Meta-analytic support vector machine for integrating multiple omics data

KEGG: Kyoto Encyclopedia of Genes and Genomes

Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077

KEGG: Kyoto Encyclopedia of Genes and Genomes

Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232

Gene selection for cancer classification using support vector machines

Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954

Gene expression profiling predicts clinical outcome of breast cancer

van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536

A Gene-Expression Signature as a Predictor of Survival in Breast Cancer

van de Vijver, M. J., He, Y. D., Veer, L. V. ‘., Dai, H. A., Hart, A. A. M., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., et al. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa021967 · Cited by 6,474

A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer

Paik, S., Shak, S., Tang, G., Kim, C., Baker, J. B., Cronin, M. T., Baehner, F. L., Walker, M. G., Watson, D., Park, T., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041588 · Cited by 6,321

Human Protein Reference Database--2009 update

Prasad, T. S. K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, R. B., Venugopal, A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn892 · Cited by 3,252

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

MAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126

Multiclass cancer diagnosis using tumor gene expression signatures

Ramaswamy, S., Tamayo, P., Rifkin, R. M., Mukherjee, S., Yeang, C., Angelo, M., Ladd, C., Reich, M. M., Latulippe, É., Mesirov, J. P., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.211566398 · Cited by 2,029

PID: the Pathway Interaction Database

Schaefer, C. F., Anthony, K., Krupa, S., Buchoff, J., Day, M., Hannay, T., & Buetow, K. H. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn653 · Cited by 1,603

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

Huang, S., Cai, N., PACHECO, P. P., Narrandes, S., Wang, Y., & Xu, W. W. (2018) · Cancer Genomics & Proteomics · doi:10.21873/cgp.20063 · Cited by 1,596

Using machine learning approaches for multi-omics data analysis: A review

Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903

Whole genome, transcriptome and methylome profiling enhances actionable target discovery in high-risk pediatric cancer

Wong, M., Mayoh, C., Lau, L. M. S., Khuong-Quang, D.-A., Pinese, M., Kumar, A., Barahona, P., Wilkie, E. E., Sullivan, P. A., Bowen-James, R., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1072-4 · Cited by 394

Machine learning and integrative analysis of biomedical big data

Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380

Genotype-phenotype associations in Fanconi anemia: A literature review

Fiesco-Roa, M. Ó., Giri, N., McReynolds, L. J., Best, A. F., & Alter, B. P. (2019) · Blood Reviews · doi:10.1016/j.blre.2019.100589 · Cited by 198

Undisclosed, unmet and neglected challenges in multi-omics studies

Tarazona, S., Arzalluz-Luque, Á., & Conesa, A. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00086-z · Cited by 185

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

Cembrowska-Lech, D., Krzemińska, A., Miller, T. I., Nowakowska, A., Adamski, C., Radaczyńska, M., Mikiciuk, G., & Mikiciuk, M. (2023) · Biology · doi:10.3390/biology12101298 · Cited by 193

Cardiac tissue engineering: state-of-the-art methods and outlook

Nguyen, A. H., Marsh, P., Schmiess-Heine, L., Burke, P. J., Lee, A. P., Lee, J., & Cao, H. (2019) · Journal of Biological Engineering · doi:10.1186/s13036-019-0185-0 · Cited by 145

Computational Techniques and Tools for Omics Data Analysis: State-of-the-Art, Challenges, and Future Directions

Kaur, P., Singh, A., & Chana, I. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09547-0 · Cited by 114

Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries

Zekavat, S. M., Ruotsalainen, S., Handsaker, R. E., Alver, M., Bloom, J., Poterba, T., Seed, C., Ernst, J., Chaffin, M., Engreitz, J., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04668-w · Cited by 98

Machine learning: its challenges and opportunities in plant system biology

Hesami, M., Alizadeh, M., Jones, A. M. P., & Torkamaneh, D. (2022) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-022-11963-6 · Cited by 91

A novel SNCA E83Q mutation in a case of dementia with Lewy bodies and atypical frontotemporal lobar degeneration

Kapasi, A., Brosch, J. R., Nudelman, K. N. H., Agrawal, S., Foroud, T., & Schneider, J. A. (2020) · Neuropathology · doi:10.1111/neup.12687 · Cited by 66

