced models with high interpretability makes this method ideal for big data mining, multi-omics data integration and any study that requires exploration and interpretation of large streams of data.


Asunto(s)
Análisis de Datos , Minería de Datos , Análisis Multivariante , Reproducibilidad de los Resultados
4.
BMC Res Notes ; 14(1): 150, 2021 Apr 20.
Artículo en Inglés | MEDLINE | ID: mdl-33879227

RESUMEN

OBJECTIVE: In this study we compare the amino acid and codon sequence of SARS-CoV-2, SARS-CoV and MERS-CoV using different statistics programs to understand their characteristics. Specifically, we are interested in how differences in the amino acid and codon sequence can lead to different incubation periods and outbreak periods. Our initial question was to compare SARS-CoV-2 to different viruses in the coronavirus family using BLAST program of NCBI and machine learning algorithms. RESULTS: The result of experiments using BLAST, Apriori and Decision Tree has shown that SARS-CoV-2 had high similarity with SARS-CoV while having comparably low similarity with MERS-CoV. We decided to compare the codons of SARS-CoV-2 and MERS-CoV to see the difference. Though the viruses are very alike according to BLAST and Apriori experiments, SVM proved that they can be effectively classified using non-linear kernels. Decision Tree experiment proved several remarkable properties of SARS-CoV-2 amino acid sequence that cannot be found in MERS-CoV amino acid sequence. The consequential purpose of this paper is to minimize the damage on humanity from SARS-CoV-2. Hence, further studies can be focused on the comparison of SARS-CoV-2 virus with other viruses that also can be transmitted during latent periods.


Asunto(s)
Minería de Datos , Coronavirus del Síndrome Respiratorio de Oriente Medio , Virus del SRAS , Algoritmos , Secuencia de Aminoácidos , Secuencia de Bases , Humanos , Aprendizaje Automático , Coronavirus del Síndrome Respiratorio de Oriente Medio/genética , Virus del SRAS/genética , /genética
5.
Sensors (Basel) ; 21(6)2021 Mar 18.
Artículo en Inglés | MEDLINE | ID: mdl-33803891

RESUMEN

Human activity recognition (HAR) remains a challenging yet crucial problem to address in computer vision. HAR is primarily intended to be used with other technologies, such as the Internet of Things, to assist in healthcare and eldercare. With the development of deep learning, automatic high-level feature extraction has become a possibility and has been used to optimize HAR performance. Furthermore, deep-learning techniques have been applied in various fields for sensor-based HAR. This study introduces a new methodology using convolution neural networks (CNN) with varying kernel dimensions along with bi-directional long short-term memory (BiLSTM) to capture features at various resolutions. The novelty of this research lies in the effective selection of the optimal video representation and in the effective extraction of spatial and temporal features from sensor data using traditional CNN and BiLSTM. Wireless sensor data mining (WISDM) and UCI datasets are used for this proposed methodology in which data are collected through diverse methods, including accelerometers, sensors, and gyroscopes. The results indicate that the proposed scheme is efficient in improving HAR. It was thus found that unlike other available methods, the proposed method improved accuracy, attaining a higher score in the WISDM dataset compared to the UCI dataset (98.53% vs. 97.05%).


Asunto(s)
Aprendizaje Profundo , Minería de Datos , Actividades Humanas , Humanos , Memoria a Largo Plazo , Redes Neurales de la Computación
6.
Artículo en Inglés | MEDLINE | ID: mdl-33802880

RESUMEN

In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes ('NEURAL-NETWORKS', 'CANCER', 'ELETRONIC-HEALTH-RECORDS', 'DIABETES-MELLITUS', 'ALZHEIMER'S-DISEASE', 'BREAST-CANCER', 'DEPRESSION', and 'RANDOM-FOREST') are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, (i) practices and techniques related to data mining in healthcare, and (ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field's evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.


