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Journal articles on the topic 'Gene ontology'

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Published: 4 June 2021
Last updated: 16 June 2025

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1

Williams, Jennifer, and William Andersen. "Bringing Ontology to the Gene Ontology." Comparative and Functional Genomics 4, no. 1 (2003): 90–93. http://dx.doi.org/10.1002/cfg.253.

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2

Al-Mubaid, Hisham. "Gene multifunctionality scoring using gene ontology." Journal of Bioinformatics and Computational Biology 16, no. 05 (2018): 1840018. http://dx.doi.org/10.1142/s0219720018400188.

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Multifunctional genes are important genes because of their essential roles in human cells. Studying and analyzing multifunctional genes can help understand disease mechanisms and drug discovery. We propose a computational method for scoring gene multifunctionality based on functional annotations of the target gene from the Gene Ontology. The method is based on identifying pairs of GO annotations that represent semantically different biological functions and any gene annotated with two annotations from one pair is considered multifunctional. The proposed method can be employed to identify multifunctional genes in the entire human genome using solely the GO annotations. We evaluated the proposed method in scoring multifunctionality of all human genes using four criteria: gene-disease associations; protein–protein interactions; gene studies with PubMed publications; and published known multifunctional gene sets. The evaluation results confirm the validity and reliability of the proposed method for identifying multifunctional human genes. The results across all four evaluation criteria were statistically significant in determining multifunctionality. For example, the method confirmed that multifunctional genes tend to be associated with diseases more than other genes, with significance [Formula: see text]. Moreover, consistent with all previous studies, proteins encoded by multifunctional genes, based on our method, are involved in protein–protein interactions significantly more ([Formula: see text]) than other proteins.
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3

Akhmad, Dinar Munggaran. "PENGUKURAN KEMIRIPAN SEMANTIK BERBASIS GRAPH PADA GENE ONTOLOGY." Computatio : Journal of Computer Science and Information Systems 4, no. 2 (2020): 102. http://dx.doi.org/10.24912/computatio.v4i2.9678.

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Ontologi mendukung suatu sistem Knowledge Management dan membuka kemungkinan untuk berpindah dari pandangan berorientasi dokumen ke arah pengetahuan yang saling terkait dan dapat dimanfaatkan kembali secara lebih fleksibel dan dinamis. Salah satu dokumen ontology yang sangat berperan dalam dunia bioinformatika adalah Gene Ontology. Gene Ontology dibangun berbasis Graph, memuat banyak term/istilah tentang Gen pada makhluk hidup. Gen dapat melakukan mutasi dan hal ini menyebabkan resistensi terhadap segala jenis obat. Gen yang diketahui menyebabkan penyakit malaria resisten terhadap obat antimalarial yaitu gen dhfr dan dhps. Penelusuran kemungkinan gen lain yang resisten dapat dilakukan dengan mengetahui sifat-sifat gen yang berhubungan dengan resistensi tersebut. Salah satu cara adalah dengan menghitung kemiripannya secara semantik melalui pendekatan path length metode Wang. Hasil penelitian menunjukkan bahwa rentang nilai kemiripan kedua gen tersebut adalah 0 – 1. Nilai kemiripan diuji pada salah satu komponen dalam Gene Ontology yaitu pada Molecular Function dengan nilai kemiripan sebesar 0.35 karena terhubung oleh 2 node yang sama yaitu catalytic activity. Dengan demikian penelitian ini diharapkan dapat mendeteksi gen lain yang terindikasi resistensi sebelum penelitian lebih lanjut secara molekuler.
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4

Stevenson, Dennis, and Cecilia Zumajo-Cardona. "From Plant Ontology to Gene Ontology and back." Current Plant Biology 14 (September 2018): 66–69. http://dx.doi.org/10.1016/j.cpb.2018.09.009.

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5

Zhao, Liang. "GOBO: a Sub-Ontology API for Gene Ontology." IEIT Journal of Adaptive and Dynamic Computing 2011, no. 1 (2011): 29. http://dx.doi.org/10.5813/www.ieit-web.org/ijadc/2011.1.5.