Clinical and genetic characterization of CACNA1A ‐related disease

Lipman, A. R., Fan, X., Shen, Y., & Chung, W. K. (2022) · Clinical Genetics · doi:10.1111/cge.14180 · Cited by 60

Analysis of Nanotoxicity with Integrated Omics and Mechanobiology

Shin, T. H., Nithiyanandam, S., Lee, D. Y., Kwon, D. H., Hwang, J. S., Kim, S. G., Jang, Y. E., Basith, S., Park, S., Mo, J., et al. (2021) · Nanomaterials · doi:10.3390/nano11092385 · Cited by 62

Artificial intelligence in the analysis of glycosylation data

Li, H., Chiang, A. W. T., & Lewis, N. E. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.108008 · Cited by 53

Genetic testing in severe aplastic anemia is required for optimal hematopoietic cell transplant outcomes

McReynolds, L. J., Rafati, M., Wang, Y., Ballew, B. J., Kim, J., Williams, V. V., Zhou, W., Hendricks, R. M., Dagnall, C. L., Freedman, N. D., et al. (2022) · Blood · doi:10.1182/blood.2022016508 · Cited by 52

Germline mutation of MDM4 , a major p53 regulator, in a familial syndrome of defective telomere maintenance

Toufektchan, E., Lejour, V., Durand, R., Giri, N., Drašković, I., Bardot, B., Laplante, P., Jaber, S., Alter, B. P., Londoño‐Vallejo, A., et al. (2020) · Science Advances · doi:10.1126/sciadv.aay3511 · Cited by 50

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies

Khorraminezhad, L., Leclercq, M., Droit, A., Bilodeau, J., & Rudkowska, I. (2020) · Nutrients · doi:10.3390/nu12103140 · Cited by 43

New clinical and molecular evidence linking mutations in ARSG to Usher syndrome type IV

Peter, V. G., Quinodoz, M., Sadio, S., Held, S., Rodrigues, M., Soares, M., Sousa, A. B., Santos, L. C., Daμμe, M., & Rivolta, C. (2020) · Human Mutation · doi:10.1002/humu.24150 · Cited by 40

Classic Thrombophilias and Thrombotic Risk Among Middle‐Aged and Older Adults: A Population‐Based Cohort Study

Manderstedt, E., Lind‐Halldén, C., Halldén, C., Elf, J. L., Svensson, P. J., Dahlbäck, B., Engström, G., Melander, O., Baras, A., Lotta, L. A., et al. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.121.023018 · Cited by 34

Genotype-cancer association in patients with Fanconi anemia due to pathogenic variants in FANCD1 (BRCA2) or FANCN (PALB2)

McReynolds, L. J., Biswas, K., Giri, N., Sharan, S. K., & Alter, B. P. (2021) · Cancer Genetics · doi:10.1016/j.cancergen.2021.10.001 · Cited by 33

Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine

Chahal, C. A. A., Alahdab, F., Asatryan, B., Addison, D., Aung, N., Chung, M. K., Denaxas, S., Dunn, J., Hall, J. L., Pamir, N., et al. (2025) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.124.004624 · Cited by 35

A decision tree to improve identification of pathogenic mutations in clinical practice

do Nascimento, P. M., Medeiros, I. G., Falcão, R. M., Stransky, B., & de Souza, J. E. S. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1060-0 · Cited by 32

Trends and patterns in cancer nanotechnology research: A survey of NCI's caNanoLab and nanotechnology characterization laboratory

Ke, W., Crist, R. M., Clogston, J. D., Stern, S. T., Dobrovolskaia, M. A., Grodzinski, P. A., & Jensen, M. A. (2022) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2022.114591 · Cited by 33

Plant-pathogen interactions: making the case for multi-omics analysis of complex pathosystems

Balotf, S., Wilson, R., Hemmati, R., Eshaghi, M., Wilson, C. R., & Mur, L. A. J. (2025) · Stress Biology · doi:10.1007/s44154-025-00260-7 · Cited by 34

Variants in CAPZA2, a member of an F-actin capping complex, cause intellectual disability and developmental delay