Asunto(s)
Investigación Biomédica , Minería de Datos , Bibliometría , Prestación de Atención de Salud , Humanos , Inteligencia
7.
Artículo en Inglés | MEDLINE | ID: mdl-33803780

RESUMEN

(1) Background: Data mining has turned essential when exploring a large amount of information in performance analysis in sports. This study aimed to select the most relevant variables influencing the external and internal load in top-elite 5-a-side soccer (Sa5) using a data mining model considering some contextual indicators as match result, body mass index (BMI), scoring rate and age. (2) Methods: A total of 50 top-elite visually impaired soccer players (age 30.86 ± 11.2 years, weight 77.64 ± 9.78 kg, height 178.48 ± 7.9 cm) were monitored using magnetic, angular and rate gyroscope (MARG) sensors during an international Sa5 congested fixture tournament.; (3) Results: Fifteen external and internal load variables were extracted from a total of 49 time-related and peak variables derived from the MARG sensors using a principal component analysis as the most used data mining technique. The principal component analysis (PCA) model explained 80% of total variance using seven principal components. In contrast, the first principal component of the match was defined by jumps, take off by 24.8% of the total variance. Blind players usually performed a higher number of accelerations per min when losing a match. Scoring players execute higher DistanceExplosive and Distance21-24 km/h. And the younger players presented higher HRAVG and AccMax. (4) Conclusions: The influence of some contextual variables on external and internal load during top elite Sa5 official matches should be addressed by coaches, athletes, and medical staff. The PCA seems to be a useful statistical technique to select those relevant variables representing the team's external and internal load. Besides, as a data reduction method, PCA allows administrating individualized training loads considering those relevant variables defining team load behavior.


Asunto(s)
Rendimiento Atlético , Fútbol , Aceleración , Adulto , Minería de Datos , Humanos , Carga de Trabajo , Adulto Joven
8.
Medicina (B Aires) ; 81(2): 214-223, 2021.
Artículo en Español | MEDLINE | ID: mdl-33906140

RESUMEN

In the present work we use text mining as a treatment tool for a large scientific database, with the aim of obtaining new information about all the publications signed by Argentine authors and indexed until 2019, in the area of life sciences. More than 75 000 articles were analysed, published in around 5000 media, signed by about 186 000 authors with a workplace in Argentina or in collaborations with Argentine laboratories. Using automated tools that were developed ad hoc, the text of around 70 800 abstracts was analysed, seeking, through non-supervised digital detection, the main topics addressed by the authors, and the relationship with health problems in Argentina and their treatment. Results are also presented regarding the number of publications per year, the journals that have published them, and their authors and collaborations. These results, together with the predictions that were obtained, could become a useful tool to optimize the management of resources dedicated to basic and clinical research.


Asunto(s)
Minería de Datos , Argentina , Humanos
9.
J Appl Oral Sci ; 29: e20200799, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33886941

RESUMEN

OBJECTIVES: This study aimed to investigate patterns and risk factors related to the feasibility of achieving technical quality and periapical healing in root canal non-surgical retreatment, using regression and data mining methods. METHODOLOGY: This retrospective observational study included 321 consecutive patients presenting for root canal retreatment. Patients were treated by graduate students, following standard protocols. Data on medical history, diagnosis, treatment, and follow-up visits variables were collected from physical records and periapical radiographs and transferred to an electronic chart database. Basic statistics were tabulated, and univariate and multivariate analytical methods were used to identify risk factors for technical quality and periapical healing. Decision trees were generated to predict technical quality and periapical healing patterns using the J48 algorithm in the Weka software. RESULTS: Technical outcome was satisfactory in 65.20%, and we observed periapical healing in 80.50% of the cases. Several factors were related to technical quality, including severity of root curvature and altered root canal morphology (p<0.05). Follow-up periods had a mean of 4.05 years. Periapical lesion area, tooth type, and apical resorption proved to be significantly associated with retreatment failure (p<0.05). Data mining analysis suggested that apical root resorption might prevent satisfactory technical outcomes even in teeth with straight root canals. Also, large periapical lesions and poor root filling quality in primary endodontic treatment might be related to healing failure. CONCLUSION: Frequent patterns and factors affecting technical outcomes of endodontic retreatment included root canal morphological features and its alterations resulting from primary endodontic treatment. Healing outcomes were mainly associated with the extent of apical periodontitis pathological damages in dental and periapical tissues. To determine treatment predictability, we suggest patterns including clinical and radiographic features of apical periodontitis and technical quality of primary endodontic treatment.


