The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information

Gene Ontology: tool for the unification of biology

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206

The Gene Ontology (GO) database and informatics resource

Gene Ontology Consortium (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh036 · Cited by 4,596

Mutation nomenclature extensions and suggestions to describe complex mutations: A discussion

den Dunnen, J. T., & Antonarakis, S. E. (2000) · Human Mutation · doi:10.1002/(sici)1098-1004(200001)15:1 · Cited by 1,757

Creating the Gene Ontology Resource: Design and ImplementationPreprint

The Gene Ontology Consortium (2001) · Genome Research · doi:10.1101/gr.180801 · Cited by 957

Online Mendelian Inheritance in Man 'OMIM'.

Amladi, S. (2007) · PubMed · Cited by 871

Creating the gene ontology resource: design and implementation.

Blake, J. A., Corradi, J., Eppig, J. T., Hill, D. P., Richardson, J. E., & Ringwald, M. (2001) · The Mouseion at the JAXlibrary (Jackson Laboratory) · Cited by 826

Biology of the Laboratory Mouse

(1941) · JAMA · doi:10.1001/jama.1941.02820440081034 · Cited by 805

The knockout mouse project

The Comprehensive Knockout Mouse Project Consortium (2004) · Nature Genetics · doi:10.1038/ng0904-921 · Cited by 608

Gene Ontology Consortium. The Gene Ontology (GO) database and informatics resource

Harris, M. A., Clark, J., Ireland, A., Lomax, J., Ashburner, M., Foulger, R. E., Eilbeck, K. L., Lewis, S., Marshall, B., Mungall, C., et al. (2004) · Cited by 295

Using ontologies to describe mouse phenotypes

Gkoutos, G. V., Green, E. C. J., Mallon, A., Hancock, J. M., & Davidson, D. R. (2004) · Genome biology · doi:10.1186/gb-2004-6-1-r8 · Cited by 241

An integrative, translational approach to understanding rare and orphan genetically based diseases

Hoehndorf, R., Schofield, P. N., & Gkoutos, G. V. (2013) · Interface Focus · doi:10.1098/rsfs.2012.0055 · Cited by 17

Management and Analysis of Genomic Functional and Phenotypic Controlled Annotations to Support Biomedical Investigation and Practice

Masseroli, M. (2007) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2006.884367 · Cited by 17

Exploring Genetic, Genomic, and Phenotypic Data at the Rat Genome Database

Laulederkind, S. J. F., Hayman, G. T., Wang, S., Lowry, T. F., Nigam, R., Petri, V., Smith, J. R., Dwinell, M. R., Jacob, H. J., & Shimoyama, M. E. (2012) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0114s40 · Cited by 16

GeneRIF is a more comprehensive, current and computationally tractable source of gene-disease relationships than OMIM

Osborne, J. D., Lin, S. M., Kibbe, W. A., Danila, M. I., & Rex, L. C. (2006) · Cited by 16

A Review of Current Standards and the Evolution of Histopathology Nomenclature for Laboratory Animals

Elmore, S. A., Cardiff, R. D., Cesta, M. F., Gkoutos, G. V., Hoehndorf, R., Keenan, C. M., McKerlie, C., Schofield, P. N., Sundberg, J. P., & Ward, J. M. (2018) · ILAR Journal · doi:10.1093/ilar/ily005 · Cited by 16

Integration of anatomy ontology data with protein–protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

Fernando, P. C., Mabee, P., & Zeng, E. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03773-2 · Cited by 16

Removing the cloak of invisibility: phenotyping the mouse

Justice, M. J. (2008) · Disease Models & Mechanisms · doi:10.1242/dmm.001057 · Cited by 16

The mouse-human anatomy ontology mapping project

Hayamizu, T. F., de Coronado, S., Fragoso, G., Sioutos, N., Kadin, J. A., & Ringwald, M. (2012) · Database · doi:10.1093/database/bar066 · Cited by 16

Exact score distribution computation for ontological similarity searches

Schulz, M. H., Köhler, S., Bauer, S., & Robinson, P. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-441 · Cited by 16

RNA-Sequencing Analysis Reveals a Regulatory Role for Transcription Factor Fezf2 in the Mature Motor Cortex

Clare, A. J., Wicky, H. E., Empson, R. M., & Hughes, S. M. (2017) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2017.00283 · Cited by 16

Rat Genome Databases, Repositories, and Tools.

