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  1. AU="Yimin Geng"
  2. AU="Wang, Chang-Sheng"
  3. AU="Doyle, Andrew D"
  4. AU="Redpath, Sophie H A"
  5. AU="Hopman, Maria Te"
  6. AU="Pomputius, William"
  7. AU="Agrawal, Sonali"
  8. AU="Martinez, Luis R"
  9. AU="Passoni, Lorena"
  10. AU="Slimani, Wafa"
  11. AU="Jin, J"
  12. AU="Xia, Hongmin"
  13. AU="Akdemir, İrem"
  14. AU=Ciccone Giovannino

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  1. Artikel ; Online: Improved Parallel Random Forest Algorithm Combining Information Theory and Norm

    MAO Yimin, GENG Junhao

    Jisuanji kexue yu tansuo, Vol 16, Iss 5, Pp 1064-

    2022  Band 1075

    Abstract: Aiming at the problems of excessive redundancy and irrelevant features, low training feature information and low parallelization efficiency in big data random forest algorithm based on MapReduce, this paper proposes a parallel random forest algorithm ... ...

    Abstract Aiming at the problems of excessive redundancy and irrelevant features, low training feature information and low parallelization efficiency in big data random forest algorithm based on MapReduce, this paper proposes a parallel random forest algorithm based on information theory and norm (PRFITN). Firstly, the algorithm designs the DRIGFN (dimension reduction based on information gain and Frobenius norm) strategy to reduce the number of redundant and irrelevant features. Secondly, a feature grouping strategy based on information theory (FGSIT) is proposed. According to the FGSIT strategy, the features are grouped, and the stratified sampling method is adopted to ensure the information amount of the training features when constructing the decision tree in the random forest. Accuracy of classification results is improved. Finally, in order to improve the parallel efficiency of the cluster, the redistribution of key-value pairs (RSKP) is presented to realize the rapid and uniform distribution of key-value pairs, and obtain the global classification results. Experimental results show that the algorithm has better classification effect in big data environment, especially for datasets with more features.
    Schlagwörter |mapreduce|random forest (rf)|drigfn strategy|feature grouping strategy based on information theory (fgsit)|redistribution of key-value pairs (rskp) strategy ; Electronic computers. Computer science ; QA75.5-76.95
    Thema/Rubrik (Code) 006
    Sprache Chinesisch
    Erscheinungsdatum 2022-05-01T00:00:00Z
    Verlag Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Systematic Review of Genetic Polymorphisms Associated with Acute Pain Induced by Radiotherapy for Head and Neck Cancers

    Vivian Salama / Yimin Geng / Jillian Rigert / Clifton D. Fuller / Sanjay Shete / Amy C. Moreno

    Clinical and Translational Radiation Oncology, Vol 43, Iss , Pp 100669- (2023)

    2023  

    Abstract: Background/objective: Pain is the most common acute symptom following radiation therapy (RT) for head and neck cancer (HNC). The multifactorial origin of RT-induced pain makes it highly challenging to manage. Multiple studies were conducted to identify ... ...

    Abstract Background/objective: Pain is the most common acute symptom following radiation therapy (RT) for head and neck cancer (HNC). The multifactorial origin of RT-induced pain makes it highly challenging to manage. Multiple studies were conducted to identify genetic variants associated with cancer pain, however few of them focused on RT-induced acute pain. In this review, we summarize the potential mechanisms of acute pain after RT in HNC and identify genetic variants associated with RT-induced acute pain and relevant acute toxicities. Methods: A comprehensive search of Ovid Medline, EMBASE and Web of Science databases using terms including “Variants”, “Polymorphisms”, “Radiotherapy”, “Acute pain”, “Acute toxicity” published up to February 28, 2022, was performed by two reviewers. Review articles and citations were reviewed manually. The identified SNPs associated with RT-induced acute pain and toxicities were reported, and the molecular functions of the associated genes were described based on genetic annotation using The Human Gene Database; GeneCards. Results: A total of 386 articles were identified electronically and 8 more articles were included after manual search. 21 articles were finally included. 32 variants in 27 genes, of which 25% in inflammatory/immune response, 20% had function in DNA damage response and repair, 20% in cell death or cell cycle, were associated with RT-inflammatory pain and acute oral mucositis or dermatitis. 4 variants in 4 genes were associated with neuropathy and neuropathic pain. 5 variants in 4 genes were associated with RT-induced mixed types of post-RT-throat/neck pain. Conclusion: Different types of pain develop after RT in HNC, including inflammatory pain; neuropathic pain; nociceptive pain; and mixed oral pain. Genetic variants involved in DNA damage response and repair, cell death, inflammation and neuropathic pathways may affect pain presentation post-RT. These variants could be used for personalized pain management in HNC patients receiving RT.
    Schlagwörter Genetic variants ; Polymorphisms ; Radiation pain ; Radiation mucositis ; Radiation dermatitis ; Head and neck cancers ; Medical physics. Medical radiology. Nuclear medicine ; R895-920 ; Neoplasms. Tumors. Oncology. Including cancer and carcinogens ; RC254-282
    Thema/Rubrik (Code) 616
    Sprache Englisch
    Erscheinungsdatum 2023-11-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Artikel ; Online: Safety and Efficacy of Influenza Vaccination in Patients Receiving Immune Checkpoint Inhibitors. Systematic Review with Meta-Analysis

    Maria A. Lopez-Olivo / Valeria Valerio / Aliza R. Karpes Matusevich / Marianela Brizio / Michelle Kwok / Yimin Geng / Maria E. Suarez-Almazor / Ines Colmegna

    Vaccines, Vol 10, Iss 1195, p

    2022  Band 1195

    Abstract: The potential increased risk of immune-related adverse events (irAEs) post-influenza vaccine is a concern in patients receiving immune checkpoint inhibitors (ICI). We conducted a systematic review with meta-analysis of studies reporting the effects of ... ...

    Abstract The potential increased risk of immune-related adverse events (irAEs) post-influenza vaccine is a concern in patients receiving immune checkpoint inhibitors (ICI). We conducted a systematic review with meta-analysis of studies reporting the effects of influenza vaccination in patients with cancer during ICI treatment. We searched five electronic databases until 01/2022. Two authors independently selected studies, appraised their quality, and collected data. The primary outcome was the determination of pooled irAE rates. Secondary outcomes included determination of immunogenicity and influenza infection rates and cancer-related outcomes. Nineteen studies (26 publications, n = 4705) were included; 89.5% were observational. Vaccinated patients reported slighter lower rates of irAEs compared to unvaccinated patients (32% versus 41%, respectively). Seroprotection for influenza type A was 78%–79%, and for type B was 75%. Influenza and irAE-related death rates were similar between groups. The pooled proportion of participants reporting a laboratory-confirmed infection was 2% (95% CI 0% to 6%), and influenza-like illness was 14% (95% CI 2% to 32%). No differences were reported on the rates of laboratory-confirmed infection between vaccinated and unvaccinated patients. Longer progression-free and overall survival was also observed in vaccinated compared with unvaccinated patients. Current evidence suggests that influenza vaccination is safe in patients receiving ICIs, does not increase the risk of irAEs, and may improve survival.
    Schlagwörter influenza vaccine ; systematic review ; meta-analysis ; immune checkpoint inhibitors ; cancer ; Medicine ; R
    Thema/Rubrik (Code) 610
    Sprache Englisch
    Erscheinungsdatum 2022-07-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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