Feasible analysis of gene expression –a computational based classification for breast cancer

Nandagopal, V.; Geeitha, S.; Kumar, K. Vinoth; Anbarasi, L. Jani

doi:10.1016/j.measurement.2019.03.015

Cited by 22 publications

(9 citation statements)

References 16 publications

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“…[17] This graph varies with conventional sharing in the cellular based activities where in the D2D communication shares without the base station (BS)[ Fig.8] where many UEs can be connected together in the virtual mode and sharing is similar to that of the online social communicational networks. [16] The huge user group can be split into multiple small groups where the UEs are connected strongly.…”

Section: Resultsmentioning

confidence: 99%

D2d Big Data Analytics for User Behavior over Cellular Networks for Improving Content Desliveries

Baalaji.¹,

Khanna²

2019

IJRTE

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 Abstract: (Nowadays cellular phones are profoundly increasing and present network capacity need to be increased to meet the growing demands of user equipment (UE) that has led to evolution of cellular and communication networks. Device-to-Device (D2D) communication is a usage technology that extends enormous features that can be incorporated with LTE and conssidesred as a finest technological componentespecially for the 5G network. Generally the 5G wireless networks are being introsduced to improve the present technology that meets the future demands extending efficient and reliable solutions. This Device-to-Device (D2D) communication can be established within LTE that limits to its proximity and comes with various advantages such as increase of spectral efficiency, energy efficiency, reduction of transmission delay, efficient offloaded traffic, avoiding congestion in cellular network. This paper deals D2D entities that include user behaviors, content deliveries and characteristics in big data platform that utilizes sharing large scale data accurately and effectively. Besides D2D, the proposed work builds concept of big data analytics integrated with D2D for effectively improving the content deliveries while offloading large data set.The presentwork also discussesbig data predictive analysis for the users based on D2D network services that help for further work.

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Section: Resultsmentioning

confidence: 99%

D2d Big Data Analytics for User Behavior over Cellular Networks for Improving Content Desliveries

Baalaji.¹,

Khanna²

2019

IJRTE

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“…The paper discusses about the steganography layout, its types, methods and medium such as text, audio and image shown in Fig 6a and 6b.Steganography holds advantages that overcomes cryptography models. The techniques used in the steganography models are used for the determining the stego images by providing security for images and also for the data enclosed within the image, audio , video or image [14] as well. Some steganalysis tools are also implemented to detect the files which is hidden in any type of the image.…”

Section: Discussionmentioning

confidence: 99%

Steganographical Techniques in Hiding Text Images – System

Vinoth¹,

Kanniga²

2019

IJRTE

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In this era, internet has become a widespread entity all over the world. Sharing and communicating images, video contributes an essential role in this scientific era. In the meanwhile, secure transition of data has indeed become a major issue. Steganography is one of technology that contributes a vital role in protecting the secret data against an unauthorized access. In this type, secret data can be concealed into a cover file that includes audio, video, text and also image. In this paper, we propose different types of steganography methods for secure transfer of data in a confidential manner. The paper also explores stenographical techniques in terms of text, image and audio. The proposed work compares and discusses the stenographical methods in spatial domain, frequency domain derived under the image technique

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“…For example, RNA sequencing quantifies expression levels of thousands of genes. Such omics data is useful in predicting organismal traits, with empirical applications including diagnosis and classification of diseases and prediction of patient survival 5 – 8 and possible future applications in predicting crop yields 9 , insecticide resistance 10 , and environmental adaptation 11 .…”

Section: Introductionmentioning

confidence: 99%

Relationship between gene regulation network structure and prediction accuracy in high dimensional regression

Okinaga

Kyogoku

Kondo

et al. 2021

Sci Rep

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The least absolute shrinkage and selection operator (lasso) and principal component regression (PCR) are popular methods of estimating traits from high-dimensional omics data, such as transcriptomes. The prediction accuracy of these estimation methods is highly dependent on the covariance structure, which is characterized by gene regulation networks. However, the manner in which the structure of a gene regulation network together with the sample size affects prediction accuracy has not yet been sufficiently investigated. In this study, Monte Carlo simulations are conducted to investigate the prediction accuracy for several network structures under various sample sizes. When the gene regulation network is a random graph, a sufficiently large number of observations are required to ensure good prediction accuracy with the lasso. The PCR provided poor prediction accuracy regardless of the sample size. However, a real gene regulation network is likely to exhibit a scale-free structure. In such cases, the simulation indicates that a relatively small number of observations, such as $$N=300$$ N = 300 , is sufficient to allow the accurate prediction of traits from a transcriptome with the lasso.

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Feasible analysis of gene expression –a computational based classification for breast cancer

Cited by 22 publications

References 16 publications

D2d Big Data Analytics for User Behavior over Cellular Networks for Improving Content Desliveries

D2d Big Data Analytics for User Behavior over Cellular Networks for Improving Content Desliveries

Steganographical Techniques in Hiding Text Images – System

Relationship between gene regulation network structure and prediction accuracy in high dimensional regression

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