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6

Lovering, R., P. Scambler, M. Hubank, R. Apweiler, and P. J. Talmud. "CARDIOVASCULAR GENE ONTOLOGY INITIATIVE." Atherosclerosis Supplements 9, no. 1 (2008): 101. http://dx.doi.org/10.1016/s1567-5688(08)70404-5.

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7

Joslyn, C. A., S. M. Mniszewski, A. Fulmer, and G. Heaton. "The Gene Ontology Categorizer." Bioinformatics 20, Suppl 1 (2004): i169—i177. http://dx.doi.org/10.1093/bioinformatics/bth921.

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8

Camon, E. "The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology." Nucleic Acids Research 32, no. 90001 (2004): 262D—266. http://dx.doi.org/10.1093/nar/gkh021.

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9

Peng, Jiajie, Tao Wang, Jixuan Wang, Yadong Wang, and Jin Chen. "Extending gene ontology with gene association networks." Bioinformatics 32, no. 8 (2015): 1185–94. http://dx.doi.org/10.1093/bioinformatics/btv712.

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10

Xie, Hanqing. "Gene Ontology-Facilitated Genome Analysis." Current Genomics 4, no. 7 (2003): 569–74. http://dx.doi.org/10.2174/1389202033490196.

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11

Denny, Paul, Marc Feuermann, David P. Hill, Ruth C. Lovering, Helene Plun-Favreau, and Paola Roncaglia. "Exploring autophagy with Gene Ontology." Autophagy 14, no. 3 (2018): 419–36. http://dx.doi.org/10.1080/15548627.2017.1415189.

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12

Lovering, Ruth C., Emily C. Dimmer, and Philippa J. Talmud. "Improvements to cardiovascular Gene Ontology." Atherosclerosis 205, no. 1 (2009): 9–14. http://dx.doi.org/10.1016/j.atherosclerosis.2008.10.014.

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13

Geifman, Nophar, Alon Monsonego, and Eitan Rubin. "The Neural/Immune Gene Ontology: clipping the Gene Ontology for neurological and immunological systems." BMC Bioinformatics 11, no. 1 (2010): 458. http://dx.doi.org/10.1186/1471-2105-11-458.

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14

Fruzangohar, Mario, Esmaeil Ebrahimie, Abiodun D. Ogunniyi, Layla K. Mahdi, James C. Paton, and David L. Adelson. "Comparative GO: A Web Application for Comparative Gene Ontology and Gene Ontology-Based Gene Selection in Bacteria." PLoS ONE 8, no. 3 (2013): e58759. http://dx.doi.org/10.1371/journal.pone.0058759.

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15

Agapito, Giuseppe, Marianna Milano, Pietro Hiram Guzzi, and Mario Cannataro. "Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations." IEEE/ACM Transactions on Computational Biology and Bioinformatics 13, no. 2 (2016): 197–208. http://dx.doi.org/10.1109/tcbb.2015.2462348.

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16

Couto, Francisco M., and Mário J. Silva. "Disjunctive shared information between ontology concepts: application to Gene Ontology." Journal of Biomedical Semantics 2, no. 1 (2011): 5. http://dx.doi.org/10.1186/2041-1480-2-5.

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17

Shen, Ying, and Lin Zhang. "Gene function prediction with knowledge from gene ontology." International Journal of Data Mining and Bioinformatics 13, no. 1 (2015): 50. http://dx.doi.org/10.1504/ijdmb.2015.070840.

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18

Adryan, B., and R. Schuh. "Gene-Ontology-based clustering of gene expression data." Bioinformatics 20, no. 16 (2004): 2851–52. http://dx.doi.org/10.1093/bioinformatics/bth289.

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19

Rue-Albrecht, Kévin, Paul A. McGettigan, Belinda Hernández, et al. "GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data." BMC Bioinformatics 17, no. 1 (2016): 126. https://doi.org/10.1186/s12859-016-0971-3.

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<strong>Background: </strong>Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors.<strong>Results: </strong>We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples.<strong>Conclusions: </strong>GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.
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20

Fruzangohar, Mario, Esmaeil Ebrahimie, Abiodun D. Ogunniyi, Layla K. Mahdi, James C. Paton, and David L. Adelson. "Correction: Comparative GO: A Web Application for Comparative Gene Ontology and Gene Ontology-Based Gene Selection in Bacteria." PLOS ONE 10, no. 4 (2015): e0125537. http://dx.doi.org/10.1371/journal.pone.0125537.

