Coverage Optimization using Nature-Inspired Algorithm for Directional Sensor Networks

Ahmad, Hifzan; Shivalingagowda, Chaya; Kohli, Narendra; Kumar, Sunil

doi:10.1007/978-981-33-6195-9_9

Cited by 3 publications

(7 citation statements)

References 45 publications

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“…Many studies have tried to identify signaling pathways involved in breast cancer, and due to the disruption of these signaling pathways, cancer development is a complex process. To determine the significant modules related to breast cancer, we have collected a set of signaling pathways involved in breast cancer [50]. These signaling pathways affect cell responses such as survival, proliferation, migration, differentiation, and apoptosis and promote breast cancer [50].…”

Section: Resultsmentioning

confidence: 99%

“…To determine the significant modules related to breast cancer, we have collected a set of signaling pathways involved in breast cancer [50]. These signaling pathways affect cell responses such as survival, proliferation, migration, differentiation, and apoptosis and promote breast cancer [50]. In the following, we summarize these signaling pathways and their relationship with breast cancer.…”

Section: Resultsmentioning

confidence: 99%

“…Targeting the Ras/Raf signaling pathway and its upstream activators in breast cancer has received much interest. Recent studies indicate that this pathway is abnormally activated in breast cancer with high frequency, particularly by upstream activation of EGFR [50]. Abnormal activation of Ras proteins is the primary inspiration of oncogenes that have an essential role in the main signaling pathway in cancer, and mutation of Ras proteins causes cancer development [50].…”

Section: Resultsmentioning

confidence: 99%

“…The PI3K-AKT is crucial for the control of intracellular physiological processes. This signaling pathway is stimulated by several oncogenes and growth factor receptors, including MET, KIT, EGFR, and ERBB [50].…”

Section: Resultsmentioning

confidence: 99%

“…Activation of the ERBB family of receptors forms a significant group of related signaling pathways that control breast cancer’s proliferation, survival, angiogenesis, and metastasis. ERBB receptors modulate mammary gland growth and are often intensified, mutated, and overexpressed in breast cancer [50]. Since they have essential functions in regulating tumor proliferation, survival, and metastasis, the ERBB family could be a therapeutic target in breast cancer.…”

Section: Resultsmentioning

confidence: 99%

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A Novel Machine Learning Method for Mutational Analysis to Identifying Driver Genes in Breast Cancer

Taheri

Habibi

2022

Preprint

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Breast cancer has emerged as a severe public health issue and one of the main reasons for cancer-related mortality in women worldwide. Although the definitive reason for breast cancer is unknown, many genes and mutations in these genes associated with breast cancer have been identified using developed methods. The recurrence of a mutation in patients is a highly used feature for finding driver mutations. However, for various reasons, some mutations are more likely to arise than others. Sequencing analysis has demonstrated that cancer-driver genes perform across complicated pathways and networks, with mutations often arising in a modular pattern. In this work, we proposed a novel machine-learning method to study the functionality of genes in the networks derived from mutation associations, gene-gene interactions, and graph clustering for breast cancer analysis. These networks have revealed essential biological elements in the vital pathways, notably those that undergo low-frequency mutations. The statistical power of the clinical study is considerably increased when evaluating the network rather than just the effects of a single gene. The proposed method discovered key driver genes with various mutation frequencies. We investigated the function of the potential driver genes and related pathways. By presenting lower-frequency genes, we recognized breast cancer-related pathways that are less studied. In addition, we suggested a novel Monte Carlo-based algorithm to identify driver modules in breast cancer. We demonstrated our proposed modules' importance and role in critical signaling pathways in breast cancer, and this evaluation for breast cancer-related driver modules gave us an inclusive insight into breast cancer development.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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A Novel Machine Learning Method for Mutational Analysis to Identifying Driver Genes in Breast Cancer

Taheri

Habibi

2022

Preprint

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Collaborative Sensing in Internet of Things: A Comprehensive Survey

Shi

Liu

et al. 2022

IEEE Commun. Surv. Tutorials

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Energy Efficient Data Aggregation and Density-Based Spatial Clustering of Applications with Noise for Activity Monitoring in Wireless Sensor Networks

Singh¹,

din²

2022

Eng. Sci.

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Clustering saves energy and extends network longevity in applications such as activity monitoring in wireless sensor networks. Clustering separate tes nodes into clusters from which the cluster head can collect and send data to the base station. The current investigation also demonstrates data aggregation at the cluster head before sending it to the base station. The cluster head is chosen based on leftover energy and distance from the sink and other network nodes. The current study found that the existing clustering protocol decays its energy quickly, but our proposal can overcome these challenges and sensors can survive longer. This paper proposes Density-based Spatial Clustering of Applications with Noise (DB-SCAN) clustering algorithms to make data gathering and transmitting of data energy efficient. The data aggregation protocol will collect the packets and combine them. Further, it will be forwarded using the clustering approach. It is used to improve transmission efficiency by reducing the transmission of redundant data to the sink. This proposed protocol, Energy Efficient Cluster based Data Aggregation (EECDA), will help increase network lifetime by ensuring uniform distribution of energy amongst the nodes.

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Coverage Optimization using Nature-Inspired Algorithm for Directional Sensor Networks

Cited by 3 publications

References 45 publications

A Novel Machine Learning Method for Mutational Analysis to Identifying Driver Genes in Breast Cancer

A Novel Machine Learning Method for Mutational Analysis to Identifying Driver Genes in Breast Cancer

Collaborative Sensing in Internet of Things: A Comprehensive Survey

Energy Efficient Data Aggregation and Density-Based Spatial Clustering of Applications with Noise for Activity Monitoring in Wireless Sensor Networks

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