Geri Dön

Relation prediction over biomedical knowledge bases for drugrepositioning

Başlık çevirisi mevcut değil.

PDF İndir

  1. Tez No: 626144
  2. Yazar: MEHMET GÖKHAN BAKAL
  3. Danışmanlar: PROF. DR. DANIŞMAN YOK
  4. Tez Türü: Doktora
  5. Konular: Biyomühendislik, Tıbbi Biyoloji, Bioengineering, Medical Biology
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2019
  8. Dil: İngilizce
  9. Üniversite: Unıversıty Of Kentucky
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: Belirtilmemiş.

Özet

Relation Prediction over Biomedical Knowledge Bases for Drug Repositioning Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task using existing biomedical knowledge bases. Our approaches can be broadly labeled as link prediction or knowledge base completion in computer science literature. Specifically, first we investigate the predictive power of graph paths connecting entities in the publicly available biomedical knowledge base, SemMedDB (the entities and relations constitute a large knowledge graph as a whole). To that end, we build logistic regression models utilizing semantic graph pattern features extracted from the SemMedDB to predict treatment and causative relations in Unified Medical Language System (UMLS) Metathesaurus. Second, we study matrix and tensor factorization algorithms for predicting drug repositioning pairs in repoDB, a general purpose gold standard database of approved and failed drug–disease indications. The idea here is to predict repoDB pairs by approximating the given input matrix/tensor structure where the value of a cell represents the existence of a relation coming from SemMedDB and UMLS knowledge bases. The essential goal is to predict the test pairs that have a blank cell in the input matrix/tensor based on the shared biomedical context among existing non-blank cells. Our final approach involves graph convolutional neural networks where entities and relation types are embedded in a vector space involving neighborhood information. Basically, we minimize an objective function to guide our model to concept/relation embeddings such that distance scores for positive relation pairs are lower than those for the negative ones. Overall, our results demonstrate that recent link prediction methods applied to automatically curated, and hence imprecise, knowledge bases can nevertheless result in high accuracy drug candidate prediction with appropriate configuration of both the methods and datasets used.

Özet (Çeviri)

Özet çevirisi mevcut değil.

Benzer Tezler

  1. Tez No

    333114

    Revealing gene interactions using Bayesian networks

    Gen etkileşimlerinin Bayezyen ağlar ile ortaya çıkarılması

    ŞENOL İŞÇİ

    Doktora

    İngilizce

    İngilizce

    2013

    BiyoistatistikBoğaziçi Üniversitesi

    PROF. DR. CENGİZHAN ÖZTÜRK

    YRD. DOÇ. DR. HASAN HÜSEYİN OTU

  2. Tez No

    652928

    Identification of tea plantation areas using Google cloud based random forest and deep learning

    Google bulut servise dayalı rastgele orman ve derin öğrenme ile çay tarım alanlarının belirlenmesi

    BERKAY ÖZEN

    Yüksek Lisans

    İngilizce

    İngilizce

    2020

    Jeodezi ve Fotogrametriİstanbul Teknik Üniversitesi

    Geomatik Mühendisliği Ana Bilim Dalı

    DOÇ. DR. ESRA ERTEN

  3. Tez No

    638082

    Klasik ve mikrogermeli ortam teorisiyle modellenen plaklarin caputo kesirli türevi yardimiyla nonlokal titreşim analizi

    Nonlocal vibration analysis of classic and microstretch plates with the help of caputo fractional derivative

    SONER AYDINLIK

    Doktora

    Türkçe

    Türkçe

    2020

    Matematikİstanbul Teknik Üniversitesi

    Matematik Mühendisliği Ana Bilim Dalı

    DOÇ. DR. AHMET KIRIŞ

  4. Tez No

    363577

    Meyve dokularında niteliksel ölçüm yapabilen elle tutulabilir spektroskopik donanım tasarımı ve gerçeklemesi

    Design and implementation of a hand-held devi̇ce to spectroscopically assess fruit quality

    ALİ SARIKAŞ

    Yüksek Lisans

    Türkçe

    Türkçe

    2014

    Elektrik ve Elektronik Mühendisliğiİstanbul Teknik Üniversitesi

    Elektronik ve Haberleşme Mühendisliği Ana Bilim Dalı

    PROF. DR. FATMA İNCİ ÇİLESİZ

  5. Tez No

    44638

    Gebeliğin indüklediği hipertansif olgularda bazı erken tanı metodlarının (Plazma Fibronektin` i, idrar kalsiyum/ kreatinin oranı ve roll-over testi) değerlendirilmesi

    Evaluation of some diagnostic tests (Plasma Fibronectin, urinary calcium/ creatinin ratio and roll-over test) in early prediction of pregnancy induced hypertension

    İZZET ŞAHİN

    Tıpta Uzmanlık

    Türkçe

    Türkçe

    1995

    Kadın Hastalıkları ve DoğumGazi Üniversitesi

    Kadın Hastalıkları ve Doğum Ana Bilim Dalı

    PROF.DR. ÖZDEMİR HİMMETOĞLU

Geri Dön