Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450

Biopython: freely available python tools for computational molecular biology and bioinformatics

Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080

Predicting splicing from primary sequence with deep learning

Jaganathan, K., Kyriazopoulou Panagiotopoulou, S., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., Kosmicki, J. A., Arbelaez, J., Cui, W., Schwartz, G. B., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.015 · Cited by 2,853

Identifying facial phenotypes of genetic disorders using deep learning

Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., Basel-Salmon, L., Krawitz, P. M., Kamphausen, S. B., Zenker, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0279-0 · Cited by 739

Identifying Mendelian disease genes with the Variant Effect Scoring Tool

Carter, H. K., Douville, C. B., Stenson, P. D., Cooper, D. N., & Karchin, R. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-s3-s3 · Cited by 593

Predicting the clinical impact of human mutation with deep neural networks

Sundaram, L., Gao, H., Padigepati, S. R., McRae, J. F., Li, Y., Kosmicki, J. A., Fritzilas, N., Hakenberg, J., Dutta, A., Shon, J. C., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0167-z · Cited by 505

Typical and atypical pathology in primary progressive aphasia variants

Spinelli, E. G., Mandelli, M. L., Miller, Z. A., Santos‐Santos, M. A., Wilson, S. M., Agosta, F., Grinberg, L. T., Huang, E. J., Trojanowski, J. Q., Meyer, M., et al. (2017) · Annals of Neurology · doi:10.1002/ana.24885 · Cited by 409

AI in Health: State of the Art, Challenges, and Future Directions

Wang, F., & Preininger, A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677908 · Cited by 398

Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization

Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D., & Pfeiffer, D. (2019) · Scientific Reports · doi:10.1038/s41598-019-42557-4 · Cited by 372

ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants

Alirezaie, N., Kernohan, K. D., Hartley, T., Majewski, J., & Hocking, T. D. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.08.005 · Cited by 271

Congenital disorders of glycosylation (CDG): Quo vadis?

Péanne, R., de Lonlay, P., Foulquier, F., Kornak, U., Lefeber, D. J., Morava, É., Pérez, B., Séta, N., Thiel, C., SCHAFTINGEN, E. V., et al. (2017) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2017.10.012 · Cited by 265

Prediction of glycosylation sites using random forests

Hamby, S. E., & Hirst, J. D. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-500 · Cited by 236

Perspectives on Glycosylation and Its Congenital Disorders

Ng, B. G., & Freeze, H. H. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.03.002 · Cited by 232

The Application of Unsupervised Clustering Methods to Alzheimer’s Disease

Alashwal, H., Halaby, M. E., Crouse, J. J., ABDALLA, A. S., & Moustafa, A. A. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00031 · Cited by 220

Neurology of inherited glycosylation disorders

Freeze, H. H., Eklund, E. A., Ng, B. G., & Patterson, M. C. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70040-6 · Cited by 210

The Challenge of Rare Diseases

Stoller, J. K. (2018) · CHEST Journal · doi:10.1016/j.chest.2017.12.018 · Cited by 198

Genome-wide association identifies diverse causes of common variable immunodeficiency

Orange, J. S., Glessner, J., Resnick, E. S., Sullivan, K. E., Lucas, M., Ferry, B. L., Kim, C. E., Hou, C., Wang, F., Chiavacci, R. M., et al. (2011) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2011.02.039 · Cited by 195

A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse

Garcelon, N., Neuraz, A., Salomon, R., Faour, H., Benoît, V., Delapalme, A., Münnich, A., Burgun, A., & Rance, B. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.02.019 · Cited by 182

Glycosylation site prediction using ensembles of Support Vector Machine classifiers

Caragea, C., Sinapov, J., Silvescu, A., Dobbs, D. L., & Honavar, V. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-438 · Cited by 171

What is new in CDG?

