Publications

Peer-reviewed publications at QGG

Ning, M., Zhao, Y., Dai, D., Yao, C., Liu, H., Fang, L., Wang, B., Zhang, Y. & Cao, J. (2023). Gene co-expression network and differential expression analyses of subcutaneous white adipose tissue reveal novel insights into the pathological mechanisms underlying ketosis in dairy cows. Journal of Dairy Science, 106(7), 5018-5028. https://doi.org/10.3168/jds.2022-22941

Xiang, R., Fang, L., Liu, S., Macleod, I. M., Liu, Z., Breen, E. J., Gao, Y., Liu, G. E., Tenesa, A., CattleGTEx Consortium, Mason, B. A., Chamberlain, A. J., Wray, N. R. & Goddard, M. E. (2023). Gene expression and RNA splicing explain large proportions of the heritability for complex traits in cattle. Cell Genomics, 3(10), Article 100385. https://doi.org/10.1016/j.xgen.2023.100385

Raffo, M. A. & Jensen, J. (2023). Gene × gene and genotype × environment interactions in wheat. Crop Science, 63(4), 1779-1793. https://doi.org/10.1002/csc2.20986

Skovbjerg, C. K., Angra, D., Robertson-Shersby-Harvie, T., Kreplak, J., Keeble-Gagnère, G., Kaur, S., Ecke, W., Windhorst, A., Nielsen, L. K., Schiemann, A., Knudsen, J., Gutierrez, N., Tagkouli, V., Fechete, L. I., Janss, L., Stougaard, J., Warsame, A., Alves, S., Khazaei, H. ... Andersen, S. U. (2023). Genetic analysis of global faba bean diversity, agronomic traits and selection signatures. Theoretical and Applied Genetics, 136(5), Article 114. https://doi.org/10.1007/s00122-023-04360-8

Bornhofen, E., Fé, D., Nagy, I., Lenk, I., Greve, M., Didion, T., Jensen, C. S., Asp, T. & Janss, L. (2023). Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data. BMC Genomics, 24(1), Article 213. https://doi.org/10.1186/s12864-023-09292-7

Byg, L. M., Speed, M., Speed, D. & Østergaard, S. D. (2023). Genetic liability to bipolar disorder and body mass index: A bidirectional two-sample mendelian randomization study. Bipolar Disorders, 25(1), 25-31. https://doi.org/10.1111/bdi.13267

Zaalberg, R. M., Chu, T. T., Bovbjerg, H., Jensen, J. & Villumsen, T. M. (2023). Genetic parameters for early piglet weight, litter traits and number of functional teats in organic pigs. Animal, 17(3), Article 100717. https://doi.org/10.1016/j.animal.2023.100717

Manzanilla-Pech, C. I. V., Stephansen, R. B. & Lassen, J. (2023). Genetic parameters for feed intake and body weight in dairy cattle using high-throughput 3-dimensional cameras in Danish commercial farms. Journal of Dairy Science, 106(12), 9006-9015. https://doi.org/10.3168/jds.2023-23405

Adlam, D., Berrandou, T. E., Georges, A., Nelson, C. P., Giannoulatou, E., Henry, J., Ma, L., Blencowe, M., Turley, T. N., Yang, M. L., Chopade, S., Finan, C., Braund, P. S., Sadeg-Sayoud, I., Iismaa, S. E., Kosel, M. L., Zhou, X., Hamby, S. E., Cheng, J. ... DISCO register (2023). Genome-wide association meta-analysis of spontaneous coronary artery dissection identifies risk variants and genes related to artery integrity and tissue-mediated coagulation. Nature Genetics, 55(6), 964-972. https://doi.org/10.1038/s41588-023-01410-1

Cai, Z., Wu, X., Thomsen, B., Lund, M. S. & Sahana, G. (2023). Genome-wide association study identifies functional genomic variants associated with young stock survival in Nordic Red Dairy Cattle. Journal of Dairy Science, 106(11), 7832-7845. https://doi.org/10.3168/jds.2023-23252

Mei, Q., Liu, H., Zhao, S., Xiang, T. & Christensen, O. F. (2023). Genomic evaluation for two-way crossbred performance in cattle. Genetics Selection Evolution, 55(1), Article 17. https://doi.org/10.1186/s12711-023-00792-4

Legarra, A. & Christensen, O. F. (2023). Genomic evaluation methods to include intermediate correlated features such as high-throughput or omics phenotypes. JDS Communications , 4(1), 55-60. https://doi.org/10.3168/jdsc.2022-0276

Guillenea, A., Christensen, O. F., Thomasen, J. R., Liu, H. & Karaman, E. (2023). Genomic evaluation of crossbred dairy cows considering breed origin of alleles. Abstract from Interbull Meeting 2023, Lyon, France.

