Derin Öğrenme Teknikleri Kullanarak İkili ve Çok Etiketli Sınıflandırma İle Enzimatik Fonksiyon Tahmini

Year 2021, Issue: 32, 262 - 267, 31.12.2021

Münevver Baran Mustafa Öztürk Fatma Latifoğlu

https://doi.org/10.31590/ejosat.1041643

Abstract

Biyolojik katilazör olarak görev yapan enzimler katalizlediği tepkime türüne ve mekanizmasına göre sınıflandırılırken her sınıf altında substrat seçiciliği durumlarına göre de alt sınıflar oluşturulmuştur. Aynı zamanda enzimlerin sınıflandırılmasında yapısal, kimyasal ve bağlantısallık özellikleri önemli olmaktadır. Enzim fonksiyonunu tahmini yeni enzimlerin tasarlamalarına yardımcı olmak ve enzimle ilişkili hastalıkları teşhisinde önemli olmaktadır. Enzimlerin önemli bir çoğunluğu belirli reaksiyonları gerçekleştiriken, sınırlı sayıda enzim farklı reaksiyonlar gerçekleştirebilmektedir. Bu nedenle birden fazla enzimatik fonksiyonla doğrudan ilişkilendirilebilmektedir.
Gerçekleştirilen bu çalışmada enzimatik fonksiyonun ikili ve çok etiketli sınıflandırma ile tahmini amaçlanmıştır. Enzimlerin sınıflandırılmasında daha başarılı sonuçların kimyasal özelliklerin kullanılmasında ortaya çıktığı görülmüştür. Ancak tüm özelliklerin kullanılması durumunda sınıflandırma performansının daha da arttığı görülmüştür. Enzimatik fonksiyon tahmnine yönelik kullanılan modellerin başarısı incelendiğinde Derin Öğrenme modellerinin hem ikili hemde çok etiketli sınıflandırma performansının daha yüksek olduğu görülmüştür. Sonuç olarak önerilen modellerinin enzimatik fonksiyonların sınıflandırılmasında önemli bir araç olduğu ortaya konmuştur.

Keywords

Enzimatik fonksiyon, Enzim Komisyonu numaraları, Derin Sinir Ağları, Makine Öğrenmesi

Enzymatic Function Estimation with Binary and Multilabel Classification Using Deep Learning Techniques

Abstract

Enzymes that act as biological catalysts are classified according to the reaction type and mechanism they catalyze, while subclasses are formed under each class according to their substrate selectivity. At the same time, structural, chemical and connectivity features are important in the classification of enzymes. Predicting enzyme function is important in helping to design new enzymes and in diagnosing enzyme-related diseases. While a significant majority of enzymes carry out certain reactions, a limited number of enzymes can perform different reactions. Therefore, it can be directly associated with more than one enzymatic function. In this study, it was aimed to predict the enzymatic function by binary and multi-label classification. It has been observed that more successful results the use of chemical properties in have emerged in the classification of enzymes. However, it was observed that the classification performance increased even more when all features were used. When the success of the models used for enzymatic function estimation was examined, it was seen that the Deep Learning models had higher both binary and multi-label classification performance. As a result, it has been demonstrated that the proposed models are an important tool in the classification of enzymatic functions.

Keywords

Enzymatic function, Enzyme Commission numbers, Deep Neural Networks, Machine Learning

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