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21

Ebrahimie, Esmaeil, Mario Fruzangohar, Seyyed Hani Moussavi Nik, and Morgan Newman. "Gene Ontology-Based Analysis of Zebrafish Omics Data Using the Web Tool Comparative Gene Ontology." Zebrafish 14, no. 5 (2017): 492–94. http://dx.doi.org/10.1089/zeb.2016.1290.

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22

Hu, Yang, Wenyang Zhou, Jun Ren, et al. "Annotating the Function of the Human Genome with Gene Ontology and Disease Ontology." BioMed Research International 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/4130861.

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Increasing evidences indicated that function annotation of human genome in molecular level and phenotype level is very important for systematic analysis of genes. In this study, we presented a framework named Gene2Function to annotate Gene Reference into Functions (GeneRIFs), in which each functional description of GeneRIFs could be annotated by a text mining tool Open Biomedical Annotator (OBA), and each Entrez gene could be mapped to Human Genome Organisation Gene Nomenclature Committee (HGNC) gene symbol. After annotating all the records about human genes of GeneRIFs, 288,869 associations between 13,148 mRNAs and 7,182 terms, 9,496 associations between 948 microRNAs and 533 terms, and 901 associations between 139 long noncoding RNAs (lncRNAs) and 297 terms were obtained as a comprehensive annotation resource of human genome. High consistency of term frequency of individual gene (Pearson correlation = 0.6401,p=2.2e-16) and gene frequency of individual term (Pearson correlation = 0.1298,p=3.686e-14) in GeneRIFs and GOA shows our annotation resource is very reliable.
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23

T, Mecthaline Lithiya. "Prediction of Micro-RNA Diseases using Cross Ontology and Gene Ontology." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (2018): 1164–67. http://dx.doi.org/10.22214/ijraset.2018.3181.

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24

Dameron, Olivier, Charles Bettembourg, and Nolwenn Le Meur. "Measuring the Evolution of Ontology Complexity: The Gene Ontology Case Study." PLoS ONE 8, no. 10 (2013): e75993. http://dx.doi.org/10.1371/journal.pone.0075993.

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25

Manda, Prashanti, Seval Ozkan, Hui Wang, Fiona McCarthy, and Susan M. Bridges. "Cross-Ontology Multi-level Association Rule Mining in the Gene Ontology." PLoS ONE 7, no. 10 (2012): e47411. http://dx.doi.org/10.1371/journal.pone.0047411.

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26

Sant, David W., Michael Sinclair, Christopher J. Mungall, et al. "Sequence Ontology terminology for gene regulation." Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms 1864, no. 10 (2021): 194745. http://dx.doi.org/10.1016/j.bbagrm.2021.194745.

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27

Pal, Debnath. "On gene ontology and function annotation." Bioinformation 1, no. 1 (2006): 97–98. http://dx.doi.org/10.6026/97320630001097.

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28

Alam-Faruque, Yasmin, Emily C. Dimmer, Rachael P. Huntley, Claire O’Donovan, Peter Scambler, and Rolf Apweiler. "The Renal Gene Ontology Annotation Initiative." Organogenesis 6, no. 2 (2010): 71–75. http://dx.doi.org/10.4161/org.6.2.11294.

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29

Ko, Song, Bo-Yeong Kang, and Dae-Won Kim. "Improving Clustering Performance Using Gene Ontology." Journal of Korean Institute of Intelligent Systems 19, no. 6 (2009): 802–8. http://dx.doi.org/10.5391/jkiis.2009.19.6.802.

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30

Tweedie, S., M. Ashburner, K. Falls, et al. "FlyBase: enhancing Drosophila Gene Ontology annotations." Nucleic Acids Research 37, Database (2009): D555—D559. http://dx.doi.org/10.1093/nar/gkn788.

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31

Lee, B., K. Brown, Y. Hathout, and J. Seo. "GOTreePlus: an interactive gene ontology browser." Bioinformatics 24, no. 7 (2008): 1026–28. http://dx.doi.org/10.1093/bioinformatics/btn068.