Raffo, M. A., Cuyabano, B. C. D., Rincent, R., Sarup, P., Moreau, L., Mary-Huard, T. & Jensen, J. (2023). Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat. Frontiers in Plant Science, 13, Article 1075077. https://doi.org/10.3389/fpls.2022.1075077

Shi, R., Chen, Z., Su, G., Luo, H., Liu, L., Guo, G. & Wang, Y. (2023). Genomic prediction of service sire effect on female reproductive performance in Holstein cattle: A comparison between different methods, validation population and marker densities. Journal of Animal Breeding and Genetics, 140(4), 355-365. https://doi.org/10.1111/jbg.12763

Tenhunen, S. R., Thomasen, J. R., Sørensen, L. P., Berg, P. & Kargo, M. (2023). Genomic selection has aided Nordic Jersey to decrease risks of inbreeding. 393. Abstract from
74th Annual Meeting of European Federation of Animal Science, Lyon, France.

Wang, X., Li, W., Feng, X., Li, J., Liu, G. E., Fang, L. & Yu, Y. (2023). Harnessing male germline epigenomics for the genetic improvement in cattle. Journal of Animal Science and Biotechnology, 14(1), Article 76. https://doi.org/10.1186/s40104-023-00874-9

Ahuja, B., Doriya, R., Salunke, S., Hashmi, M. F., Gupta, A. & Bokde, N. D. (2023). HDIEA: high dimensional color image encryption architecture using five-dimensional Gauss-logistic and Lorenz system. Connection Science, 35(1), Article 2175792. https://doi.org/10.1080/09540091.2023.2175792

Manzanilla Pech, C. I. V., Stephansen, R. B. & Lassen, J. (2023). Heterogeneity of 3D cameras feed intake heritabilities in first parity Holstein, Jersey and Nordic Red cows. In Book of Abstracts of the 74th Annual Meeting of the European Federation of Animal Science : EAAP Book of Abstracts, Volume 29 Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-936-7

Bjerge, K., Geissmann, Q., Alison, J., Mann, H. M. R., Høye, T. T., Dyrmann, M. & Karstoft, H. (2023). Hierarchical classification of insects with multitask learning and anomaly detection. Ecological Informatics, 77, Article 102278. https://doi.org/10.1016/j.ecoinf.2023.102278

Bjerge, K., Geissmann, Q., Alison, J., Mann, H. M. R., Høye, T. T., Dyrmann, M. & Karstoft, H. (2023). Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection. bioRxiv. https://doi.org/10.1101/2023.06.29.546989

Gautason, E., Sahana, G., Guldbrandtsen, B. & Berg, P. (2023). Impact of kinship matrices on genetic gain and inbreeding with optimum contribution selection in a genomic dairy cattle breeding program. Genetics Selection Evolution, 55(1), Article 48. https://doi.org/10.1186/s12711-023-00826-x

Zhang, Z., Shi, S., Zhang, Q., Aamand, G. P., Lund, M. S., Su, G. & Ding, X. (2023). Improving Genomic Prediction Accuracy in the Chinese Holstein Population by Combining with the Nordic Holstein Reference Population. Animals, 13(4), Article 636. https://doi.org/10.3390/ani13040636

Lund, P., Børsting, C. F., Albrektsen, R., Nielsen, O.-K. & Villumsen, T. M., (2023). Input til udledningspostnotat om malkekvægs fordøjelse, No. 2023-0504452, 10 p., May 01, 2023.

Boerner, V., Nguyen, T. T. T. & Nieuwhof, G. J. (2023). Integration of Interbull's multiple across-country evaluation approach breeding values into the multiple-trait single-step random regression test-day genetic evaluation for yield traits of Australian Red breeds. Journal of Dairy Science, 106(2), 1159-1167. https://doi.org/10.3168/jds.2022-21816

Liu, C., Huang, R., Su, G., Hou, L., Zhou, W., Liu, Q., Qiu, Z., Zhao, Q. & Li, P. (2023). Introgression of pigs in Taihu Lake region possibly contributed to the improvement of fertility in Danish Large White pigs. BMC Genomics, 24(1), Article 733. https://doi.org/10.1186/s12864-023-09860-x

Sahana, G., Cai, Z., Sanchez, M. P., Bouwman, A. C. & Boichard, D. (2023). Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle. Journal of Dairy Science, 106(8), 5218-5241. https://doi.org/10.3168/jds.2022-22694