Asunto(s)
Cavidad Pulpar , Periodontitis Periapical , Minería de Datos , Cavidad Pulpar/diagnóstico por imagen , Humanos , Retratamiento , Estudios Retrospectivos , Tratamiento del Conducto Radicular
10.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 38(2): 197-209, 2021 Apr 25.
Artículo en Chino | MEDLINE | ID: mdl-33913279

RESUMEN

In order to understand the evolution of the diagnosis and treatment plans of corona virus disease 2019 (COVID-19), and provide convenience for medical staff in actual diagnosis and treatment, this paper uses the 9 diagnosis and treatment plans of COVID-19 issued by the National Health Commission during the period from January 26, 2020 to August 19, 2020 as research data to perform comparative analysis and visual analysis. Based on text mining, this paper obtained the text similarity and summarized its evolution law by expressing and measuring the similarity of the overall diagnosis and treatment plans of COVID-19 and the same modules, which provides reference for clinical diagnosis and treatment practice and other diagnosis and treatment plan formulation.


Asunto(s)
Minería de Datos , Humanos
11.
Zhongguo Zhong Yao Za Zhi ; 46(6): 1558-1563, 2021 Mar.
Artículo en Chino | MEDLINE | ID: mdl-33787154

RESUMEN

To explore prescription medication regularity in the treatment of Alzheimer's disease with traditional Chinese medicine(TCM). With Alzheimer's disease or senile dementia as the subject, collecting and sorting out the journal papers in CNKI were collected as the data source to establish the literature research database of Alzheimer's disease prescriptions, and then the association rule analysis, factor analysis and systematic cluster analysis on the included TCM were conducted. Among the 113 prescriptions included in the standard, the single herb Acori Tatarinowii Rhizoma was the most common. The herbs were mainly warm and flat among four pro-perties, mainly sweet, bitter and spicy among five flavors. The drugs were mainly distributed in five internal organs, and the most commonly used drugs were deficiency tonifying drugs as well as blood activating and stasis removing drugs. In the association rule analysis, it was found that there were 6 drug pairs with the highest association strength. Eight common factors were extracted from the factor analysis, and they were classified into 6 categories in the systematic cluster analysis. The results have shown that the overall principles in treating Alzheimer's disease with modern Chinese medicine are tonifying deficiency, invigorating circulation, activating blood and dispelling phlegm.


Asunto(s)
Enfermedad de Alzheimer , Medicamentos Herbarios Chinos , Enfermedad de Alzheimer/tratamiento farmacológico , Minería de Datos , Medicamentos Herbarios Chinos/uso terapéutico , Humanos , Medicina China Tradicional , Prescripciones
12.
Sci Rep ; 11(1): 5322, 2021 03 05.
Artículo en Inglés | MEDLINE | ID: mdl-33674708

RESUMEN

The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients' past medical records (before their COVID-19 infection). MLHO's architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge.


