Different DNA Sequencing Using DNA Graphs: A Study

Alanazi, Abdulaziz M.; Muhiuddin, G.; Albalawi, Doha A.; Samanta, Sovan

doi:10.3390/app12115414

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…Graph theory is an area of discrete mathematics whose popularity has increased in recent years, not only regarding theoretical developments, but also regarding its applications for numerous aspects [39][40][41][42][43][44][45]. Graph theory can be easily used in solving daily life problems, and it has been employed quite frequently in more theoretical studies, data analyses, and artificial intelligence applications.…”

Section: Directed Correlation Networkmentioning

confidence: 99%

Network-Induced Soft Sets and Stock Market Applications

2022

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The intricacy of the financial systems reflected in bilateral ties has piqued the interest of many specialists. In this research, we introduce network-induced soft sets, a novel mathematical model for studying the dynamics of a financial stock market with several orders of interaction. To achieve its intelligent parameterization, this model relies on the bilateral connections between economic actors, who are agents in a financial network, rather than relying on any other single feature of the network itself. Our study also introduces recently developed statistical measures for network-induced soft sets and provides an analysis of their application to the study of financial markets. Findings validate the efficacy of this novel method in assessing the effects of various economic stress periods registered in Borsa Istanbul.

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Section: Directed Correlation Networkmentioning

confidence: 99%

Network-Induced Soft Sets and Stock Market Applications

2022

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“…There are different types of graph centrality measures [17,18], such as degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, etc. Each type of graph centrality measure captures a different aspect of the node's importance in the network.…”

Section: Introductionmentioning

confidence: 99%

A Study on Graph Centrality Measures of Different Diseases Due to DNA Sequencing

Muhiuddin

Samanta²,

Aljohani

et al. 2023

Mathematics

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Rare genetic diseases are often caused by single-gene defects that affect various biological processes across different scales. However, it is challenging to identify the causal genes and understand the molecular mechanisms of these diseases. In this paper, we present a multiplex network approach to study the relationship between human diseases and genes. We construct a human disease network (HDN) and a human genome network (HGN) based on genotype–phenotype associations and gene interactions, respectively. We analyze 3771 rare diseases and find distinct phenotypic modules within each dimension that reflect the functional effects of gene mutations. These modules can also be used to predict novel gene candidates for unsolved rare diseases and to explore the cross-scale impact of gene perturbations. We compute various centrality measures for both networks and compare them. Our main finding is that diseases are weakly connected in the HDN, while genes are strongly connected in the HGN. This implies that diseases are relatively isolated from each other, while genes are involved in multiple biological processes. This result has implications for understanding the transmission of infectious diseases and the development of therapeutic interventions. We also show that not all diseases have the same potential to spread infections to other parts of the body, depending on their centrality in the HDN. Our results show that the phenotypic module formalism can capture the complexity of rare diseases beyond simple physical interaction networks and can be applied to study diseases arising from DNA (Deoxyribonucleic Acid) sequencing errors. This study provides a novel network-based framework for integrating multi-scale data and advancing the understanding and diagnosis of rare genetic diseases.

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The importance of precise and suitable descriptors in data‐driven approach to boost development of lithium batteries: A perspective

Wang,

Zhang

et al. 2024

Electron

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Conventional approaches for developing new materials may no longer be adequate to meet the urgent needs of humanity's energy transition. The emergence of machine learning (ML) and artificial intelligence (AI) has led materials scientists to recognize the potential of using AI/ML to accelerate the creation of new battery materials. Although fixed material properties have been extensively studied as descriptors to establish the link between AI and materials chemistry, they often lack versatility and accuracy due to a lack of understanding of the underlying mechanisms of AI/ML. Therefore, materials scientists need to have a comprehensive understanding of the operational mechanisms and learning logic of AI/ML to design more accurate descriptors. This paper provides a review of previous research studies conducted on AI, ML, and descriptors, which have been used to address challenges at various levels, ranging from materials development to battery performance prediction. Additionally, it introduces the basics of AI and ML to assist materials and battery developers in comprehending their operational mechanisms. The paper demonstrates the significance of precise and suitable ML descriptors in the creation of new battery materials. It does so by providing examples, summarizing current descriptors and ML algorithms, and examining the potential implications of future AI advancements for the sustainable energy industry.

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Different DNA Sequencing Using DNA Graphs: A Study

Cited by 4 publications

References 35 publications

Network-Induced Soft Sets and Stock Market Applications

Network-Induced Soft Sets and Stock Market Applications

A Study on Graph Centrality Measures of Different Diseases Due to DNA Sequencing

The importance of precise and suitable descriptors in data‐driven approach to boost development of lithium batteries: A perspective

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