Laulederkind, S., Hayman, G., Wang, S.-J., Hoffman, M., Smith, J. R., Bolton, E. R., Pons, J. D., Tutaj, M. A., Tutaj, M., Thota, J., et al. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9581-3_3 · Cited by 14

Genomic integration to identify molecular biomarkers associated with indicator traits of gastrointestinal nematode resistance in sheep

Berton, M. P., da Silva, R. P., Banchero, G. E., Mourão, G. B., Ferraz, J. B. S., Schenkel, F. S., & Baldi, F. S. (2022) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12682 · Cited by 15

Mouse Genome Database: From sequence to phenotypes and disease models

Eppig, J. T., Richardson, J. E., Kadin, J. A., Smith, C. L., Blake, J. A., Bult, C. J., & the MGD Team (2015) · genesis · doi:10.1002/dvg.22874 · Cited by 15

Identification of Key Nodes of Type 2 Diabetes Mellitus Protein Interactome and Study of their Interactions with Phloridzin

Randhawa, V., Sharma, P., Bhushan, S., & Bagler, G. (2013) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2012.0115 · Cited by 15

A Gene-Phenotype Network for the Laboratory Mouse and Its Implications for Systematic Phenotyping

Espinosa, O., & Hancock, J. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019693 · Cited by 15

Rat Strain Ontology: structured controlled vocabulary designed to facilitate access to strain data at RGD

Nigam, R., Munzenmaier, D. H., Worthey, E. A., Dwinell, M. R., Shimoyama, M. E., & Jacob, H. J. (2013) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-4-36 · Cited by 15

BOAT: Automatic alignment of biomedical ontologies using term informativeness and candidate selection

Chua, W. W. K., & Kim, J. (2011) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.11.010 · Cited by 15

Identification of Parkinson’s disease candidate genes using CAESAR and screening of MAPT and SNCAIP in South African Parkinson’s disease patients

Keyser, R. J., Oppon, E., Carr, J. A., & Bardien, S. (2011) · Journal of Neural Transmission · doi:10.1007/s00702-011-0591-z · Cited by 15

PHENOSITE: A WEB DATABASE INTEGRATING THE MOUSE PHENOTYPING PLATFORM AND THE EXPERIMENTAL PROCEDURES IN MICE

Masuya, H., Yoshikawa, S., Heida, N., Toyoda, T., Wakana, S., & Shiroishi, T. (2007) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720007003168 · Cited by 15

Equus in Online Mendelian Inheritance in Animals (OMIA)

Tammen, I., Bailey, E., Mather, M., & Nicholas, F. W. (2024) · Animals · doi:10.3390/ani14142069 · Cited by 14

Improving Phenotype Name Recognition

Khordad, M., Mercer, R. E., & Rogan, P. K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21043-3_30 · Cited by 14

PrismEXP: gene annotation prediction from stratified gene-gene co-expression matrices

Lachmann, A., Rizzo, K., Bartal, A., Jeon, M., Clarke, D. J. B., & Ma’ayan, A. (2023) · PeerJ · doi:10.7717/peerj.14927 · Cited by 14

From information to understanding: the role of model organism databases in comparative and functional genomics

Bult, C. J. (2006) · Animal Genetics · doi:10.1111/j.1365-2052.2006.01475.x · Cited by 14

Semantically enabling a genome-wide association study database

Beck, T., Free, R. C., Þórisson, G. Á., & Brookes, A. J. (2012) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-3-9 · Cited by 14

An ontology of fungal subcellular traits

Kumar, T. K. A., Crow, J., Wennblom, T. J., Abril, M., Letcher, P. M., Blackwell, M., Roberson, R. W., & McLaughlin, D. J. (2011) · American Journal of Botany · doi:10.3732/ajb.1100047 · Cited by 14

A Primer for the Rat Genome Database (RGD)

Laulederkind, S. J. F., Hayman, G. T., Wang, S., Smith, J. R., Petri, V., Hoffman, M. J., Pons, J. D., Tutaj, M., Ghiasvand, O., Tutaj, M., et al. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7737-6_8 · Cited by 13