Jaeken, J., & Péanne, R. (2017) · Journal of Inherited Metabolic Disease · doi:10.1007/s10545-017-0050-6 · Cited by 170

From scientific discovery to treatments for rare diseases – the view from the National Center for Advancing Translational Sciences – Office of Rare Diseases Research

Kaufmann, P., Pariser, A., & Austin, C. P. (2018) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-018-0936-x · Cited by 169

Looking beyond the hype: applied AI and machine learning in translational medicine

Toh, T. S., Dondelinger, F., & Wang, D. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.027 · Cited by 159

Phen-Gen: combining phenotype and genotype to analyze rare disorders

Javed, A., Agrawal, S., & Ng, P. C. (2014) · Nature Methods · doi:10.1038/nmeth.3046 · Cited by 146

Estimation of Survival Distributions of Treatment Policies in Two‐Stage Randomization Designs in Clinical Trials

Lunceford, J. K., Davidian, M., & Tsiatis, A. A. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00048.x · Cited by 145

Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study

Ronicke, S., Hirsch, M. C., Türk, E., Larionov, K., Tientcheu, D., & Wagner, A. D. (2019) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-019-1040-6 · Cited by 134

Recommendations for the design of small population clinical trials

Day, S. J., Jonker, A. H., Lau, L. P. L., Hilgers, R.-D., Irony, I., Larsson, K., Roes, K. C. B., & Stallard, N. (2018) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-018-0931-2 · Cited by 133

The challenge of CDG diagnosis

Francisco, R., Marques‐da‐Silva, D., Brasil, S., Pascoal, C., Ferreira, V. D. R., Morava, É., & Jaeken, J. (2018) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2018.11.003 · Cited by 132

Challenges in Research and Health Technology Assessment of Rare Disease Technologies: Report of the ISPOR Rare Disease Special Interest Group

Nestler-Parr, S., Korchagina, D., Toumi, M., Pashos, C. L., Blanchette, C., Molsen, E., Morel, T., Simoens, S., Kaló, Z., Gatermann, R., et al. (2018) · Value in Health · doi:10.1016/j.jval.2018.03.004 · Cited by 131

MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease

Taroni, J., Grayson, P. C., Hu, Q., Eddy, S. F., Kretzler, M., Merkel, P. A., & Greene, C. S. (2019) · Cell Systems · doi:10.1016/j.cels.2019.04.003 · Cited by 119

Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas

van IJzendoorn, D. G. P., Szuhai, K., Bruijn, I. H. B., Kostine, M., Kuijjer, M. L., & Bovée, J. V. M. G. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006826 · Cited by 111

Maintaining order: COG complex controls Golgi trafficking, processing, and sorting

Blackburn, J. B., D'Souza, Z. C., & Lupashin, V. V. (2019) · FEBS Letters · doi:10.1002/1873-3468.13570 · Cited by 110

Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis

Li, Q., Zhao, K., Bustamante, C. D., Ma, X., & Wong, W. H. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0439-8 · Cited by 106

Predicting disease-causing variant combinations

Papadimitriou, S., Gazzo, A. M., Versbraegen, N., Nachtegael, C., Aerts, J. A., Moreau, Y., Dooren, S. V., Nowé, A., Smits, G., & Lenaerts, T. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1815601116 · Cited by 105

CDG Therapies: From Bench to Bedside

Brasil, S., Pascoal, C., Francisco, R., Marques‐da‐Silva, D., Andreotti, G., Videira, P. A., Morava, É., Jaeken, J., & Ferreira, V. D. R. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19051304 · Cited by 104

Analysis of the human diseasome using phenotype similarity between common, genetic and infectious diseases

Hoehndorf, R., Schofield, P. N., & Gkoutos, G. V. (2015) · Scientific Reports · doi:10.1038/srep10888 · Cited by 102

Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition

Xu, K., Yang, Z., Kang, P., Wang, Q., & Liu, W. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.04.002 · Cited by 101

Identification of deleterious synonymous variants in human genomes

Buske, O. J., Manickaraj, A. K., Mital, S. R., Ray, P. N., & Brudno, M. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt308 · Cited by 98