Huang, Y., Mao, X. O., van Jaarsveld, R. H., Shu, L., Terhal, P. A., Jia, Z., Xi, H., Peng, Y., Yan, H., Yuan, S., et al. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa078 · Cited by 30

Pathogenicity Reclassification of RPE65 Missense Variants Related to Leber Congenital Amaurosis and Early-Onset Retinal Dystrophy

Motta, F. L., Martin, R. P., Porto, F. B. O., Wohler, E., Resende, R. G., Gomes, C. P., Pesquero, J. B., & Sallum, J. M. F. (2019) · Genes · doi:10.3390/genes11010024 · Cited by 26

Cerebrospinal Fluid Metabolome in Parkinson’s Disease and Multiple System Atrophy

Kwon, D. H., Hwang, J. S., Kim, S. G., Jang, Y. E., Shin, T. H., & Lee, G. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23031879 · Cited by 29

Computational insights of K1444N substitution in GAP-related domain of NF1 gene associated with neurofibromatosis type 1 disease: a molecular modeling and dynamics approach

Agrahari, A. K., Muskan, M., Doss, C. G. P., Ramamoorthy, S., & Zayed, H. (2018) · Metabolic Brain Disease · doi:10.1007/s11011-018-0251-1 · Cited by 26

Patient-derived tumor organoids for personalized medicine in a patient with rare hepatocellular carcinoma with neuroendocrine differentiation: a case report

Meier, M., Nuciforo, S., Coto‐Llerena, M., Gallon, J., Matter, M. S., Ercan, C., Vosbeck, J., Terracciano, L., Soysal, S. D., Boll, D. T., et al. (2022) · Communications Medicine · doi:10.1038/s43856-022-00150-3 · Cited by 25

A general framework for identifying oligogenic combinations of rare variants in complex disordersPreprint

Pounraja, V. K., & Girirajan, S. (2022) · Genome Research · doi:10.1101/gr.276348.121 · Cited by 24

Spatial distribution of disease-associated variants in three-dimensional structures of protein complexes

Gress, A., Ramensky, V. E., & Kalinina, O. V. (2017) · Oncogenesis · doi:10.1038/oncsis.2017.79 · Cited by 22

New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches

Setty, S. T., Scott‐Boyer, M., Cuppens, T., & Droit, A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23126792 · Cited by 18

The fly homolog ofSUPT16H, a gene associated with neurodevelopmental disorders, is required in a cell-autonomous fashion for cell survival

Ma, M., Zhang, X., Zheng, Y., Lu, S., Pan, X., Mao, X. O., Pan, H., Chung, H.-L., Wang, H., Guo, H., et al. (2022) · Human Molecular Genetics · doi:10.1093/hmg/ddac259 · Cited by 17

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

Cervantes-Gracia, K., Chahwan, R., & Husi, H. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.828786 · Cited by 19

Pleiotropic modifiers of age-related diabetes and neonatal intestinal obstruction in cystic fibrosis

Aksit, M. A., Ling, H., Pace, R. G., Raraigh, K. S., Onchiri, F., Faino, A., Pagel, K. A., Pugh, E., Stilp, A. M., Sun, Q., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.09.004 · Cited by 19

Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes

Chen, Z., Guo, X., Tao, R., Huyghe, J. R., Law, P., Fernández–Rozadilla, C., Ping, J., Jia, G., Long, J., Li, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47399-x · Cited by 21

Haploinsufficiency underlies the neurodevelopmental consequences of SLC6A1 variants

Silva, D. B., Trinidad, M. I., Ljungdahl, A., Revalde, J. L., Berguig, G. Y., Wallace, W., Patrick, C. S., Bomba, L., Arkin, M. R., Dong, S., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.04.021 · Cited by 23

Screening and Functional Analysis of TEK Mutations in Chinese Children With Primary Congenital Glaucoma

Qiao, Y., Chen, Y., Tan, C., Sun, X., Chen, X., & Chen, J. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.764509 · Cited by 17

Integration of deep learning with Ramachandran plot molecular dynamics simulation for genetic variant classification

