Metric labeling and semimetric embedding for protein annotation prediction

Name: Metric labeling and semimetric embedding for protein annotation prediction
Author: Sefer, Emre, Kingsford, C.

İsim	Metric labeling and semimetric embedding for protein annotation prediction
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

Kaynağa git Özyeğin Üniversitesi

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Özyeğin Üniversitesi

Kaynağa git

Metric labeling and semimetric embedding for protein annotation prediction

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