The International Rare Diseases Research Consortium: Policies and Guidelines to maximize impact

Lochmüller, H., Torrent-Farnell, J., Cam, Y. L., Jonker, A. H., Lau, L. P. L., Baynam, G. S., Kaufmann, P., Dawkins, H. J. S., Lasko, P. F., Austin, C. P., et al. (2017) · European Journal of Human Genetics · doi:10.1038/s41431-017-0008-z · Cited by 93

Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays

Smith, K., Piccinini, F., Balassa, T., Koós, K., Danka, T., Azizpour, H., & Horváth, P. (2018) · Cell Systems · doi:10.1016/j.cels.2018.06.001 · Cited by 91

Recognition of the Cornelia de Lange syndrome phenotype with facial dysmorphology novel analysis

Basel‐Vanagaite, L., Wolf, L., Orin, M. J., Larizza, L., Gervasini, C., Krantz, I. D., & Deardoff, M. A. (2015) · Clinical Genetics · doi:10.1111/cge.12716 · Cited by 89

PEDIA: prioritization of exome data by image analysis

Hsieh, T., Mensah, M. A., Pantel, J. T., Aguilar, D., Bar, O., Bayat, A., Becerra-Solano, L. E., Bentzen, H. B., Biskup, S., Borisov, O., et al. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0566-2 · Cited by 82

DeepPVP: phenotype-based prioritization of causative variants using deep learning

Boudellioua, I., Kulmanov, M., Schofield, P. N., Gkoutos, G. V., & Hoehndorf, R. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2633-8 · Cited by 79

A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression

Blasco, H., Patin, F., Descat, A., Garçon, G., Corcia, P., Gelé, P., Lenglet, T., Bede, P., Meininger, V., Devos, D., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198116 · Cited by 74

Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning

Romero, R., Ramanathan, A., Yuen, T., Bhowmik, D., Mathew, M., Munshi, L. B., Javaid, S., Bloch, M., Lizneva, D., Rahimova, A., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1818411116 · Cited by 74

Natural history and genotype‐phenotype correlations in 72 individuals with SATB2 ‐associated syndrome

Zarate, Y. A., Smith‐Hicks, C. L., Greene, C., Abbott, M., Siu, V. M., Calhoun, A. R. U. L., Pandya, A., Li, C., Sellars, E. A., Kaylor, J., et al. (2018) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.38630 · Cited by 71

Involvement of Patient Organisations in Research and Development of Orphan Drugs for Rare Diseases in Europe

Mavris, M., & Cam, Y. L. (2012) · Molecular Syndromology · doi:10.1159/000342758 · Cited by 66

Stacked ensemble combined with fuzzy matching for biomedical named entity recognition of diseases

Bhasuran, B., Murugesan, G., Abdulkadhar, S., & Natarajan, J. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.09.009 · Cited by 63

Finding patients using similarity measures in a rare diseases-oriented clinical data warehouse: Dr. Warehouse and the needle in the needle stack

Garcelon, N., Neuraz, A., Benoît, V., Salomon, R., Kracker, S., Suárez, F., Bahi‐Buisson, N., Hadj-Rabia, S., Fischer, A., Münnich, A., et al. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.07.016 · Cited by 63

Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks

Rao, A., Saipradeep, V. G., Joseph, T., Kotte, S., Sivadasan, N., & Srinivasan, R. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0372-8 · Cited by 57

Stroke-Like Episodes and Cerebellar Syndrome in Phosphomannomutase Deficiency (PMM2-CDG): Evidence for Hypoglycosylation-Driven Channelopathy

Izquierdo-Serra, M., Martínez‐Monseny, A. F., López, L., Carrillo-García, J., Edo, A., Ortigoza‐Escobar, J. D., García, Ó., Candela, R. C., Carrasco-Marina, M. L., Gutiérrez-Solana, L., et al. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19020619 · Cited by 56

