The Landscape of Gene Expression during Hyperfilamentous Biofilm Development in Oral Candida albicans Isolated from a Lung Cancer Patient

Chudzik-Rząd, Beata; Zalewski, Daniel; Kasela, Martyna; Sawicki, Rafał; Szymańska, Jolanta; Bogucka‐Kocka, Anna; Malm, Anna

doi:10.3390/ijms24010368

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…Bacteria and biofilms are known to play a critical role in the genesis of prostate calcifications [26,28,29], which can cause inflammation and ultimately lead to prostate cancer. Similarly, biofilm formation can lead to lung infections caused by microorganisms such as Pseudomonas aeruginosa and Enterococcus faecalis [30][31][32], which can further develop into lung cancer.…”

Section: Cancer Of the Lung Bronchus And Prostate And Preventionmentioning

confidence: 99%

A Feasibility Study of Machine Learning Models for Cancer Rate Prediction

Chen¹,

Ding²

2023

Preprint

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Cancer is a major concern for people, and accurately predicting the probability of cancer incidence and mortality is an important research topic. With the development of big data and artificial intelligence technology, a new machine learning model has emerged. Using 72,591 pieces of data, including age, case count, population size, race, gender, site of onset, and year of discovery, we built a machine learning model. Through calculations, we found that the decision tree, random forest, logistic regression, support vector machine, and neural network achieved testing accuracies of 62.11%, 61.68%, 54.53%, 55.72%, and 63.10%, respectively. With the help of this model, scientists and policymakers can accurately predict future cancer incidence and mortality rates through databases, allowing them to make relevant policies (such as timely allocating doctors and medical resources) to better serve the people.

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Section: Cancer Of the Lung Bronchus And Prostate And Preventionmentioning

confidence: 99%

A Feasibility Study of Machine Learning Models for Cancer Rate Prediction

Chen¹,

Ding²

2023

Preprint

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Machine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality

Ding

2024

Diseases

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In recent years, the escalating environmental challenges have contributed to a rising incidence of cancer. The precise anticipation of cancer incidence and mortality rates has emerged as a pivotal focus in scientific inquiry, exerting a profound impact on the formulation of public health policies. This investigation adopts a pioneering machine learning framework to address this critical issue, utilizing a dataset encompassing 72,591 comprehensive records that include essential variables such as age, case count, population size, race, gender, site, and year of diagnosis. Diverse machine learning algorithms, including decision trees, random forests, logistic regression, support vector machines, and neural networks, were employed in this study. The ensuing analysis revealed testing accuracies of 62.17%, 61.92%, 54.53%, 55.72%, and 62.30% for the respective models. This state-of-the-art model not only enhances our understanding of cancer dynamics but also equips researchers and policymakers with the capability of making meticulous projections concerning forthcoming cancer incidence and mortality rates. Considering sustainability, the application of this advanced machine learning framework emphasizes the importance of judiciously utilizing extensive and intricate databases. By doing so, it facilitates a more sustainable approach to healthcare planning, allowing for informed decision-making that takes into account the long-term ecological and societal impacts of cancer-related policies. This integrative perspective underscores the broader commitment to sustainable practices in both health research and public policy formulation.

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The Landscape of Gene Expression during Hyperfilamentous Biofilm Development in Oral Candida albicans Isolated from a Lung Cancer Patient

Cited by 2 publications

References 45 publications

A Feasibility Study of Machine Learning Models for Cancer Rate Prediction

A Feasibility Study of Machine Learning Models for Cancer Rate Prediction

Machine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality

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