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Journals / Zeki Sistemler Teori ve Uygulamaları Dergisi / 2021 / Cilt: 4 - Sayı: 2

Convolutional Neural Network Approach to Predict Tumor Samples Using Gene Expression Data

Gen İfade Verilerinde Konvolusyonel Sinir Ağı Kullanılarak Tümör Örneklerinin Tahmini

Büşra Nur DARENDELİ
Alper YILMAZ
Pages
136–141
Publication Date
2021-09-23
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In conclusion, our study shows that the deep learning approach and biological data have a huge potential in the diagnosis and identification of tumor samples.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Cancer is threatening millions of people each year and its early diagnosis is still a challenging task.
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Key Findings & Data:Early diagnosis is one of the major ways to tackle the disease and lower the mortality rate.
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Conclusion & Impact:In conclusion, our study shows that the deep learning approach and biological data have a huge potential in the diagnosis and identification of tumor samples.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:CNN · Gen İfadesi · Kanser

Abstract

Cancer is threatening millions of people each year and its early diagnosis is still a challenging task. Early diagnosis is one of the major ways to tackle the disease and lower the mortality rate. Advancements in deep learning approaches and the availability of biological data offer applications that can facilitate the diagnosis and characterization of cancer. Here, we aimed to provide a new perspective of cancer diagnosis using a deep learning approach on gene expression data. In this study, RNA-Seq data of approximately 30 different types of cancer patients the Cancer Genome Atlas (TCGA) study, and normal tissue RNA-Seq data from GTEx were used. The input data for the training was transformed to RGB format and the training was carried out with a Convolutional Neural Network (CNN). The trained algorithm is able to predict cancer with 97% accuracy, using gene expression data. In conclusion, our study shows that the deep learning approach and biological data have a huge potential in the diagnosis and identification of tumor samples.

Özet

Kanser her yıl milyonlarca insanı tehdit eden, erken teşhisi hala mümkün olmayan yaygın bir hastalıktır. Erken teşhis, kanserle baş etmenin ve ölüm oranını düşürmenin en önemli yollarından biridir. Derin öğrenme yaklaşımlarındaki gelişmeler ve biyolojik verilerdeki artış, kanserin teşhisini ve karakterizasyonunu kolaylaştırabilecek uygulamalar sunmaktadır. Bu çalışmada, gen ifade verilerini kullanarak derin öğrenme yaklaşımı ile kanser teşhisine yeni bir bakış açısı sağlamayı amaçladık. 30 farklı kanser çeşidine ait RNA-Seq verisi Kanser Genom Atlası (TCGA) adlı kaynaktan normal dokuların RNA-Seq verileri GTEx adlı kaynaktan temin edilip model eğitiminde kullanılmıştır. Gen ifade verileri RGB formatına dönüştürülüp Konvolusyonel Sinir Ağı (CNN) eğitimi için kullanıldı. Eğitilen model, gen ifade verilerine dayanarak kanseri %97 doğrulukla tahmin edebilmektedir. Sonuç olarak çalışmamız, derin öğrenme yaklaşımının ve biyolojik verilerin tümör örneklerinin tanısında büyük bir potansiyele sahip olduğunu göstermektedir.

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Cite This Article

DARENDELİ, B. N., YILMAZ, A. (2021). Convolutional Neural Network Approach to Predict Tumor Samples Using Gene Expression Data. Zeki Sistemler Teori ve Uygulamaları Dergisi, Cilt: 4 - Sayı: 2, 136–141. https://ajindex.com/articles/882942

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