Medical Informatics IV

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CHAPTER


DOI :10.26650/B/T3.2024.40.002   IUP :10.26650/B/T3.2024.40.002    Full Text (PDF)

Natural Language Processing in Bioinformatics

Orçun Taşar, Çiğdem Selçukcan Erol

As in many other areas, with the increase in the volume of data produced in the fields of mainly biology and medicine, both the data and the processes of transforming it into information began to become more complex. Therefore, it was inevitable that the bioinformatics discipline, which has great capabilities for the transformation and organization of data in the related domains, needed new tools. Bioinformatics practices have been greatly enhanced by the incorporation of machine learning and deep learning (methodologies, particularly over the last decade. Structured data has long been used in ML and DL studies to derive predictive outcomes in the clinical and biomedical worlds, and now, with the advent of natural language processing (NLP) within artificial intelligence practices, it is also possible to process unstructured textual data. NLP approaches, which can now be used in the development of automated systems for time-consuming, costly, and error-prone routine tasks, can even be used to detect biomedical elements relationships that have been in the literature for a long time but have never been noticed before. Thanks to innovative bioinformatics and AI-powered NLP techniques, the fields of biology, genetics, medicine, and pharmaceuticals are rapidly transforming. In this book chapter, the use cases of NLP within bioinformatics on data in the fields of biology and medicine are discussed with current real-life examples.


Keywords: Bioinformatics, natural language processing, NLP, medicine, biology
DOI :10.26650/B/T3.2024.40.002   IUP :10.26650/B/T3.2024.40.002    Full Text (PDF)

Bi̇yoenformati̇kte Doğal Di̇l İşleme

Orçun Taşar, Çiğdem Selçukcan Erol

Pek çok alanda olduğu gibi, biyoloji ve tıp alanlarında üretilen verinin boyutunun artması ile birlikte bu verinin bilgiye dönüştürülmesi için izlenecek süreçler de karmaşık hale gelmeye başlamış; bu durum ilgili alanlardaki verinin işlenmesi ve organize edilmesi için pek çok metodoloji barındıran biyoenformatik disiplininin yeni araçlara ihtiyaç duymasına sebebiyet vermiştir. Bu doğrultuda özellikle son on yılda makine öğrenmesi ve derin öğrenme metodolojilerinin de eklenmesiyle birlikte biyoenformatiğin uygulama alanları büyük ölçüde genişlemiş oldu. Yapısal veriler, uzun bir süredir klinik ve biyomedikal alanlarda tahmine dayalı çıktıların alınmasında kullanılmakta olup doğal dil işleme (natural language processing - NLP) yöntemlerinin gelişmesiyle artık benzer öngörülerin elde edilmesinde yazınsal metinler gibi yapısal olmayan verilerin de kullanılması mümkün hale gelmiştir. Doğal dil işleme yaklaşımları, maliyetli, zaman alıcı ve hataya yatkın rutin işlemlerin otomatize hale getirilmesinde kullanıldığı gibi, literatürde uzun zamandır mevcut olan ama gizli kalmış biyomedikal varlıkların arasındaki ilişkilerin ortaya çıkarılmasına da katkı vermektedir. Yapay zeka destekli doğal dil işleme teknikleri sayesinde biyoloji, genetik, tıp, ilaç keşfi gibi alanlar hızla dönüşmektedir. Bu kitap bölümünde doğal işleme yöntemlerinin biyoenformatikte kullanımı, biyoloji ve tıp alanlarında gerçekleştirilmiş çalışmalardan örnekler verilerek tartışılmıştır. 


Keywords: Biyoenformatik, doğal dil işleme, NLP, tıp, biyoloji

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