Characterisation of semantic similarity on gene ontology based on a shortest path approach

Shen, Ying; Zhang, Shaohong; Wong, Hau-San; Zhang, Lin

doi:10.1504/ijdmb.2014.062887

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…Algoritma PATH adalah metode yang umum digunakan untuk mengukur kesamaan semantik, terutama dalam konteks aplikasi berbasis ontologi. Algoritma ini didasarkan pada jalur terpendek dan kedalaman maksimum dari grafik ontologi, dan telah diterapkan dalam berbagai domain, termasuk biologi, biologi komputasional, dan ilmu komputer [19], [20].…”

Section: E Algoritma Path Untuk Mengukur Kemiripan Semantik Dengan Al...unclassified

Pengukuran Kemiripan Tugas Pokok Dan Fungsi Sekretariat Dinas Pendidikan Kota Di Jawa Timur Menggunakan Ahp

Syafiuddin,

Yaqin

2023

INDEXIA

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Permasalahan yang akan diatasi dalam penelitian ini adalah kebutuhan akan pengaturan standar tugas pokok dan fungsi (tupoksi) untuk sekretariat Dinas Pendidikan Kota di Jawa Timur. Metode penelitian melibatkan pengumpulan data tupoksi sekretariat dari berbagai kota, dilanjutkan dengan analisis semantik dilakukan menggunakan algoritma PATH dengan alat bantu WS4J. Penggunaan WS4J untuk menilai kesamaan nilai hanya terbatas pada hubungan antar kata-kata. Maka, penyesuaian dilakukan menggunakan metode Analytical Hierarchy Process (AHP) untuk menentukan nilai relatif atau bobot. Dalam AHP, terdapat dua faktor yang menjadi kriteria penentuan bobot, yaitu kategori kata benda dan kata kerja. Hasil akhir penelitian ini berupa common fragment tupoksi sekretariat dari berbagai Dinas Pendidikan Kota di Jawa Timur. Common fragment ini nantinya dapat digunakan sebagai dasar standar yang konsisten, yang akan membantu mengatasi berbagai permasalahan yang muncul akibat tupoksi tidak terstandarisasi.

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Section: E Algoritma Path Untuk Mengukur Kemiripan Semantik Dengan Al...unclassified

Pengukuran Kemiripan Tugas Pokok Dan Fungsi Sekretariat Dinas Pendidikan Kota Di Jawa Timur Menggunakan Ahp

Syafiuddin,

Yaqin

2023

INDEXIA

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“…Therefore, "is-a" influences more the semantic relationships between GO terms. Most works in literature (e.g., [37], [46]) assign "is-a" relations the weight 0.8 and assign "part-of" relations the weight 0.6.…”

Section: Assigning a Functional Weight To Each Edge In Go Graphmentioning

confidence: 99%

“…1. Consider that the Type Weights of "is-a" and "part-of" relations are 0.8 and 0.6 respectively [37], [46]. Consider that parameter described previously takes the following values (recall that ):…”

Section: Assigning a Functional Weight To Each Edge In Go Graphmentioning

confidence: 99%

RGFinder: A System for Determining Semantically Related Genes Using GO Graph Minimum Spanning Tree

Taha

2015

IEEE Trans.on Nanobioscience

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Biologists often need to know the set S' of genes that are the most functionally and semantically related to a given set S of genes. For determining the set S', most current gene similarity measures overlook the structural dependencies among the Gene Ontology (GO) terms annotating the set S, which may lead to erroneous results. We introduce in this paper a biological search engine called RGFinder that considers the structural dependencies among GO terms by employing the concept of existence dependency. RGFinder assigns a weight to each edge in GO graph to represent the degree of relatedness between the two GO terms connected by the edge. The value of the weight is determined based on the following factors: 1) type of the relation represented by the edge (e.g., an "is-a" relation is assigned a different weight than a "part-of" relation), 2) the functional relationship between the two GO terms connected by the edge, and 3) the string-substring relationship between the names of the two GO terms connected by the edge. RGFinder then constructs a minimum spanning tree of GO graph based on these weights. In the framework of RGFinder, the set S' is annotated to the GO terms located at the lowest convergences of the subtree of the minimum spanning tree that passes through the GO terms annotating set S. We evaluated RGFinder experimentally and compared it with four gene set enrichment systems. Results showed marked improvement.

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“…The edge-based approaches are based on counting the number of edges in the specific path between two terms. In most edge-based measures, a distance function is defined on the shortest path ( SP ) or on the average of all paths [ 17 , 18 ]. This distance can easily be converted into a similarity measure.…”

Section: Introductionmentioning

confidence: 99%

TopoICSim: a new semantic similarity measure based on gene ontology

Ehsani

Drabløs

2016

BMC Bioinformatics

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BackgroundThe Gene Ontology (GO) is a dynamic, controlled vocabulary that describes the cellular function of genes and proteins according to tree major categories: biological process, molecular function and cellular component. It has become widely used in many bioinformatics applications for annotating genes and measuring their semantic similarity, rather than their sequence similarity. Generally speaking, semantic similarity measures involve the GO tree topology, information content of GO terms, or a combination of both.ResultsHere we present a new semantic similarity measure called TopoICSim (Topological Information Content Similarity) which uses information on the specific paths between GO terms based on the topology of the GO tree, and the distribution of information content along these paths. The TopoICSim algorithm was evaluated on two human benchmark datasets based on KEGG pathways and Pfam domains grouped as clans, using GO terms from either the biological process or molecular function. The performance of the TopoICSim measure compared favorably to five existing methods. Furthermore, the TopoICSim similarity was also tested on gene/protein sets defined by correlated gene expression, using three human datasets, and showed improved performance compared to two previously published similarity measures. Finally we used an online benchmarking resource which evaluates any similarity measure against a set of 11 similarity measures in three tests, using gene/protein sets based on sequence similarity, Pfam domains, and enzyme classifications. The results for TopoICSim showed improved performance relative to most of the measures included in the benchmarking, and in particular a very robust performance throughout the different tests.ConclusionsThe TopoICSim similarity measure provides a competitive method with robust performance for quantification of semantic similarity between genes and proteins based on GO annotations. An R script for TopoICSim is available at http://bigr.medisin.ntnu.no/tools/TopoICSim.R.

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Characterisation of semantic similarity on gene ontology based on a shortest path approach

Cited by 4 publications

References 25 publications

Pengukuran Kemiripan Tugas Pokok Dan Fungsi Sekretariat Dinas Pendidikan Kota Di Jawa Timur Menggunakan Ahp

Pengukuran Kemiripan Tugas Pokok Dan Fungsi Sekretariat Dinas Pendidikan Kota Di Jawa Timur Menggunakan Ahp

RGFinder: A System for Determining Semantically Related Genes Using GO Graph Minimum Spanning Tree

TopoICSim: a new semantic similarity measure based on gene ontology

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