Prioritizing Pain-Associated Targets with Machine Learning

Jeon, M., Jagodnik, K. M., Kropiwnicki, E., Stein, D. J., & Ma’ayan, A. (2021) · Biochemistry · doi:10.1021/acs.biochem.0c00930 · Cited by 13

Orthology for comparative genomics in the mouse genome database

Dolan, M. E., Baldarelli, R. M., Bello, S. M., Ni, L., McAndrews-Hill, M., Bult, C. J., Kadin, J. A., Richardson, J. E., Ringwald, M., Eppig, J. T., et al. (2015) · Mammalian Genome · doi:10.1007/s00335-015-9588-5 · Cited by 13

MUGEN mouse database; Animal models of human immunological diseases

Aidinis, V., Chandras, C., Manoloukos, M., Thanassopoulou, A., Kranidioti, K., Armaka, M., Douni, E. K., Kontoyiannis, D. L., Zouberakis, M., & Kollias, G. A. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm838 · Cited by 13

An integer programming framework for inferring disease complexes from network data

Mazza, A., Klockmeier, K., Wanker, E. E., & Sharan, R. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw692 · Cited by 13

Prediction of Protein Functions

Sleator, R. D. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-424-7_2 · Cited by 13

Clinical Ontologies for Discovery Applications

Lussier, Y. A., & Bodenreider, O. (2007) · Springer eBooks · doi:10.1007/978-0-387-48438-9_6 · Cited by 13

Phenobabelomics--mouse phenotype data resources

Hancock, J. M., & Mallon, A. (2008) · Briefings in Functional Genomics and Proteomics · doi:10.1093/bfgp/elm033 · Cited by 13

Learning to Recognize Phenotype Candidates in the Auto-Immune Literature Using SVM Re-Ranking

Collier, N., Tran, M.-V., Le, H.-Q., Ha, Q.-T., Oellrich, A., & Rebholz‐Schuhmann, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072965 · Cited by 12

Predicting causal genes from psychiatric genome-wide association studies using high-level etiological knowledge

Wainberg, M., Merico, D., Keller, M. C., Fauman, E. B., & Tripathy, S. J. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01542-6 · Cited by 12

Mining Skeletal Phenotype Descriptions from Scientific Literature

Groza, T., Hunter, J., & Zankl, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055656 · Cited by 12

Toward knowledge support for analysis and interpretation of complex traits

Collier, N., Oellrich, A., & Groza, T. (2013) · Genome biology · doi:10.1186/gb-2013-14-9-214 · Cited by 12

Uncovering Genes and Regulatory Pathways Related to Urinary Albumin Excretion

Hageman, R. S., Leduc, M. S., Caputo, C. R., Tsaih, S., Churchill, G. A., & Korstanje, R. (2010) · Journal of the American Society of Nephrology · doi:10.1681/asn.2010050561 · Cited by 12

Concept selection for phenotypes and diseases using learn to rank

Collier, N., Oellrich, A., & Groza, T. (2015) · Journal of Biomedical Semantics · doi:10.1186/s13326-015-0019-z · Cited by 12

Reprint of “Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction”

Rouillard, A. D., Wang, Z., & Ma’ayan, A. (2015) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2015.08.005 · Cited by 12

Open Biomedical Ontology-based Medline exploration

Xuan, W., Dai, M., Mirel, B., Song, J. Y., Athey, B. D., Watson, S. J., & Meng, F. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s5-s6 · Cited by 12

Phenotype Data: A Neglected Resource in Biomedical Research?