Asunto(s)
/mortalidad , Minería de Datos/métodos , Aprendizaje Automático , Modelos Estadísticos , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , /terapia , Registros Electrónicos de Salud/estadística & datos numéricos , Femenino , Hospitalización/estadística & datos numéricos , Humanos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Masculino , Persona de Mediana Edad , Pandemias/estadística & datos numéricos , Pronóstico , Curva ROC , Reproducibilidad de los Resultados , Respiración Artificial/estadística & datos numéricos , Estudios Retrospectivos , Medición de Riesgo/métodos , Factores de Riesgo , /patogenicidad
13.
PLoS One ; 16(3): e0247995, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33657164

RESUMEN

BACKGROUND: Primary care is the major point of access in most health systems in developed countries and therefore for the detection of coronavirus disease 2019 (COVID-19) cases. The quality of its IT systems, together with access to the results of mass screening with Polymerase chain reaction (PCR) tests, makes it possible to analyse the impact of various concurrent factors on the likelihood of contracting the disease. METHODS AND FINDINGS: Through data mining techniques with the sociodemographic and clinical variables recorded in patient's medical histories, a decision tree-based logistic regression model has been proposed which analyses the significance of demographic and clinical variables in the probability of having a positive PCR in a sample of 7,314 individuals treated in the Primary Care service of the public health system of Catalonia. The statistical approach to decision tree modelling allows 66.2% of diagnoses of infection by COVID-19 to be classified with a sensitivity of 64.3% and a specificity of 62.5%, with prior contact with a positive case being the primary predictor variable. CONCLUSIONS: The use of a classification tree model may be useful in screening for COVID-19 infection. Contact detection is the most reliable variable for detecting Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases. The model would support that, beyond a symptomatic diagnosis, the best way to detect cases would be to engage in contact tracing.


Asunto(s)
/diagnóstico , Transmisión de Enfermedad Infecciosa/estadística & datos numéricos , Adulto , Anciano , Estudios de Cohortes , Trazado de Contacto , Minería de Datos/métodos , Árboles de Decisión , Femenino , Humanos , Masculino , Tamizaje Masivo/métodos , Persona de Mediana Edad , Probabilidad , Estudios Retrospectivos , Sensibilidad y Especificidad
14.
Fa Yi Xue Za Zhi ; 37(1): 15-20, 2021 Feb.
Artículo en Inglés, Chino | MEDLINE | ID: mdl-33780179

RESUMEN

Abstract: Objective To explore the feasibility of the CT image reconstruction of laryngeal cartilage and hyoid bone in adult age estimation using data mining methods. Methods The neck thin slice CT scans of 413 individuals aged 18 to <80 years were collected and divided into test set and train set, randomly. According to grading methods such as TURK et al., all samples were graded comprehensively. The process of thyroid cartilage ossification was divided into 6 stages, the process of cricoid cartilage ossification was divided into 5 stages, and the synosteosis between the greater horn of hyoid and hyoid body was divided into 3 stages. Multiple linear regression model, support vector regression model, and Bayesian ridge regression model were developed for adult age estimation by scikit-learn 0.17 machine learning kit (Python language). Leave-one-out cross-validation and the test set were used to further evaluate performance of the models. Results All indicators were moderately or poorly associated with age. The model with the highest accuracy in male age estimation was the support vector regression model, with a mean absolute error of 8.67 years, much higher than the other two models. The model with the highest accuracy in female adult age estimation was the support vector regression model, with a mean absolute error of 12.69 years, but its accuracy differences with the other two models had no statistical significance. Conclusion Data mining technology can improve the accuracy of adult age estimation, but the accuracy of adult age estimation based on laryngeal cartilage and hyoid bone is still not satisfactory, so it should be combined with other indicators in practice.


Asunto(s)
Hueso Hioides , Cartílagos Laríngeos , Adolescente , Adulto , Teorema de Bayes , Niño , Minería de Datos , Femenino , Humanos , Hueso Hioides/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador , Cartílagos Laríngeos/diagnóstico por imagen , Masculino , Tomografía Computarizada por Rayos X
15.
Methods Mol Biol ; 2212: 169-179, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33733356

RESUMEN

In biology, the term "epistasis" indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted gene, located far away on the chromosome or on an entirely different chromosome, and this interaction can have a strong effect on the function of the two genes. These changes then can alter the consequences of the biological processes, influencing the organism's phenotype. Machine learning is an area of computer science that develops statistical methods able to recognize patterns from data. A typical machine learning algorithm consists of a training phase, where the model learns to recognize specific trends in the data, and a test phase, where the trained model applies its learned intelligence to recognize trends in external data. Scientists have applied machine learning to epistasis problems multiple times, especially to identify gene-gene interactions from genome-wide association study (GWAS) data. In this brief survey, we report and describe the main scientific articles published in data mining and epistasis. Our article confirms the effectiveness of machine learning in this genetics subfield.