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32

Shahzad, Muhammad, Kamran Ahsan, Adnan Nadeem, and Muhammad Sarim. "Gene Ontology Tools: A Comparative Study." Journal of Basic & Applied Sciences 11 (December 11, 2015): 619–29. http://dx.doi.org/10.6000/1927-5129.2015.11.83.

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33

Ovezmyradov, Guvanch, Qianhao Lu, and Martin C. Göpfert. "Mining Gene Ontology Data with AGENDA." Bioinformatics and Biology Insights 6 (January 2012): BBI.S9101. http://dx.doi.org/10.4137/bbi.s9101.

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The Gene Ontology (GO) initiative is a collaborative effort that uses controlled vocabularies for annotating genetic information. We here present AGENDA (Application for mining Gene Ontology Data), a novel web-based tool for accessing the GO database. AGENDA allows the user to simultaneously retrieve and compare gene lists linked to different GO terms in diverse species using batch queries, facilitating comparative approaches to genetic information. The web-based application offers diverse search options and allows the user to bookmark, visualize, and download the results. AGENDA is an open source web-based application that is freely available for non-commercial use at the project homepage. URL: http://sourceforge.net/projects/bioagenda .
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34

Tu, Kang, Hui Yu, and Mingzhu Zhu. "MEGO: gene functional module expression based on gene ontology." BioTechniques 38, no. 2 (2005): 277–83. http://dx.doi.org/10.2144/05382rr04.

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35

Chang, Billy, Rafal Kustra, and Weidong Tian. "Functional-Network-Based Gene Set Analysis Using Gene-Ontology." PLoS ONE 8, no. 2 (2013): e55635. http://dx.doi.org/10.1371/journal.pone.0055635.

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36

Liu, Qi, Yong Deng, Chuan Wang, Tie-Liu Shi, and Yi-Xue Li. "Correlating Expression Data with Gene Function Using Gene Ontology†." Chinese Journal of Chemistry 24, no. 9 (2006): 1247–54. http://dx.doi.org/10.1002/cjoc.200690232.

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37

Gan, Mingxin. "Correlating Information Contents of Gene Ontology Terms to Infer Semantic Similarity of Gene Products." Computational and Mathematical Methods in Medicine 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/891842.

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Successful applications of the gene ontology to the inference of functional relationships between gene products in recent years have raised the need for computational methods to automatically calculate semantic similarity between gene products based on semantic similarity of gene ontology terms. Nevertheless, existing methods, though having been widely used in a variety of applications, may significantly overestimate semantic similarity between genes that are actually not functionally related, thereby yielding misleading results in applications. To overcome this limitation, we propose to represent a gene product as a vector that is composed of information contents of gene ontology terms annotated for the gene product, and we suggest calculating similarity between two gene products as the relatedness of their corresponding vectors using three measures: Pearson’s correlation coefficient, cosine similarity, and the Jaccard index. We focus on the biological process domain of the gene ontology and annotations of yeast proteins to study the effectiveness of the proposed measures. Results show that semantic similarity scores calculated using the proposed measures are more consistent with known biological knowledge than those derived using a list of existing methods, suggesting the effectiveness of our method in characterizing functional relationships between gene products.
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38

Hill, David P., Tanya Z. Berardini, Douglas G. Howe, and Kimberly M. Van Auken. "Representing ontogeny through ontology: A developmental biologist's guide to the gene ontology." Molecular Reproduction and Development 77, no. 4 (2009): 314–29. http://dx.doi.org/10.1002/mrd.21130.

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39

Newton, Joe R. "Linked gene ontology categories are novel and differ from associated gene ontology categories for the bipolar disorders." Psychiatric Genetics 17, no. 1 (2007): 29–34. http://dx.doi.org/10.1097/ypg.0b013e328010f28c.

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40

Shanthini, S. Booma, and V. Bhuvaneswari. "GENE ONTOLOGY SIMILARITY METRIC BASED ON DAG USING DIABETIC GENE." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 05 (2013): 108–13. https://doi.org/10.5281/zenodo.14594816.