Groth, P., & Weiss, B. (2006) · Current Bioinformatics · doi:10.2174/157489306777828008 · Cited by 12

Reporting phenotypes in mouse models when considering body size as a potential confounder

Oellrich, A., Meehan, T. F., Parkinson, H., Sarntivijai, S., White, J. K., & Karp, N. A. (2016) · Journal of Biomedical Semantics · doi:10.1186/s13326-016-0050-8 · Cited by 12

Meta‐analysis: Congruence of genomic and phenotypic differentiation across diverse natural study systems

Wood, Z. T., Wiegardt, A. K., Barton, K. L., Clark, J. D., Homola, J. J., Olsen, B. J., King, B. L., Kovach, A. I., & Kinnison, M. T. (2021) · Evolutionary Applications · doi:10.1111/eva.13264 · Cited by 9

A knowledge based approach to matching human neurodegenerative disease and animal models

Maynard, S. M., Mungall, C., Lewis, S., Imam, F. T., & Martone, M. E. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00007 · Cited by 11

Analysis of disease-associated objects at the Rat Genome Database

Wang, S., Laulederkind, S. J. F., Hayman, G. T., Smith, J. R., Petri, V., Lowry, T. F., Nigam, R., Dwinell, M. R., Worthey, E. A., Munzenmaier, D. H., et al. (2013) · Database · doi:10.1093/database/bat046 · Cited by 11

Phenotype Mining for Functional Genomics and Gene Discovery

Groth, P., Leser, U., & Weiss, B. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-176-5_10 · Cited by 11

Where to search top-K biomedical ontologies?

Oliveira, D., Butt, A. S., Armin Haller, A., Rebholz‐Schuhmann, D., & Sahay, R. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby015 · Cited by 11

The Xenopus phenotype ontology: bridging model organism phenotype data to human health and development

Fisher, M. E., Segerdell, E., Matentzoglu, N., Nenni, M. J., Fortriede, J. D., Chu, S., Pells, T. J., Osumi-Sutherland, D., Chaturvedi, P., James‐Zorn, C., et al. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04636-8 · Cited by 11

Open biomedical pluralism: formalising knowledge about breast cancer phenotypes

Šojić, A., & Kutz, O. (2012) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-3-s2-s3 · Cited by 11

Meta-analysis and profiling of cardiac expression modules

Akavia, U. D., & Benayahu, D. (2008) · Physiological Genomics · doi:10.1152/physiolgenomics.90248.2008 · Cited by 11

Proteins: Form and function

(1991) · General Pharmacology The Vascular System · doi:10.1016/0306-3623(91)90044-7 · Cited by 11

Dintor: functional annotation of genomic and proteomic data

Weichenberger, C. X., Blankenburg, H., Palermo, A., D’Elia, Y., König, E., Bernstein, E., & Domingues, F. S. (2015) · BMC Genomics · doi:10.1186/s12864-015-2279-5 · Cited by 10

Longitudinal Analysis of Human Pancreatic Adenocarcinoma Development Reveals Transient Gene Expression Signatures

Kim, J., Ekstrom, T. J., Yang, W., Donahue, G., Grygoryev, D., Ngo, T. T. M., Muschler, J. L., Morgan, T. K., & Zaret, K. S. (2021) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-21-0483 · Cited by 10

Integration of global resources for human genetic variation and disease

Schofield, P. N., & Hancock, J. M. (2012) · Human Mutation · doi:10.1002/humu.22079 · Cited by 10

Two approaches to integrating phenotype and clinical information.

Burgun, A., Fleur Mougin, A., & Bodenreider, O. (2009) · PubMed Central · Cited by 10

Systematic analysis of gene properties influencing organ system phenotypes in mammalian perturbations

Vogt, I., Prinz, J., Worf, K., & Campillos, M. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu487 · Cited by 11

Building reference alignments for compound matching of multiple ontologies using OBO cross-products

Pesquita, C., Cheatham, M., Faria, D., Barros, J., dos Santos, E. M., & Couto, F. M. (2014) · International Conference on Ontology Matching · Cited by 10

Ontology-based cross-species integration and analysis of Saccharomyces cerevisiae phenotypes

Gkoutos, G. V., & Hoehndorf, R. (2012) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-3-s2-s6 · Cited by 10

A Novel Drug-Mouse Phenotypic Similarity Method Detects Molecular Determinants of Drug Effects

Prinz, J., Vogt, I., Adornetto, G., & Campillos, M. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005111 · Cited by 10

GOstruct 2.0

Kahanda, I., & Ben‐Hur, A. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3107417 · Cited by 11

An advanced strategy for integration of biological measurement data

Masuya, H., Gkoutos, G. V., Tanaka, N., Waki, K., Okuda, Y., Kushida, T., Kobayashi, N., Doi, K., Kozaki, K., & Hoehndorf, R. (2011) · University of Birmingham Research Portal (University of Birmingham) · Cited by 10

