Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining

Vougas, Konstantinos; Sakellaropoulos, Theodore; Kotsinas, Athanassios; Foukas, George-Romanos P; Ntargaras, Andreas; Koinis, Filippos; Polyzos, Alexander; Myrianthopoulos, Vassilios; Zhou, Hua; Narang, Sonali; Georgoulias, Vassilis; Alexopoulos, Leonidas G.; Aifantis, Iannis; Townsend, Paul A.; Sfikakis, Petros P.; Fitzgerald, Rebecca C.; Thanos, Dimitris; Bártek, Jiří; Petty, Russell; Tsirigos, Aristotelis; Gorgoulis, Vassilis G.

doi:10.1016/j.pharmthera.2019.107395

Cited by 88 publications

(55 citation statements)

References 191 publications

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“…The third step consists of identification of factors that are anticipated to be altered in the specific context. Single-cell transcriptome and proteome profiling of tissues along with development of sophisticated high-throughput methods and machine learning tools will be key to understanding the nature of senescent cells and may aid in identifying potential therapeutic approaches (Vougas et al, 2019). To help with the identification of genes associated with senescence a novel database SeneQuest 1 has been established (Gorgoulis et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Mechanisms of Cellular Senescence: Cell Cycle Arrest and Senescence Associated Secretory Phenotype

Kumari

Jat

2021

Front. Cell Dev. Biol.

708

657

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Cellular senescence is a stable cell cycle arrest that can be triggered in normal cells in response to various intrinsic and extrinsic stimuli, as well as developmental signals. Senescence is considered to be a highly dynamic, multi-step process, during which the properties of senescent cells continuously evolve and diversify in a context dependent manner. It is associated with multiple cellular and molecular changes and distinct phenotypic alterations, including a stable proliferation arrest unresponsive to mitogenic stimuli. Senescent cells remain viable, have alterations in metabolic activity and undergo dramatic changes in gene expression and develop a complex senescence-associated secretory phenotype. Cellular senescence can compromise tissue repair and regeneration, thereby contributing toward aging. Removal of senescent cells can attenuate age-related tissue dysfunction and extend health span. Senescence can also act as a potent anti-tumor mechanism, by preventing proliferation of potentially cancerous cells. It is a cellular program which acts as a double-edged sword, with both beneficial and detrimental effects on the health of the organism, and considered to be an example of evolutionary antagonistic pleiotropy. Activation of the p53/p21WAF1/CIP1 and p16INK4A/pRB tumor suppressor pathways play a central role in regulating senescence. Several other pathways have recently been implicated in mediating senescence and the senescent phenotype. Herein we review the molecular mechanisms that underlie cellular senescence and the senescence associated growth arrest with a particular focus on why cells stop dividing, the stability of the growth arrest, the hypersecretory phenotype and how the different pathways are all integrated.

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Section: Discussionmentioning

confidence: 99%

Mechanisms of Cellular Senescence: Cell Cycle Arrest and Senescence Associated Secretory Phenotype

Kumari

Jat

2021

Front. Cell Dev. Biol.

708

657

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“…Thus, to understand the pleiotropic phenotypes of senescent cells, a shift from traditional reductionism to more systematic, multi-parametric approaches is needed. The development of sophisticated high-throughput methods and machine learning tools that can handle multi-omics data will help achieve this goal (Vougas et al, 2019). Although ''old'' and ''new'' have pros and cons, we can combine the best to achieve a ''de profundis'' analysis of senescent phenotypes.…”

Section: Conclusion Open Questions and Perspectivesmentioning

confidence: 99%

Cellular Senescence: Defining a Path Forward

Gorgoulis

Adams

Alimonti

et al. 2019

Cell

Self Cite

1,732

2,036

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Cellular senescence is a cell state implicated in various physiological processes and a wide spectrum of age-related diseases. Recently, interest in therapeutically targeting senescence to improve healthy aging and age-related disease, otherwise known as senotherapy, has been growing rapidly. Thus, the accurate detection of senescent cells, especially in vivo, is essential. Here, we present a consensus from the International Cell Senescence Association (ICSA), defining and discussing key cellular and molecular features of senescence and offering recommendations on how to use them as biomarkers. We also present a resource tool to facilitate the identification of genes linked with senescence, SeneQuest (available at http://Senequest.net). Lastly, we propose an algorithm to accurately assess and quantify senescence, both in cultured cells and in vivo. Cellular Senescence: Walking a Line between Life and Death Cell states link both physiological and stress signals to tissue homeostasis and organismal health. In both cases, the outcomes vary and are determined by the signal characteristics (type, magnitude, and duration), spatiotemporal parameters (where and when), and cellular capacity to respond (Gorgoulis et al., 2018). In the case of potentially damaging stress, damage is reversed and the structural and functional integrity of cells restored. Alternatively, damage can be irreversible, and cells activate death mechanisms mainly to restrict the impact on tissue degeneration. Between these extremes, cells can acquire other states, often associated with survival but also with permanent structural and functional changes. An example is the non-proliferative but viable state, distinct from G0 quiescence and terminal differentiation, termed cellular senescence (Rodier and Campisi, 2011). Formally described in 1961 by Hayflick and colleagues, cellular senescence, derived from the latin word senex meaning ''old'' (Hayflick and Moorhead, 1961), was originally observed in normal diploid cells that

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“…On the other hand, the unessential data significantly diminish the viability interruption discovery framework. To improve the interruption discovery execution of the progressive effects for the profoundly prepared bunch staggered group dependent mixed on conducting highlights for the substance highlights [13][14]. Various decision-making support systems exist in the real-world environment that improve potentiality in achieving this concerning issue.…”

Section: Proposed Methodsmentioning

confidence: 99%

Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms

Papineni¹,

Yarlagadda²,

Akkineni³

et al. 2021

IJETT

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Data is evolving with the rapid progress of population and communication for various types of devices such as networks, cloud computing, Internet of Things (IoT), actuators, and sensors. The increment of data and communication content goes with the equivalence of velocity, speed, size, and value to provide the useful and meaningful knowledge that helps to solve the future challenging tasks and latest issues. Besides, multicriteria based decision making is one of the key issues to solve for various issues related to the alternative effects in big data analysis. It tends to find a solution based on the latest machine learning techniques that include algorithms like decision making and deep learning mechanism based on multicriteria in providing insights to big data. On the other hand, the derivations are made for it to go with the approximations to increase the duality of runtime and improve the entire system's potentiality and efficacy. In essence, several fields, including business, agriculture, information technology, and computer science, use deep learning and multicriteria-based decision-making problems. This paper aims to provide various applications that involve the concepts of deep learning techniques and exploiting the multicriteria approaches for issues that are facing in big data analytics by proposing new studies with the fusion approaches of data-driven techniques.

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Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining

Abstract: and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining.

Cited by 88 publications

References 191 publications

Mechanisms of Cellular Senescence: Cell Cycle Arrest and Senescence Associated Secretory Phenotype

Mechanisms of Cellular Senescence: Cell Cycle Arrest and Senescence Associated Secretory Phenotype

Cellular Senescence: Defining a Path Forward

Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms

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