DNA methylation profiling reliably distinguishes pulmonary enteric adenocarcinoma from metastatic colorectal cancer

Jurmeister, P. S., Schöler, A., Arnold, A., Klauschen, F., Lenze, D., Hummel, M., Schweizer, L., Bläker, H., Pfitzner, B. M., Mamlouk, S., et al. (2019) · Modern Pathology · doi:10.1038/s41379-019-0207-y · Cited by 56

RDAD: A Machine Learning System to Support Phenotype-Based Rare Disease Diagnosis

Jia, J., Wang, R., An, Z., Guo, Y., Ni, X., & Shi, T. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00587 · Cited by 55

Congenital Disorders of Glycosylation: Glycosylation Defects in Man and Biological Models for Their Study

Marquardt, T., & Freeze, H. H. (2001) · Biological Chemistry · doi:10.1515/bc.2001.024 · Cited by 54

isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection

Rahman, M. S., Rahman, M. K., Kaykobad, M., & Rahman, M. S. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.11.003 · Cited by 53

CDG and immune response: From bedside to bench and back

Pascoal, C., Francisco, R., Ferro, T., Ferreira, V. D. R., Jaeken, J., & Videira, P. A. (2019) · Journal of Inherited Metabolic Disease · doi:10.1002/jimd.12126 · Cited by 52

A checklist for clinical trials in rare disease: obstacles and anticipatory actions—lessons learned from the FOR-DMD trial

Crow, R. A., Morehart, P. J., McDermott, M. P., Tawil, R., Martens, W. B., Herr, B. E., McColl, E., Wilkinson, J. S., Kirschner, J., King, W. M., et al. (2018) · Trials · doi:10.1186/s13063-018-2645-0 · Cited by 51

Utilization of Electronic Medical Records and Biomedical Literature to Support the Diagnosis of Rare Diseases Using Data Fusion and Collaborative Filtering Approaches

Shen, F., Liu, S., Wang, Y., Wen, A., Wang, L., & Liu, H. (2018) · JMIR Medical Informatics · doi:10.2196/11301 · Cited by 50

The Discovery of a LEMD2-Associated Nuclear Envelopathy with Early Progeroid Appearance Suggests Advanced Applications for AI-Driven Facial Phenotyping

Marbach, F., Rustad, C. F., Rieß, A., Đukić, D., Hsieh, T., Jobani, I., Prescott, T., Bevot, A., Erger, F., Houge, G., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.02.021 · Cited by 50

Next generation phenotyping in Emanuel and Pallister‐Killian syndrome using computer‐aided facial dysmorphology analysis of 2D photos

Liehr, T., Acquarola, N., Pyle, K., St‐Pierre, S., Rinholm, M., Bar, O., Wilhelm, K., & Schreyer, I. (2017) · Clinical Genetics · doi:10.1111/cge.13087 · Cited by 48

In silico clinical trials for pediatric orphan diseases

Carlier, A., Vasilevich, A. S., Maréchal, M., de Boer, J., & Geris, L. (2018) · Scientific Reports · doi:10.1038/s41598-018-20737-y · Cited by 45

Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer

Sernadela, P., González-Castro, L., Carta, C., van der Horst, E., Lopes, P. H. D. M. B., Kaliyaperumal, R., Thompson, M., Thompson, R., Queralt-Rosiñach, N., Martín, E. L., et al. (2017) · BioMed Research International · doi:10.1155/2017/8327980 · Cited by 45

Towards automated clinical coding

Catling, F. J. R., Spithourakis, G., & Riedel, S. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.09.021 · Cited by 42

Personalized Medicine: What’s in it for Rare Diseases?

