Geri Dön

A computational approach for prioritization of patient-specific cancer drivers

Başlık çevirisi mevcut değil.

  1. Tez No: 652689
  2. Yazar: AHMED AMINE TALEB BAHMED
  3. Danışmanlar: PROF. DR. CESİM ERTEN, DOÇ. DR. HİLAL KAZAN
  4. Tez Türü: Yüksek Lisans
  5. Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2020
  8. Dil: İngilizce
  9. Üniversite: Antalya Bilim Üniversitesi
  10. Enstitü: Lisansüstü Eğitim Enstitüsü
  11. Ana Bilim Dalı: Elektrik ve Bilgisayar Ana Bilim Dalı
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 60

Özet

A major challenge in cancer genomics is to distinguish the driver mutations that are causally linked to cancer from passenger mutations that are neutral and do not contribute to cancer development. The identification of these driver genes could lead to the development of therapies. Numerous methods have been proposed for this problem; however, the majority of these methods provide a single driver gene list for the entire cohort of patients. On the other hand, mutational profiles of cancer patients show a high degree of mutational heterogeneity. As such, because the set of driver genes can be distinct for each patient, a more ideal approach is to identify patient-specific drivers. The results from such an approach can lead to the development of personalized treatments and therapies. In this thesis, we develop a computational approach that integrates genomic data, biological pathways, and protein connectivity information to identify patient-specific cancer driver genes. We construct a bipartite graph that relates specific mutated genes and various outliers for each specific patient. For each patient, we rank the mutated genes based on a convex combination of two terms. The first term is a weighted scoring of the number of connections to outlier genes of that patient as well as the outlier genes of other patients. The second term incorporates the co-occurrences of a mutated gene and an outlier gene within the same pathway. We compare our method against state-of-the-art patient-specific cancer gene prioritization methods on patients and cell line data for colon, lung, and headneck cancer. We define novel reference gene sets for evaluation of results obtained from cell line data by utilizing drug sensitivity datasets. Furthermore, we propose and discuss alternative approaches for evaluating the recovery of known cancer drivers when patient-specific drivers are provided. Overall, we show that our method can better recover known and rare cancer genes based on various reference compared to other approaches. Additionally, we demonstrate the importance of pathway coverage in the identification and ranking of driver genes.

Özet (Çeviri)

A major challenge in cancer genomics is to distinguish the driver mutations that are causally linked to cancer from passenger mutations that are neutral and do not contribute to cancer development. The identification of these driver genes could lead to the development of therapies. Numerous methods have been proposed for this problem; however, the majority of these methods provide a single driver gene list for the entire cohort of patients. On the other hand, mutational profiles of cancer patients show a high degree of mutational heterogeneity. As such, because the set of driver genes can be distinct for each patient, a more ideal approach is to identify patient-specific drivers. The results from such an approach can lead to the development of personalized treatments and therapies. In this thesis, we develop a computational approach that integrates genomic data, biological pathways, and protein connectivity information to identify patient-specific cancer driver genes. We construct a bipartite graph that relates specific mutated genes and various outliers for each specific patient. For each patient, we rank the mutated genes based on a convex combination of two terms. The first term is a weighted scoring of the number of connections to outlier genes of that patient as well as the outlier genes of other patients. The second term incorporates the co-occurrences of a mutated gene and an outlier gene within the same pathway. We compare our method against state-of-the-art patient-specific cancer gene prioritization methods on patients and cell line data for colon, lung, and headneck cancer. We define novel reference gene sets for evaluation of results obtained from cell line data by utilizing drug sensitivity datasets. Furthermore, we propose and discuss alternative approaches for evaluating the recovery of known cancer drivers when patient specific drivers are provided. Overall, we show that our method can better recover known and rare cancer genes based on various reference compared to other approaches. Additionally, we demonstrate the importance of pathway coverage in the identification and ranking of driver genes.

Benzer Tezler

  1. Ranking cancer drivers via betweenness-based outlier detection and random walks

    Kanser sürücü genlerinin arasındalık bazlı aykırılık tanımı ve rastgele yürüyüşle tespiti

    AISSA HOUDJEDJ

    Yüksek Lisans

    İngilizce

    İngilizce

    2021

    Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolAntalya Bilim Üniversitesi

    Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı

    PROF. DR. CESİM ERTEN

    DOÇ. DR. HİLAL KAZAN

  2. An integrative approach to structured SNP prioritization and representative snp selection for genome-wide association studies

    Genom boyutunda ilişkilendirme çalışmalarında yapılandırılmış SNP önceliklendirmesi ve temsilci snp seçimi için bütünleşik bir yaklaşım

    GÜRKAN ÜSTÜNKAR

    Doktora

    İngilizce

    İngilizce

    2011

    Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolOrta Doğu Teknik Üniversitesi

    Bilişim Sistemleri Bölümü

    PROF. DR. GERHARD WİLHELM WEBER

    YRD. DOÇ. DR. YEŞİM AYDIN SON

  3. Yazılım tanımlı ağ tabanlı nesnelerin internetinde yönlendirme, kontrolör ve sunucu yerleştirme için mimari eniyilemesi

    Architecture optimization for forwarding, controller and server placement in software defined networking enabled internet of things

    YASİN İNAĞ

    Doktora

    Türkçe

    Türkçe

    2022

    Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolGazi Üniversitesi

    Bilgisayar Mühendisliği Ana Bilim Dalı

    DOÇ. DR. MEHMET DEMİRCİ

  4. Development of novel tools to study atonal in Neurogenesis

    Nörojenezde atonal çalışmak için yeni araçların geliştirilmesi

    SVEN PIERRE L. VILAIN

    Doktora

    İngilizce

    İngilizce

    2009

    BiyolojiKatholieke Universiteit Leuven (Catholic University of Leuven)

    Tıp Bilimleri Ana Bilim Dalı

    PROF. DR. BASSEM HASSAN

  5. An optimization model to control the flow of relief commodities in humanitarian supply chain under uncertainty

    Belirsiz koşullarda insani yardım tedarik zinciri malzeme akışını kontrol etmede optimizasyon modeli

    ISRAA ISMAIL

    Doktora

    İngilizce

    İngilizce

    2021

    Endüstri ve Endüstri Mühendisliğiİstanbul Teknik Üniversitesi

    Endüstri Mühendisliği Ana Bilim Dalı

    DOÇ. DR. ESRA BAŞ