Tam, B., Qin, Z., Zhao, B., Wang, S. M., & Lei, C. L. (2023) · iScience · doi:10.1016/j.isci.2023.106122 · Cited by 16

Genomics of pediatric cardiomyopathy

Lee, T. M., Ware, S. M., Kamsheh, A. M., Bhatnagar, S., Absi, M. A., Miller, E., Purevjav, E., Ryan, K. A., Towbin, J. A., & Lipshultz, S. E. (2025) · Pediatric Research · doi:10.1038/s41390-025-03819-2 · Cited by 17

Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2, and HBB associated with haemoglobinopathies

Tamana, S., Xenophontos, M., Minaidou, A., Stephanou, C., Harteveld, C. L., Bento, C., Traeger‐Synodinos, J., Fylaktou, I., Yasin, N. M., Hamid, F. S. A., et al. (2022) · eLife · doi:10.7554/elife.79713 · Cited by 15

Experience with genomic sequencing in pediatric patients with congenital cardiac defects in a large community hospital

Hauser, N. S., Solomon, B. D., Vilboux, T., Khromykh, A., Baveja, R., & Bodian, D. L. (2018) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.357 · Cited by 14

A Bayesian framework for efficient and accurate variant prediction

Qian, D., Li, S., Tian, Y., Clifford, J., Sarver, B. A. J., Pesaran, T. F., Gau, C., Elliott, A. M., Lu, H., & Black, M. H. (2018) · PLoS ONE · doi:10.1371/journal.pone.0203553 · Cited by 15

Molecular genetic analysis of candidate genes for glutaric aciduria type II in a cohort of patients from Queensland, Australia

Demetriou, K., Nisbet, J., Coman, D. J., Ewing, A. D., Phillips, L. K., Smith, S., Lipke, M., Inwood, A., Spicer, J., Atthow, C., et al. (2024) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2024.108516 · Cited by 14

KIF5A and ALS2 Variants in a Family With Hereditary Spastic Paraplegia and Amyotrophic Lateral Sclerosis

Simone, M., Trabacca, A., Panzeri, E., Losito, L., Citterio, A., & Bassi, M. T. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.01078 · Cited by 14

ALSgeneScanner: a pipeline for the analysis and interpretation of DNA sequencing data of ALS patients

Iacoangeli, A., Khleifat, A. A., Sproviero, W., Shatunov, A., Jones, A. R., Opie-Martin, S., Naselli, E., Topp, S. D., Fogh, I., Hodges, A., et al. (2019) · Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration · doi:10.1080/21678421.2018.1562553 · Cited by 13

Chromatin regulators in the TBX1 network confer risk for conotruncal heart defects in 22q11.2DS

Zhao, Y., Wang, Y., Shi, L., McDonald‐McGinn, D. M., Crowley, T. B., McGinn, D. E., Tran, O., Miller, D., Lin, J.-R., Zackai, E. H., et al. (2023) · npj Genomic Medicine · doi:10.1038/s41525-023-00363-y · Cited by 14

Most Monogenic Disorders Are Caused by Mutations Altering Protein Folding Free Energy

Pandey, P., & Alexov, E. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25041963 · Cited by 15

Harvey Cushing Treated the First Known Patient With Carney Complex

Tsay, C. J., Stratakis, C. A., Faucz, F. R., London, E., Stathopoulou, C., Allgäuer, M., Quezado, M., Dagradi, T., Spencer, D. D., & Lodish, M. B. (2017) · Journal of the Endocrine Society · doi:10.1210/js.2017-00283 · Cited by 13

SUCLG1 mutations and mitochondrial encephalomyopathy: a case study and review of the literature

Ramsheh, S. M., Omidvar, M. E., Tabasinezhad, M., Alipoor, B., Salmani, T., & Ghaedi, H. (2020) · Molecular Biology Reports · doi:10.1007/s11033-020-05999-y · Cited by 11

Type IV Collagen Variants in CKD: Performance of Computational Predictions for Identifying Pathogenic Variants

Shulman, C., Liang, E., Kamura, M., Udwan, K., Yao, T., Cattran, D. C., Reich, H. N., Hladunewich, M., Pei, Y., Savige, J., et al. (2021) · Kidney Medicine · doi:10.1016/j.xkme.2020.12.007 · Cited by 12