Asunto(s)
Biología Computacional/métodos , Minería de Datos/métodos , Epistasis Genética , Aprendizaje Automático , Carácter Cuantitativo Heredable , Enfermedad de Alzheimer/genética , Enfermedad de Alzheimer/metabolismo , Enfermedad de Alzheimer/patología , Enfermedad de Crohn/genética , Enfermedad de Crohn/metabolismo , Enfermedad de Crohn/patología , Genoma Humano , Estudio de Asociación del Genoma Completo , Humanos , Patrón de Herencia , Degeneración Macular/genética , Degeneración Macular/metabolismo , Degeneración Macular/patología , Neoplasias/genética , Neoplasias/metabolismo , Neoplasias/patología , Fenotipo , Plantas/genética , Polimorfismo de Nucleótido Simple
16.
Methods Mol Biol ; 2212: 325-335, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33733365

RESUMEN

Epistasis detection is a hot topic in bioinformatics due to its relevance to the detection of specific phenotypic traits and gene-gene interactions. Here, we present a step-by-step protocol to apply Epi-GTBN, a machine learning-based method based on genetic algorithm and Bayesian network to effectively mine the epistasis loci. Epi-GTBN utilizes the advantages of genetic algorithm that can achieve a global search and avoid falling into local optima incorporating it into the Bayesian network to obtain the best structure of the model. In this chapter, we describe an example of Epi-GTBN to help researchers to analyze the epistasis and gene-gene interactions of their own datasets and build the corresponding SNP-SNP network.


Asunto(s)
Minería de Datos/estadística & datos numéricos , Epistasis Genética , Aprendizaje Automático , Modelos Genéticos , Polimorfismo de Nucleótido Simple , Programas Informáticos , Teorema de Bayes , Conjuntos de Datos como Asunto , Redes Reguladoras de Genes , Sitios Genéticos , Genotipo , Humanos , Fenotipo
17.
Sci Rep ; 11(1): 6725, 2021 03 24.
Artículo en Inglés | MEDLINE | ID: mdl-33762619

RESUMEN

The recent global pandemic of the Coronavirus disease 2019 (COVID-19) caused by the new coronavirus SARS-CoV-2 presents an urgent need for the development of new therapeutic candidates. Many efforts have been devoted to screening existing drug libraries with the hope to repurpose approved drugs as potential treatments for COVID-19. However, the antiviral mechanisms of action of the drugs found active in these phenotypic screens remain largely unknown. In an effort to deconvolute the viral targets in pursuit of more effective anti-COVID-19 drug development, we mined our in-house database of approved drug screens against 994 assays and compared their activity profiles with the drug activity profile in a cytopathic effect (CPE) assay of SARS-CoV-2. We found that the autophagy and AP-1 signaling pathway activity profiles are significantly correlated with the anti-SARS-CoV-2 activity profile. In addition, a class of neurology/psychiatry drugs was found to be significantly enriched with anti-SARS-CoV-2 activity. Taken together, these results provide new insights into SARS-CoV-2 infection and potential targets for COVID-19 therapeutics, which can be further validated by in vivo animal studies and human clinical trials.