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Bioinformatics and Data Mining provide exciting and challenging researches in several application areas especially in computer science. The association between gene and diseases are analyzed using data mining techniques. The objective of the paper is to study the various similarity metrics for analyzing the diabetic gene using data mining technique. This paper provides with an overview of different similarity metrics for gene clustering. A similarity metric is proposed to cluster diabetic gees based on DAG structure of gene ontology. The experimental verification is analyzed for evaluating the cluster with biological validation. The current OMIM dataset is used for the proposed work.&nbsp;
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41

Liang, Wang, and Zhao Kai Yong. "Translate gene sequence into gene ontology terms based on statistical machine translation." F1000Research 2 (November 1, 2013): 231. http://dx.doi.org/10.12688/f1000research.2-231.v1.

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This paper presents a novel method to predict the functions of amino acid sequences, based on statistical machine translation programs. To build the translation model, we use the “parallel corpus” concept. For instance, an English sentence “I love apples” and its corresponding French sentence “j’adore les pommes” are examples of a parallel corpus. Here we regard an amino acid sequence like “MTMDKSELVQKA” as one language, and treat its functional description as “0005737 0006605 0019904 (Gene Ontology terms)” as a sentence of another language. We select amino acid sequences and their corresponding functional descriptions in Gene Ontology terms to build the parallel corpus. Then we use a phrase-based translation model to build the “amino acid sequence” to “protein function” translation model. The Bilingual Evaluation Understudy (BLEU) score, an algorithm for measuring the quality of machine-translated text, of the proposed method reaches about 0.6 when neglecting the order of Gene Ontology words. Although its functional prediction performance is still not as accurate as search-based methods, it was able to give the function of amino acid sequences directly and was more efficient.
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42

Roncaglia, Paola, Maryann E. Martone, David P. Hill, et al. "The Gene Ontology (GO) Cellular Component Ontology: integration with SAO (Subcellular Anatomy Ontology) and other recent developments." Journal of Biomedical Semantics 4, no. 1 (2013): 20. http://dx.doi.org/10.1186/2041-1480-4-20.

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43

Lagreid, A. "Predicting Gene Ontology Biological Process From Temporal Gene Expression Patterns." Genome Research 13, no. 5 (2003): 965–79. http://dx.doi.org/10.1101/gr.1144503.

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44

Denaxas, Spiridon C., and Christos Tjortjis. "Scoring and summarising gene product clusters using the Gene Ontology." International Journal of Data Mining and Bioinformatics 2, no. 3 (2008): 216. http://dx.doi.org/10.1504/ijdmb.2008.020523.

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45

Cheng, Liangxi, Hongfei Lin, Yuncui Hu, Jian Wang, and Zhihao Yang. "Gene Function Prediction Based on the Gene Ontology Hierarchical Structure." PLoS ONE 9, no. 9 (2014): e107187. http://dx.doi.org/10.1371/journal.pone.0107187.

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46

Popescu, M., J. M. Keller, and J. A. Mitchell. "Fuzzy Measures on the Gene Ontology for Gene Product Similarity." IEEE/ACM Transactions on Computational Biology and Bioinformatics 3, no. 3 (2006): 263–74. http://dx.doi.org/10.1109/tcbb.2006.37.

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47

Chen, Chien-Ming, Yu-Lun Lu, Chi-Pong Sio, Guan-Chung Wu, Wen-Shyong Tzou, and Tun-Wen Pai. "Gene Ontology based housekeeping gene selection for RNA-seq normalization." Methods 67, no. 3 (2014): 354–63. http://dx.doi.org/10.1016/j.ymeth.2014.01.019.

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48

Tu, Kang, Hui Yu, Zheng Guo, and Xia Li. "Learnability-based further prediction of gene functions in Gene Ontology." Genomics 84, no. 6 (2004): 922–28. http://dx.doi.org/10.1016/j.ygeno.2004.08.005.

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49

Zhao, Yingwen, Guangyuan Fu, Jun Wang, Maozu Guo, and Guoxian Yu. "Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing." Genomics 111, no. 3 (2019): 334–42. http://dx.doi.org/10.1016/j.ygeno.2018.02.008.

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50

Sykacek, P. "Bayesian assignment of gene ontology terms to gene expression experiments." Bioinformatics 28, no. 18 (2012): i603—i610. http://dx.doi.org/10.1093/bioinformatics/bts405.

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