Focused Strategies for Defining the Genetic Architecture of Congenital Heart Defects

Martin, L. J., & Benson, D. W. (2021) · Genes · doi:10.3390/genes12060827 · Cited by 13

The causes of Fanconi anemia in South Asia and the Middle East: A case series and review of the literature

Thompson, A. S., Saba, N., McReynolds, L. J., Munir, S., Ahmed, P., Sajjad, S., Jones, K. M., Yeager, M., Donovan, F. X., Chandrasekharappa, S. C., et al. (2021) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.1693 · Cited by 11

DrOGA: An Artificial Intelligence Solution for Driver-Status Prediction of Genomics Mutations in Precision Cancer Medicine

Bastico, M., García, A. F., Belmonte-Hernández, A., & Uribe, S. (2023) · IEEE Access · doi:10.1109/access.2023.3266983 · Cited by 12

Comprehensive Identification of Deleterious TP53 Missense VUS Variants Based on Their Impact on TP53 Structural Stability

Tam, B., Sinha, S., Qin, Z., & Wang, S. M. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222111345 · Cited by 11

Genetics and etiology of congenital heart disease

Narayan, P., Richter, F., & Morton, S. U. (2024) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/bs.ctdb.2024.01.009 · Cited by 10

Population Frequency of Fanconi Pathway Gene Variants and Their Association with Survival After Hematopoietic Cell Transplantation for Severe Aplastic Anemia

McReynolds, L. J., Wang, Y., Thompson, A. S., Ballew, B. J., Kim, J., Alter, B. P., Hicks, B. D., Zhu, B., Jones, K. M., Spellman, S. R., et al. (2020) · Transplantation and Cellular Therapy · doi:10.1016/j.bbmt.2020.01.011 · Cited by 10

Clinical, Biochemical, and Genetic Heterogeneity in Glutaric Aciduria Type II Patients

Ali, A., Almesmari, F. S. A., Dhahouri, N. A., Ali, A. M. S., Aldhanhani, M. A. A. M. A., Vijayan, R., Tenaiji, A. A., Shamsi, A. A., Hertecant, J., & Jasmi, F. A. (2021) · Genes · doi:10.3390/genes12091334 · Cited by 10

Identification of rare variants in cardiac sodium channel β4-subunit gene SCN4B associated with ventricular tachycardia

Yang, Q., Xiong, H., Xu, C., Huang, Y., Tu, X., Wu, G., Fu, F., Wang, Z., Wang, L., Zhao, Y., et al. (2019) · Molecular Genetics and Genomics · doi:10.1007/s00438-019-01567-7 · Cited by 10

Deep computational phenotyping of genomic variants impacting the SET domain of KMT2C reveal molecular mechanisms for their dysfunction

Jorge, S. D., Chi, Y., Mazaba, J. L., Haque, N., Wagenknecht, J. B., Smith, B. C., Volkman, B. F., Mathison, A., Lomberk, G. A., Zimmermann, M. T., et al. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1291307 · Cited by 10

A novel variant in the COX15 gene causing a fatal infantile cardioencephalomyopathy: A case report with clinical and molecular review

Oliveira, M. G., Tengan, C. H., Micheletti, C., de Macedo, P. R., Cernach, M. C. S. P., Cavole, T. R., Silva, M. D. F. B., Filho, J. S., Virmond, L. D. A., Milanezi, F., et al. (2021) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2021.104195 · Cited by 9

A novel frameshift mutation in TRPV6 is associated with hereditary pancreatitis

Shah, I. A., Prasad, H., Banerjee, S., Kurien, R. T., Chowdhury, S. D., & Visweswariah, S. S. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2022.1058057 · Cited by 9

Prediction of pathogenic single amino acid substitutions using molecular fragment descriptors

Zadorozhny, A. D., Smirnov, A. S., Filimonov, D. A., & Lagunin, A. A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad484 · Cited by 10

Segregation of two variants suggests the presence of autosomal dominant and recessive forms of WFS1-related disease within the same family: expanding the phenotypic spectrum of Wolfram Syndrome