Asunto(s)
/tratamiento farmacológico , Minería de Datos/métodos , Factor de Transcripción AP-1/metabolismo , Animales , Antivirales/farmacología , Autofagia/efectos de los fármacos , Autofagia/fisiología , /genética , Chlorocebus aethiops , Bases de Datos Genéticas , Aprobación de Drogas , Evaluación Preclínica de Medicamentos/métodos , Reposicionamiento de Medicamentos/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Humanos , Terapia Molecular Dirigida , Pandemias , Células Vero
18.
Sci Rep ; 11(1): 6811, 2021 03 24.
Artículo en Inglés | MEDLINE | ID: mdl-33762651

RESUMEN

High rate of cardiovascular disease (CVD) has been reported among patients with coronavirus disease 2019 (COVID-19). Importantly, CVD, as one of the comorbidities, could also increase the risks of the severity of COVID-19. Here we identified phospholipase A2 group VII (PLA2G7), a well-studied CVD biomarker, as a hub gene in COVID-19 though an integrated hypothesis-free genomic analysis on nasal swabs (n = 486) from patients with COVID-19. PLA2G7 was further found to be predominantly expressed by proinflammatory macrophages in lungs emerging with progression of COVID-19. In the validation stage, RNA level of PLA2G7 was identified in nasal swabs from both COVID-19 and pneumonia patients, other than health individuals. The positive rate of PLA2G7 were correlated with not only viral loads but also severity of pneumonia in non-COVID-19 patients. Serum protein levels of PLA2G7 were found to be elevated and beyond the normal limit in COVID-19 patients, especially among those re-positive patients. We identified and validated PLA2G7, a biomarker for CVD, was abnormally enhanced in COVID-19 at both nucleotide and protein aspects. These findings provided indications into the prevalence of cardiovascular involvements seen in patients with COVID-19. PLA2G7 could be a potential prognostic and therapeutic target in COVID-19.


Asunto(s)
1-Alquil-2-acetilglicerofosfocolina Esterasa/metabolismo , Enfermedades Cardiovasculares/metabolismo , Macrófagos/metabolismo , 1-Alquil-2-acetilglicerofosfocolina Esterasa/sangre , 1-Alquil-2-acetilglicerofosfocolina Esterasa/genética , Biomarcadores/metabolismo , /inmunología , Enfermedades Cardiovasculares/epidemiología , Enfermedades Cardiovasculares/virología , China/epidemiología , Minería de Datos/métodos , Humanos , Macrófagos/inmunología , Macrófagos/patología , Polimorfismo de Nucleótido Simple , Activación Transcripcional , Regulación hacia Arriba
19.
BMC Bioinformatics ; 22(1): 110, 2021 Mar 06.
Artículo en Inglés | MEDLINE | ID: mdl-33676405

RESUMEN

BACKGROUND: Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components. RESULTS: We present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environment. The package allows for building and analyzing non-linear interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA . To illustrate the usage of the package, we applied it to a transcriptome dataset from an autism case-control study. Our tool provided hypotheses for potential co-predictive mechanisms among features that discerned phenotype classes. These co-predictors represented neurodevelopmental and autism-related genes. CONCLUSIONS: R.ROSETTA provides new insights for interpretable machine learning analyses and knowledge-based systems. We demonstrated that our package facilitated detection of dependencies for autism-related genes. Although the sample application of R.ROSETTA illustrates transcriptome data analysis, the package can be used to analyze any data organized in decision tables.


Asunto(s)
Algoritmos , Aprendizaje Automático , Estudios de Casos y Controles , Biología Computacional , Minería de Datos
20.
JMIR Public Health Surveill ; 7(4): e22880, 2021 04 06.
Artículo en Inglés | MEDLINE | ID: mdl-33690143

RESUMEN

BACKGROUND: The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak. OBJECTIVE: The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project. METHODS: To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends. RESULTS: Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test. CONCLUSIONS: Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.


Asunto(s)
Causalidad , Infecciones por Coronavirus/epidemiología , Interpretación Estadística de Datos , Vigilancia en Salud Pública , Restaurantes/estadística & datos numéricos , Motor de Búsqueda/tendencias , Adulto , Minería de Datos , Humanos , Estados Unidos/epidemiología
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