Halfmann, S. S. G., Mählmann, L., Leyens, L., Reumann, M., & Brand, A. M. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-67144-4_22 · Cited by 40

The Analysis of Variants in the General Population Reveals That PMM2 Is Extremely Tolerant to Missense Mutations and That Diagnosis of PMM2-CDG Can Benefit from the Identification of Modifiers

Citro, V., Cimmaruta, C., Monticelli, M., Riccio, G., Mele, B. H., Cubellis, M. V., & Andreotti, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082218 · Cited by 40

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis

Subirats, L., Reguera, N., Hernández, A. M. B., Gómez‐Zúñiga, B., Minguillón, J., & Armayones, M. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15091877 · Cited by 37

More than just sugars: Conserved oligomeric Golgi complex deficiency causes glycosylation‐independent cellular defects

Blackburn, J. B., Kudlyk, T. A., Pokrovskaya, I. D., & Lupashin, V. V. (2018) · Traffic · doi:10.1111/tra.12564 · Cited by 37

Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone

Portnoi, T., Yala, A., Schuster, T., Barzilay, R., Dontchos, B. N., Lamb, L. R., & Lehman, C. D. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20813 · Cited by 37

Validation of a new multiple osteochondromas classification through Switching Neural Networks

Mordenti, M., Ferrari, E., Pedrini, E., Fabbri, N., Campanacci, L., Muselli, M., & Sangiorgi, L. (2013) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.35819 · Cited by 37

A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry

Geva, A., Gronsbell, J. L., Cai, T., Cai, T. A., Murphy, S. N., Lyons, J. C., Heinz, M. M., Natter, M. D., Patibandla, N., Bickel, J. P., et al. (2017) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2017.05.037 · Cited by 36

DeepNEU: cellular reprogramming comes of age – a machine learning platform with application to rare diseases research

Danter, W. R. (2019) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-018-0983-3 · Cited by 35

The Application of Biomarkers in Clinical Trials for Motor Neuron Disease

Ganesalingam, J., & Bowser, R. P. (2010) · Biomarkers in Medicine · doi:10.2217/bmm.09.71 · Cited by 33

Rare Disease Registries Classification and Characterization: A Data Mining Approach

Santoro, M. C., Coi, A., Paola, M. L. D., Bianucci, A. M., Gainotti, S., Mollo, E., Taruscio, D., Vittozzi, L., & Bianchi, F. (2015) · Public Health Genomics · doi:10.1159/000369993 · Cited by 32

Characteristics of clinical trials in rare vs. common diseases: A register-based Latvian study

Logviss, K., Krievins, D. K., & Purvina, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0194494 · Cited by 31

Mucopolysaccharidosis type II detection by Naïve Bayes Classifier: An example of patient classification for a rare disease using electronic medical records from the Canadian Primary Care Sentinel Surveillance Network

Ehsani‐Moghaddam, B., Queenan, J. A., MacKenzie, J. J., & Birtwhistle, R. (2018) · PLoS ONE · doi:10.1371/journal.pone.0209018 · Cited by 30

A Computational Framework for Genome-wide Characterization of the Human Disease Landscape

Lee, Y.-S., Krishnan, A., Oughtred, R., Rust, J., Chang, C., Ryu, J., Kristensen, V. N., Dolinski, K. J., Theesfeld, C. L., & Troyanskaya, O. G. (2019) · Cell Systems · doi:10.1016/j.cels.2018.12.010 · Cited by 28

Detecting Acromegaly: Screening for Disease with a Morphable Model

Miller, E. G., Lu, Q., Paisley, A. N., Trainer, P. J., Blanz, V., Dedden, K., & Miller, R. E. (2006) · Lecture notes in computer science · doi:10.1007/11866763_61 · Cited by 28

Industrializing rare disease therapy discovery and development

Ekins, S. (2017) · Nature Biotechnology · doi:10.1038/nbt.3787 · Cited by 28

Design of Biomedical Robots for Phenotype Prediction Problems

deAndrés‐Galiana, E. J., Fernández‐Martínez, J. L., & Sonis, S. T. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2016.0008 · Cited by 27

Adaptive control of epileptiform excitability in an in vitro model of limbic seizures