Lusk, L. G., Black, E., & Vengoechea, J. E. (2019) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2018-105782 · Cited by 9

Comprehensive analysis of germline mutations in northern Brazil: a panel of 16 genes for hereditary cancer-predisposing syndrome investigation

Vidal, A. F., Ferraz, R. S., El-Husny, A., Silva, C. S., Vinasco‐Sandoval, T., de Magalhães, L. L., de Moraes, M. R., Barra, W. F., Pereira, C. L. B. L., de Assumpção, P. P., et al. (2021) · BMC Cancer · doi:10.1186/s12885-021-08089-9 · Cited by 9

Whole Exome Sequencing with Comprehensive Gene Set Analysis Identified a Biparental-Origin Homozygous c.509G>A Mutation in PPIB Gene Clustered in Two Taiwanese Families Exhibiting Fetal Skeletal Dysplasia during Prenatal Ultrasound

Chang, T., Chung, I., Wu, W.-J., Chang, S., Lin, W., Ginsberg, N. A., Ma, G., & Chen, M. (2020) · Diagnostics · doi:10.3390/diagnostics10050286 · Cited by 9

Genetic Causes of Oculocutaneous Albinism in Pakistani Population

Sajid, Z., Yousaf, S., Waryah, Y. M., Mughal, T. A., Kausar, T., Shahzad, M., Rao, A. R., Abbasi, A. A., Shaikh, R. S., Waryah, A. M., et al. (2021) · Genes · doi:10.3390/genes12040492 · Cited by 12

Low T cell diversity associates with poor outcome in bladder cancer: A comprehensive longitudinal analysis of the T cell receptor repertoire

Kjær, A., Kristjánsdóttir, N., Juul, R. I., Nordentoft, I., Birkenkamp‐Demtröder, K., Ahrenfeldt, J., Strandgaard, T., Radif, D., Hodgson, D., Abbosh, C., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102101 · Cited by 9

Finding a Needle in a Haystack: Variant Effect Predictor (VEP) Prioritizes Disease Causative Variants from Millions of Neutral Ones

Khimsuriya, Y., Vaniyawala, S., Banaganapalli, B., Khan, M., Elango, R., & Shaik, N. A. (2019) · Springer eBooks · doi:10.1007/978-3-030-18375-2_6 · Cited by 8

Integrative omics data mining

Kanchan, S., Kesheri, M., Srivastava, U., Karathia, H., Ratna-Raj, R., Chittoori, B. C. S., Bogomolnaya, L. M., Sinha, R. P., & Denvir, J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-16092-9.00015-1 · Cited by 8

Meta-Analysis Based on Nonconvex Regularization

Zhang, H., Li, S.-J., Zhang, H., Yang, Z., Ren, Y., Xia, L.-Y., & Liang, Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-62473-2 · Cited by 8

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Huang, R. S. P., Decker, B. J., Murugesan, K., Hiemenz, M., Mata, D. A., Li, G., Creeden, J., Ramkissoon, S. H., & Ross, J. S. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-002558 · Cited by 8

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Singh, A., Talseth‐Palmer, B. A., Xavier, A., Scott, R. J., Drabløs, F., & Sjursen, W. (2023) · BMC Medical Genomics · doi:10.1186/s12920-023-01562-3 · Cited by 7

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Tarnovskaya, S. I., Kostareva, A. A., & Zhorov, B. S. (2021) · Membranes · doi:10.3390/membranes11080599 · Cited by 7

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Niranjan, V., Uttarkar, A., Kaul, A., & Varghese, M. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2617-7_19 · Cited by 6

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Dmitriev, D. A., Shilov, B. V., Polunin, M. M., Zadorozhny, A. D., & Lagunin, A. A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242417240 · Cited by 6

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Tucci, F. A., Laurinavičius, A., Kather, J. N., & Eloy, C. (2024) · Tumori Journal · doi:10.1177/03008916241231035 · Cited by 6

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Ramaker, M. E., Abdulrahim, J. W., Corey, K., Ramaker, R. C., Kwee, L. C., Kraus, W. E., & Shah, S. H. (2024) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.123.004464 · Cited by 5