Using multiple ontologies to integrate complex biological data

Shimoyama, M. E., Petri, V., Pasko, D., Bromberg, S. K., Wu, W., Chen, J., Nenasheva, N., Kwitek, A. E., Twigger, S. N., & Jacob, H. J. (2005) · Comparative and Functional Genomics · doi:10.1002/cfg.498 · Cited by 10

The Rat Genome Database: Genetic, Genomic, and Phenotypic Data Across Multiple Species

Laulederkind, S. J. F., Hayman, G. T., Wang, S., Kaldunski, M. L., Vedi, M., Demos, W., Tutaj, M., Smith, J. R., Lamers, L., Gibson, A. C., et al. (2023) · Current Protocols · doi:10.1002/cpz1.804 · Cited by 9

Quantitative comparison of mapping methods between Human and Mammalian Phenotype Ontology

Oellrich, A., Gkoutos, G. V., Hoehndorf, R., & Rebholz‐Schuhmann, D. (2012) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-3-s2-s1 · Cited by 9

A Parametric Targetability Evaluation Approach for Vitiligo Proteome Extracted through Integration of Gene Ontologies and Protein Interaction Topologies

Malhotra, A. G., Singh, S., Jha, M., & Pandey, K. M. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2835459 · Cited by 9

The Transcriptomic Toolbox: Resources for Interpreting Large Gene Expression Data within a Precision Medicine Context for Metabolic Disease Atherosclerosis

de Evsikova, C. M., Raplee, I., Lockhart, J. H., Jaimes, G., & Evsikov, A. V. (2019) · Journal of Personalized Medicine · doi:10.3390/jpm9020021 · Cited by 10

CASIMIR: Coordination and Sustainability of International Mouse Informatics Resources

Hancock, J. M., Schofield, P. N., Chandras, C., Zouberakis, M., Aidinis, V., Smedley, D. P., Rosenthal, N. A., & Schughart, K. (2008) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2008.4696712 · Cited by 9

A Resource of Quantitative Functional Annotation forHomo sapiensGenes

Taşan, M., Drabkin, H., Beaver, J. E., Chua, H. N., Dunham, J., Tian, W., Blake, J. A., & Roth, F. P. (2012) · G3 Genes Genomes Genetics · doi:10.1534/g3.111.000828 · Cited by 8

Cross Species Integration of Functional Genomics Experiments

Jay, J. J. (2012) · International review of neurobiology · doi:10.1016/b978-0-12-398323-7.00001-x · Cited by 8

Assessing Bayesian Phylogenetic Information Content of Morphological Data Using Knowledge From Anatomy Ontologies

Porto, D. S., Dahdul, W., Lapp, H., Balhoff, J. P., Vision, T., Mabee, P., & Uyeda, J. C. (2022) · Systematic Biology · doi:10.1093/sysbio/syac022 · Cited by 8

A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms

Ono, T., & Kuhara, S. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-179 · Cited by 8

Contributions to an animal trait ontology1,2

Hulsegge, B., Smits, M. A., PAS, M. F. W. T., & Woelders, H. (2012) · Journal of Animal Science · doi:10.2527/jas.2011-4251 · Cited by 8

FAIR data representation in times of eScience: a comparison of instance-based and class-based semantic representations of empirical data using phenotype descriptions as example

Vogt, L. (2021) · Journal of Biomedical Semantics · doi:10.1186/s13326-021-00254-0 · Cited by 11

Natural language processing and visualization in the molecular imaging domain

Tulipano, P. K., Tao, Y., Millar, W. S., ZANZONICO, P. B., Kolbert, K. S., Xu, H., Yu, H. Q., Chen, L., Lussier, Y. A., & Friedman, C. (2006) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2006.08.002 · Cited by 8

Quantitative analysis of proteins which are members of the same protein complex but cause locus heterogeneity in disease

Gamba, A., Salmona, M., & Bazzoni, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-66836-7 · Cited by 8

