A data mining framework based on boundary-points for gene selection from DNA-microarrays: Pancreatic Ductal Adenocarcinoma as a case study

Ramos, Juan Pablo Hernández; Castellanos-Garzón, José A.; Paz, Juan F. De; Corchado, Juan M.

doi:10.1016/j.engappai.2018.01.007

Cited by 11 publications

(5 citation statements)

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“…Juan Ramos [5] discussed about the fact, The findings of this study are quite encouraging and have demonstrated their biological validity. So, our suggestion has produced a methodology that can be used in the gene selection process using DNA-microarray data, although this problem still has to be solved.…”

Section: Literature Surveysupporting

confidence: 53%

Survey on Accelerating Biomedical Research With Fast Computation Methods for Microarray Data Analysis

2023

IRJMETS

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A potent technique for examining the gene expression patterns of various organisms is microarray technology. Unfortunately, microarray data analysis is frequently time-consuming and computationally intensive, particularly when working with large datasets. Using parallel computing techniques, we suggest a quick computation method for microarray data analysis in this study. Using a combination of the Expectation-Maximization (EM) algorithm, 3D printing, the Generalized Hidden Markov Model (GHMM) framework, and Lab-on-a-Chip (LOC) devices, the study suggests a unique method for the quick calculation of microarray data. The suggested method attempts to speed up microarray analysis computations and improve its precision, which is important for discovering gene expression patterns and biomarkers linked to various disorders. The LOC devices for sample preparation and analysis are made using a 3D printer, the EM method is utilized to estimate the model parameters, and the GHMM framework is used to model the gene expression patterns. The proposed method has demonstrated promising accuracy and computational results, making it a suitable remedy for microarray data analysis in clinical research. The processing of microarray data is distributed using the MapReduce architecture in the method, which speeds up computing. We also employ a speedy technique for data standardization and filtering, which speeds up the processing even more. It shows how quickly our technology can handle big microarray datasets while still producing findings with a high degree of precision and accuracy.

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Section: Literature Surveysupporting

confidence: 53%

Survey on Accelerating Biomedical Research With Fast Computation Methods for Microarray Data Analysis

2023

IRJMETS

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“…56 undergraduates who specialize in volleyball specializing in sports training in a university were selected as the research objects. [19]. Tables 4 and 5 are shown below.…”

Section: Application Of Data Mining Methods In Volleyball Trainingmentioning

confidence: 99%

Data Mining Method Based on Mobile Network Communication in Volleyball Training

Han

2022

Mobile Information Systems

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In recent years, volleyball has developed rapidly, and the requirements of athletes’ physical fitness have been continuously improved. The way to improve physical fitness is physical training. Therefore, how to improve sports performance and physical fitness level has become a problem that needs to be solved in current volleyball training. This paper adopts the methods of literature materials, expert interviews, testing methods, experimental methods, mathematical statistics, and logical analysis methods and conducts experiments on the actual situation of college volleyball students by using mobile network communication data mining. Comparing the test and experimental data, it was found that the test groups had significant differences in the test scores of agility, jumping ability, lower arm explosive power, upper body power, abdominal strength, back strength, and coordination. The sample T test was performed on the improvement of the experimental group and the control group (boys) in the 30 m running and 9 m × 10 movement test scores, and P3 = 0.442 and P9 = 0.338 ( P > 0.05 ) were obtained, respectively. The posttest score was 0.064 higher than the pretest score.

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“…Classification actually extracts from the system information that will reduce the level of chaos in the system and move the system in a direction that is both more disciplined, more orderly, and less structured. 18,19…”

Section: Overviewmentioning

confidence: 99%

“…) (18) where e(t) denotes a quantity of misclassified instances of node t while n(t) stands for a quantity of training set of node t. Since each subtree is visited at most once during the pruning process of the PEP algorithm, the algorithm is faster and more effective than other algorithms. It is considered as one of the most accurate pruning algorithms.…”

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confidence: 99%

Design of Early Warning System for Mental Health Problems Based on Data Mining and Database

2023

Rev Bras Med Esporte

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Introduction: Data mining technology is mainly employed in the era of big data to evaluate the acquired information. Subsequently, reasoning about the data inductively is fully automated to discover possible patterns. Objective: Recently, data mining technology in the national mental health database has deepened and can be effectively used to solve various mental health early warning problems. Methods: For example, it can be applied to mine psychological data and extract the most important features and information. Results: This paper presents the design of an early warning system for mental health problems based on data mining techniques to offer some thoughts on early warning of mental health problems, including data preparation, data mining, results in analysis, and decision tree algorithm. Conclusion: The experimental results indicate that the results of the early warning system in this paper can achieve an accuracy rate of more than 96% with a high accuracy rate. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

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A data mining framework based on boundary-points for gene selection from DNA-microarrays: Pancreatic Ductal Adenocarcinoma as a case study

Cited by 11 publications

References 50 publications

Survey on Accelerating Biomedical Research With Fast Computation Methods for Microarray Data Analysis

Survey on Accelerating Biomedical Research With Fast Computation Methods for Microarray Data Analysis

Data Mining Method Based on Mobile Network Communication in Volleyball Training

Design of Early Warning System for Mental Health Problems Based on Data Mining and Database

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