Yazar
Sefer, Emre, Kingsford, C.
Basım Tarihi
2021-05-01
Basım Yeri
-
Mary Ann Liebert, Inc.
Konu
Gene ontology, Metric labeling, Protein function prediction
Tür
Süreli Yayın
Dil
İngilizce
Dijital
Evet
Yazma
Hayır
Kütüphane
Özyeğin Üniversitesi
Demirbaş Numarası
1066-5277, 1066-5277
Kayıt Numarası
5cb0f7ff-5992-4bfa-8d41-3b76567cb575
Lokasyon
Computer Science
Tarih
2021-05-01
Örnek Metin
Computational techniques have been successful at predicting protein function from relational data (functional or physical interactions). These techniques have been used to generate hypotheses and to direct experimental validation. With few exceptions, the task is modeled as multilabel classification problems where the labels (functions) are treated independently or semi-independently. However, databases such as the Gene Ontology provide information about the similarities between functions. We explore the use of the Metric Labeling combinatorial optimization problem to make use of heuristically computed distances between functions to make more accurate predictions of protein function in networks derived from both physical interactions and a combination of other data types. To do this, we give a new technique (based on convex optimization) for converting heuristic semimetric distances into a metric with minimum least-squared distortion (LSD). The Metric Labeling approach is shown to outperform five existing techniques for inferring function from networks. These results suggest that Metric Labeling is useful for protein function prediction, and that LSD minimization can help solve the problem of converting heuristic distances to a metric.
DOI
10.1089/cmb.2020.0425
Cilt
28