Panuccio, G., Guez, A., Vincent, R., Avoli, M., & Pineau, J. (2013) · Experimental Neurology · doi:10.1016/j.expneurol.2013.01.002 · Cited by 27

HHT diagnosis by Mid-infrared spectroscopy and artificial neural network analysis

Lux, A., Müller, R., Tulk, M., Olivieri, C., Zarrabeitia, R., Salonikios, T., & Wirnitzer, B. (2013) · Orphanet Journal of Rare Diseases · doi:10.1186/1750-1172-8-94 · Cited by 26

Rare disease knowledge enrichment through a data-driven approach

Shen, F., Zhao, Y., Wang, L., Rastegar-Mojarad, M., Wang, Y., Liu, S., & Liu, H. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0752-9 · Cited by 26

From gestalt to gene: early predictive dysmorphic features of PMM2-CDG

Martínez‐Monseny, A. F., Cuadras, D., Bolasell, M., Muchart, J., Arjona, C., Borregán, M., Algrabli, A., Montero, R., Artuch, R., Velázquez-Fragua, R., et al. (2018) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2018-105588 · Cited by 25

eDiVA—Classification and prioritization of pathogenic variants for clinical diagnostics

Bosio, M., Drechsel, O., Rahman, R., Muyas, F., Rabionet, R., Bezdan, D., Domènech, L., Hor, H., Schott, J., Munell, F., et al. (2019) · Human Mutation · doi:10.1002/humu.23772 · Cited by 24

In Silico Prediction of the Effects of Mutations in the Human Mevalonate Kinase Gene: Towards a Predictive Framework for Mevalonate Kinase Deficiency

Browne, C., & Timson, D. J. (2015) · Annals of Human Genetics · doi:10.1111/ahg.12126 · Cited by 23

Deep learning approach for survival prediction for patients with synovial sarcoma

Han, I., Kim, J. H., Park, H., Kim, H., & Seo, S. W. (2018) · Tumor Biology · doi:10.1177/1010428318799264 · Cited by 22

Sensitivity analysis of gene ranking methods in phenotype prediction

deAndrés‐Galiana, E. J., Fernández‐Martínez, J. L., & Sonis, S. T. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.10.012 · Cited by 22

Constructing a database for the relations between CNV and human genetic diseases via systematic text mining

Yang, X., Song, Z., Wu, C., Wang, W., Li, G., Zhang, W., Wu, L., & Lü, K. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2526-2 · Cited by 21

Mining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen

Mears, A. J., Schock, S., Hadwen, J., Putos, S., Dyment, D. A., Boycott, K. M., & MacKenzie, A. (2017) · npj Genomic Medicine · doi:10.1038/s41525-017-0018-3 · Cited by 20

Orphan drug development: the increasing role of clinical pharmacology

AHMED, M. A., Okour, M., Brundage, R. C., & Kartha, R. V. (2019) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-019-09646-3 · Cited by 20

Detecting Acromegaly: Screening for Disease with a Morphable Model

Miller, E. G., Lu, Q., Paisley, A. N., Trainer, P. J., Blanz, V., Dedden, K., Miller, R. E., Larsen, R., Nielsen, M., & Jon Sporring, A. (2006) · Cited by 17

Inferring characteristic phenotypes via class association rule mining in the bone dysplasia domain

Paul, R., Groza, T., Hunter, J., & Zankl, A. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.12.001 · Cited by 14

Improving Young Stroke Prediction by Learning with Active Data Augmenter in a Large-Scale Electronic Medical Claims Database

Hung, C.-Y., Lin, C., & Lee, C.-C. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513479 · Cited by 13

Patient’s Experience in Pediatric Primary Immunodeficiency Disorders: Computerized Classification of Questionnaires

Mücke, U., Klemann, C., Baumann, U., Meyer‐Bahlburg, A., Kortum, X., Klawonn, F., Lechner, W. M., & Grigull, L. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.00384 · Cited by 11

