Benchmarking Gene Selection Techniques for Prediction of Distinct Carcinoma from Gene Expression Data: A Computational Study

Venkataramana, Lokeswari; Jacob, Shomona Gracia; Shanmuganathan, Saraswathi; Dattuluri, Venkata Vara Prasad

doi:10.1007/978-3-030-33820-6_10

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…The analysis and accurate interpretation of biological information is crucial because, in such data, the number of variables (genes and their coded products) is many times greater than the number of tested samples. The challenge in genomic data is in applying appropriate computational methods to the ever-increasing size of high-throughput multiomics data [ 10 ]. Processing and integration of multiomics data requires in-depth technical and biological knowledge of how these data are generated [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Sharma

Colonna

2021

Mol Diagn Ther

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Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein–protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous “updates” to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions. Supplementary Information The online version contains supplementary material available at 10.1007/s40291-020-00505-3.

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Section: Introductionmentioning

confidence: 99%

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Sharma

Colonna

2021

Mol Diagn Ther

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“…These experiments utilize the CityPulse-tra c dataset and the Citypulse pollution datasets. [53] One of the studies, which utilizes the CNN-convolutional-neural-networks been under optimization with the other approaches like ACO-ant-colony optimization. The PCO-particle-swarm optimization technique is implemented in the study.…”

Section: Sno Author Descriptionmentioning

confidence: 99%

Survey of Deep-learning Techniques in Big-Data analytics

Goswami¹,

Kumar²

2021

Preprint

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The present elaboration of Big-data research studies relying upon Deep-learning methods had revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms' operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. The present enhancements in the Deep-learning approaches in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition to having more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper elaborates the above statements stating the impact of the Deep-learning process utilizing the Big-data to operate in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing on having the impacts of operational parameters, concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors, the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

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Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease

Başarslan

Kayaalp

2020

Studies in Computational Intelligence

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Benchmarking Gene Selection Techniques for Prediction of Distinct Carcinoma from Gene Expression Data: A Computational Study

Cited by 4 publications

References 27 publications

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Survey of Deep-learning Techniques in Big-Data analytics

Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease

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