Integration of Non-Negative Matrix Factorization to Identification of Immune System Checkpoints

Creative Commons License

Siyah B., Kalaycı M. E., Özdemir S., Berber T., Turhan K.

15. Tıp Bilişimi Kongresi, Trabzon, Türkiye, 30 - 31 Mayıs 2024, ss.158-170

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Trabzon
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.158-170
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

 In this study, we aimed to discover potential new immune checkpoint candidates by analyzing 20,000 articles from the Scope database. We employed the non-negative matrix factorization method (NMF), a powerful technique for extracting meaningful information from high-dimensional data, to identify sentences regarding novel immune checkpoint. The NMF analysis revealed several promising candidate sentences that could potentially represent new immune checkpoint molecules or pathways. The identification of new immune checkpoint candidates could have significant implications for the development of novel immunotherapy strategies for cancer and other diseases. Overall, this study highlights the importance of data-driven approaches in advancing and identifying new therapeutic targets for immune system related disorders.