CSAX: Characterizing Systematic Anomalies in eXpression Data

Noto, K., Majidi, S., Edlow, A. G., Wick, H. C., Bianchi, D. W., & Slonim, D. K. (2015) · Journal of Computational Biology · doi:10.1089/cmb.2014.0155 · Cited by 10

Impact of structural prior knowledge in SNV prediction: Towards causal variant finding in rare disease

Dehiya, V., Thomas, J., & Sael, L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204101 · Cited by 6

Application of density estimation algorithms in analyzing co-morbidities of migraine

Yang, M.-H., Yang, F., & Oyang, Y. (2013) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-013-0028-8 · Cited by 5

Important Considerations in the Initial Clinical Evaluation of the Dysmorphic Neonate

Smpokou, P., Lanpher, B. C., & Rosenbaum, K. N. (2015) · Advances in Neonatal Care · doi:10.1097/anc.0000000000000216 · Cited by 2

Cerebrospinal fluid metabolomics in West Syndrome: central role of the serine metabolic pathway

Lagrue, E., Hounoum, B. M., Rullier, C., Andres, C. R., Emond, P., Bocca, C., Castelnau, P., & Blasco, H. (2018) · Journal of Translational Science · doi:10.15761/jts.1000223 · Cited by 1

Congenital Adrenal Hyperplasia—Current Insights in Pathophysiology, Diagnostics, and Management

Claahsen - van der Grinten, H. L., Speiser, P. W., Ahmed, S. F., Arlt, W., Auchus, R. J., Falhammar, H., Flück, C. E., Guasti, L., Huebner, A., Kortmann, B. B. M., et al. (2022) · Endocrine Reviews · doi:10.1210/endrev/bnab016 · Cited by 556

Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development

Mannuru, N. R., Shahriar, S., Teel, Z. A., Wang, T., Lund, B. D., Tijani, S., Pohboon, C. O., Agbaji, D. A., Alhassan, J., Galley, J., et al. (2023) · Information Development · doi:10.1177/02666669231200628 · Cited by 426

Drug Repurposing for Rare Diseases

Roessler, H. I., Knoers, N. V. A. M., van Haelst, M. M., & van Haaften, G. W. (2021) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2021.01.003 · Cited by 290

The use of machine learning in rare diseases: a scoping review

Schaefer, J., Lehne, M., Schepers, J., Prasser, F., & Thun, S. (2020) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-020-01424-6 · Cited by 213

Artificial intelligence in rare disease diagnosis and treatment

Wojtara, M. S., Rana, E., Rahman, T., Khanna, P., & Singh, H. (2023) · Clinical and Translational Science · doi:10.1111/cts.13619 · Cited by 174

Artificial intelligence in rare diseases: toward clinical impact

Amorim, A. M. B., Orzeł, U., Caniceiro, A. B., Rosário‐Ferreira, N., & Moreira, I. S. (2025) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2025.10.010 · Cited by 14

Navigating the uncharted territory of rare diseases: leveraging artificial intelligence to achieve groundbreaking advances, with a focus on Congenital Disorders of Glycosylation (CDG): A comprehensive analysis

Choubisa, V., & Soni, K. (2024) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2024.12.2.1255

Artificial Intelligence Supports Research Progress in the Diagnosis and Treatment of Rare Diseases

Mengchun, G., Yuanshi, J., Wuren, M., Peng, L., Ye, J., Jifa, H., Ling, N., Wenzhao, S., & Shuyang, Z. (2022) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.12376/j.issn.2097-0501.2022.02.003

Association of Rare Disease Diagnosis and Its Treatment Using Artificial Intelligence – A Review

Kamboj, A., Singh, A. K., Kumar, S., & Garg, V. K. (2025) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/aisummit66170.2025.11411235

Unifying the odyssey: artificial intelligence for rare disease diagnosis and therapy

Ho, M., Žitnik, M., Azachi, R., Basu, S., Rajpurkar, P., & Sidlow, R. (2026) · Health and Technology · doi:10.1007/s12553-026-01057-y · Cited by 4