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Hettinga, K. A., & Zhang, L. (2018) · Springer eBooks · doi:10.1007/978-3-319-69682-9_22 · Cited by 6

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Rehmat, N., Farooq, H., Kumar, S. V., Hussain, S. U., & Naveed, H. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175781 · Cited by 6

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Kooshavar, D., Amor, D. J., Boggs, K., Baker, N. L., Barnett, C., Silva, M. D., Edwards, S., Fahey, M. C., Marum, J. E., Snell, P., et al. (2024) · Brain Communications · doi:10.1093/braincomms/fcae056 · Cited by 5

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Kamsheh, A. M., Ware, S. M., Bhatnagar, S., Martin, L. J., Lee, T. M., Towbin, J. A., Kantor, P. F., Lal, A. K., Bansal, N., Ballweg, J. A., et al. (2025) · Circulation Heart Failure · doi:10.1161/circheartfailure.125.013104 · Cited by 5

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Phogat, A., Krishnan, S. R., Pandey, M., & Gromiha, M. M. (2025) · Methods · doi:10.1016/j.ymeth.2025.01.020 · Cited by 5

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Yin, X., Shen, H., Wang, H., Wang, Q., Zhang, S., Zhang, C., Jia, Q., Guo, S., Xu, X., Zhang, W., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57520-3 · Cited by 5

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Khalil‐Mgharbel, A., Polena, H., Kleouforo, P. D., Sohag, M. M. H., Alcaraz, J., Martin, D. K., & Vilgrain, I. (2018) · Biotechnology Journal · doi:10.1002/biot.201800463 · Cited by 5

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Othman, H., Jemimah, S., & da Rocha, J. B. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020263 · Cited by 3

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Rajadhyaksha, N., & Chitkara, A. (2018) · International Journal of Bioinformatics and Biological Science · doi:10.30954/2319-5169 · Cited by 5

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Hu, Z., Zhou, Y., & Tong, T. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.656826 · Cited by 4

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Pace, N. P., Vella, B., Craus, J., Caruana, R., Savona‐Ventura, C., & Vassallo, J. (2021) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3486 · Cited by 4

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Qiao, Y., Shao, T., Chen, Y., Chen, J., Sun, X., & Chen, X. (2022) · Ophthalmic Genetics · doi:10.1080/13816810.2022.2109683 · Cited by 4

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Xie, Y., Li, M., Dong, W., Jiang, W., & Zhao, H. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009849 · Cited by 4

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Zhu, H., Zhang, J., Rao, S., Durbin, M. D., Li, Y., Lang, R., Liu, J., Xiao, B., Shan, H., Meng, Z., et al. (2025) · Genome Research · doi:10.1101/gr.280047.124 · Cited by 4

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Uguen, K., Bergot, T., Scott‐Boyer, M., Chapalain, S., Desdouets, C., Commet, S., Zhu, C., Xu, Y., Wang, Y., Roscioli, T., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68284-9 · Cited by 3

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Mohsen, H., Blenman, K., Emani, P. S., Morris, Q., Carrot‐Zhang, J., & Pusztai, L. (2025) · BMC Medical Genomics · doi:10.1186/s12920-025-02154-z · Cited by 3

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Zimbru, C. G., Andreescu, N. I., Albu, A., Chiriță-Emandi, A., Stanciu, A., & Puiu, M. (2019) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb47216.2019.8969963 · Cited by 3

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Shah, I. A., Rashid, R., Bhat, A., Rashid, H., Bashir, R., Asrar, M. M., Wani, I. A., Charoo, B. A., Radha, V., Mohan, V., et al. (2023) · Gene · doi:10.1016/j.gene.2023.147576 · Cited by 3

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Kim, S., Jhong, J.-H., Lee, J., & Koo, J.-Y. (2017) · Figshare · doi:10.6084/m9.figshare.c.3675877_d1

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Kim, S., Huo, Z., Park, Y., & Tseng, G. C. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781107706484.003

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Tembhare, K., Sharma, T., Kasibhatla, S. M., Achalere, A., & Joshi, R. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101507 · Cited by 10

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Kim, S., Kang, D., Huo, Z., Park, Y., & Tseng, G. C. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx765 · Cited by 79

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