A Hybrid Cloud Resource Clustering Method Using Analysis of Application Characteristics

Oh, Yoori; Kim, Yoonhee

doi:10.1109/fas-w.2017.162

Cited by 2 publications

(4 citation statements)

References 5 publications

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“…We first determined the optimum number of clusters by the elbow method, which consists of plotting the within-cluster sums of squares (WCSS( x )) value on the y -axis according to the number of clusters ( x ) considered on the x -axis . Chemicals were clustered using k -means and hierarchical clustering to identify chemicals with similar toxicity characteristics based on the CV-relevant in vitro biological space.…”

Section: Materials and Methodsmentioning

confidence: 99%

High-Throughput Screening to Identify Chemical Cardiotoxic Potential

Krishna

Berridge

Kleinstreuer

2020

Chem. Res. Toxicol.

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Cardiovascular (CV) disease is one of the most prevalent public health concerns, and mounting evidence supports the contribution of environmental chemicals to CV disease burden. In this study, we performed cardiotoxicity profiling for the Tox21 chemical library by focusing on high-throughput screening (HTS) assays whose targets are associated with adverse events related to CV failure modes. Our objective was to develop new hypotheses around environmental chemicals of potential interest for adverse CV outcomes using Tox21/ToxCast HTS data. Molecular and cellular events linked to six failure modes of CV toxicity were cross-referenced with 1399 Tox21/ToxCast assays to identify cardio-relevant bioactivity signatures. The resulting 40 targets, measured in 314 assays, were integrated via a ToxPi visualization tool and ranking system to prioritize 1138 chemicals based upon formal integration across multiple domains of information. Filtering was performed based on cytotoxicity and generalized cell stress endpoints to try and isolate chemicals with effects specific to CV biology, and bioactivity- and structure-based clustering identified subgroups of chemicals preferentially affecting targets such as ion channels and vascular tissue biology. Our approach identified drugs with known cardiotoxic effects, such as estrogenic modulators like clomiphene and raloxifene, anti-arrhythmic drugs like amiodarone and haloperidol, and antipsychotic drugs like chlorpromazine. Several classes of environmental chemicals such as organotins, bisphenol-like chemicals, pesticides, and quaternary ammonium compounds demonstrated strong bioactivity against CV targets; these were compared to existing data in the literature (e.g., from cardiomyocytes, animal data, or human epidemiological studies) and prioritized for further testing.

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Section: Materials and Methodsmentioning

confidence: 99%

High-Throughput Screening to Identify Chemical Cardiotoxic Potential

Krishna

Berridge

Kleinstreuer

2020

Chem. Res. Toxicol.

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“…• Start with k = 2 and continue increasing it in every iteration by 1 • Calculate the cost that results with the clustering • Monitor the evolution of the within-cluster sum of squares • Check the elbow point (Oh and Kim, 2017)…”

Section: ) Elbow Methodsmentioning

confidence: 99%

“…If the number of clusters increases, the average distortion will diminish, each cluster will have fewer points and the points will be closer to their corresponding centroids. On the other hand, the enhancement in average distortion will decrease as the value of k increases (Oh and Kim, 2017).…”

Section: Fig 2: Intersection Of Contextual and Behavioral Clustersmentioning

confidence: 98%

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A Clustering based Approach for Contextual Anomaly Detection in Internet of Things

ElMenshawy¹,

Helmy²,

El-Tazi³

2019

Journal of Computer Science

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Internet of Things (IoT) is a network which connects different communication devices with the internet to attain quick, robust and realtime information transfer and communication, achieving intelligent management. IoT is still in its infancy so it faces numerous challenges varying from data management to security concerns. Sensors generate enormous quantities of data that need to be handled efficiently to have successful deployment of IoT applications. Concerning data management, a great challenge that faces the IoT environment is the detection of contextual anomalies. Contextual anomaly detection is a sophisticated task because the context has to be taken into consideration in the anomaly detection process rather than checking only the deviation of the data value as in point anomaly detection. As a result, in this paper, a novel clustering based algorithm is proposed to detect contextual anomalies in Internet of Things. Attributes were separated into two different categories, namely contextual attributes and behavioral attributes. K-Means clustering technique was applied on the contextual and behavioral attributes separately, then the intersection between the contextual and behavioral clusters was used to detect the contextual anomalies. Moreover, the algorithm was applied on a real room occupation dataset of size around 20,000 records and the experiments showed promising results.

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A Hybrid Cloud Resource Clustering Method Using Analysis of Application Characteristics

Cited by 2 publications

References 5 publications

High-Throughput Screening to Identify Chemical Cardiotoxic Potential

High-Throughput Screening to Identify Chemical Cardiotoxic Potential

A Clustering based Approach for Contextual Anomaly Detection in Internet of Things

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