Yeast Phenomics—Large-scale Mapping of the Genetic Basis for Organismal Traits

(2014) · CRC Press eBooks · doi:10.1201/b16437-11 · Cited by 8

An extended transcription factor regulatory network controls hepatocyte identity

Dubois‐Chevalier, J., Gheeraert, C., Berthier, A., Boulet, C., Dubois, V., Guille, L., Fourcot, M., Marot, G., Gauthier, K., Dubuquoy, L., et al. (2023) · EMBO Reports · doi:10.15252/embr.202357020 · Cited by 6

Expanding and refining the Mammalian Phenotype Ontology to enhance disease model discovery

Bello, S. M., Anagnostopoulos, A. V., Carmody, L., Matentzoglu, N., & Smith, C. L. (2025) · Disease Models & Mechanisms · doi:10.1242/dmm.052385 · Cited by 6

Large‐scale mouse knockouts and phenotypes

Ramírez‐Solis, R., Ryder, E. J., Houghton, R., White, J. K., & BOTTOMLEY, J. R. (2012) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1183 · Cited by 7

Performing Integrative Functional Genomics Analysis in GeneWeaver.org

Jay, J. J., & Chesler, E. J. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-721-1_2 · Cited by 6

Giant Island Mice Exhibit Widespread Gene Expression Changes in Key Metabolic Organs

Nolte, M. J., Jing, P., Dewey, C. N., & Payseur, B. A. (2020) · Genome Biology and Evolution · doi:10.1093/gbe/evaa118 · Cited by 6

Towards a Disease Ontology

Schofield, P. N., Rozell, B., & Gkoutos, G. V. (2007) · Computational biology · doi:10.1007/978-1-84628-885-2_5 · Cited by 6

Opportunities for Bioinformatics in the Classification of Behavior and Psychiatric Disorders

Chesler, E. J., & Logan, R. W. (2012) · International review of neurobiology · doi:10.1016/b978-0-12-398323-7.00008-2 · Cited by 6

Ontologies in Biology

Kelso, J. F., Hoehndorf, R., & Prüfer, K. (2010) · Springer eBooks · doi:10.1007/978-90-481-8847-5_15 · Cited by 6

Information-based methods for predicting gene function from systematic gene knock-downs

Weirauch, M. T., Wong, C. K., Byrne, A. B., & Stuart, J. M. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-463 · Cited by 6

Integrative database management for mouse development: Systems and concepts

Singh, A. V., Rouchka, E. C., Rempala, G. A., Bastian, C. D., & Knudsen, T. B. (2007) · Birth Defects Research Part C Embryo Today Reviews · doi:10.1002/bdrc.20089 · Cited by 6

Integration of Biological Data with Semantic Networks

Hsing, M., & Cherkasov, A. R. (2006) · Current Bioinformatics · doi:10.2174/157489306777827946 · Cited by 6

Automatically transforming pre- to post-composed phenotypes: EQ-lising HPO and MP

Oellrich, A., Grabmüller, C., & Rebholz‐Schuhmann, D. (2013) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-4-29 · Cited by 6

Exact Score Distribution Computation for Similarity Searches in Ontologies

Schulz, M. H., Köhler, S., Bauer, S., Vingron, M., & Robinson, P. N. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04241-6_25 · Cited by 6

Compound matching of biomedical ontologies.

Oliveira, D., & Pesquita, C. (2015) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 6

Biological and Medical Ontologies: Systems Biology Ontology (SBO)

Bernasconi, A., & Masseroli, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20399-3 · Cited by 7

Decomposing Phenotype Descriptions for the Human Skeletal Phenome

Groza, T., Hunter, J., & Zankl, A. (2013) · Biomedical Informatics Insights · doi:10.4137/bii.s10729 · Cited by 5

A biomedical ontology on genetic disease

Iqtidar, A., Muzaffar, A. W., Qamar, U., & Rehman, S. U. (2017) · International Conference on Internet of Things, Data and Cloud Computing (ICC) · doi:10.1145/3018896.3018966 · Cited by 5

Predicting disease‐associated genes: Computational methods, databases, and evaluations

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Bogue, M. A., Philip, V. M., Walton, D. O., Grubb, S. C., Dunn, M. H., Kolishovski, G., Emerson, J., Mukherjee, G., Stearns, T. M., He, H., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1032 · Cited by 80

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