Berrandou, T.-E., Balding, D. & Speed, D. (2023). LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics. American Journal of Human Genetics, 110(1), 23-29. https://doi.org/10.1016/j.ajhg.2022.11.010

Li, J., Zhao, T., Guan, D., Pan, Z., Bai, Z., Teng, J., Zhang, Z., Zheng, Z., Zeng, J., Zhou, H., Fang, L. & Cheng, H. (2023). Learning functional conservation between human and pig to decipher evolutionary mechanisms underlying gene expression and complex traits. Cell Genomics, 3(10), Article 100390. https://doi.org/10.1016/j.xgen.2023.100390

Sahana, J., Cortés-Sánchez, J. L., Sandt, V., Melnik, D., Corydon, T. J., Schulz, H., Cai, Z., Evert, K., Grimm, D. & Wehland, M. (2023). Long-Term Simulation of Microgravity Induces Changes in Gene Expression in Breast Cancer Cells. International Journal of Molecular Sciences , 24(2), Article 1181. https://doi.org/10.3390/ijms24021181

Guo, X., Sarup, P., Jahoor, A., Jensen, J. & Christensen, O. F. (2023). Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley. Genetics Selection Evolution, 55(1), Article 61. https://doi.org/10.1186/s12711-023-00835-w

Hellwing, A. L. F., Vestergaard, M. & Kargo, M. (2023). Methane emission from purebred Holstein, Nordic Red and F1 crossbred cows of the two breeds. In Book of Abstracts of the 74th Annual Meeting of the European Federation of Animal Science (pp. 937-937). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-936-7

Forte, F. P., Malinowska, M., Nagy, I., Schmid, J., Dijkwel, P., Hume, D. E., Johnson, R. D., Simpson, W. R. & Asp, T. (2023). Methylome changes in Lolium perenne associated with long-term colonisation by the endophytic fungus Epichloë sp. LpTG-3 strain AR37. Frontiers in Plant Science, 14, Article 1258100. https://doi.org/10.3389/fpls.2023.1258100, https://doi.org/10.3389/fpls.2023.1258100

Boggio, G. M., Christensen, O. F., Legarra, A., Meynadier, A. & Marie-Etancelin, C. (2023). Microbiability of milk composition and genetic control of microbiota effects in sheep. Journal of Dairy Science, 106(9), 6288-6298. https://doi.org/10.3168/jds.2022-22948

Clasen, J. B., Fikse, W. F., Su, G. & Karaman, E. (2023). Multibreed genomic prediction using summary statistics and a breed-origin-of-alleles approach. Heredity, 131(1), 33-42. https://doi.org/10.1038/s41437-023-00619-4

Tao, K., Thorsgaard Jensen, I., Zhang, S., Villa Rodríguez, E. D., Blahovska, Z., Salomonsen, C. L., Martyn, A., Björgvinsdóttir, Þ. N., Kelly, S., Janss, L., Glasius, M., Waagepetersen, R. & Radutoiu, S. (2023). Nitrogen source and Nod factor signaling map out the assemblies of Lotus japonicus root bacterial communities.

Stephansen, R. B., Martin, P., Manzanilla-Pech, C. I. V., Gredler-Grandl, B., Sahana, G., Madsen, P., Weigel, K., Tempelman, R. J., Peñagaricano, F., Gaddis, K. L. P., White, H. M., Santos, J. E. P., Koltes, J. E., Schenkel, F., Hailemariam, D., Plastow, G., Abdalla, E., VandeHaar, M., Veerkamp, R. F. ... Lassen, J. (2023). Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe. Journal of Dairy Science, 106(12), 9078-9094. https://doi.org/10.3168/jds.2023-23330, https://doi.org/10.3168/jds.2023-23330

Friggens, N. C., Ithurbide, M. & Bouquet, A. E. R. (2023). Novel traits related to productive life. Abstract from 45th ADSA Discover Conference, Itasca, Illinois, United States.

Bouquet, A. E. R., Nielsen, H. M., Milkevych, V., Kargo, M., Thomasen, J. R. & Slagboom, M. (2023). Optimizing dairy cattle breeding goals to improve production and udder health of crossbred cows. In Book of Abstracts of the 74th Annual Meeting of the European Federation of Animal Science Wageningen Academic Publishers.

Jiang, Y. F., Wang, S., Wang, C. L., Xu, R. H., Wang, W. W., Jiang, Y., Wang, M. S., Jiang, L., Dai, L. H., Wang, J. R., Chu, X. H., Zeng, Y. Q., Fang, L. Z., Wu, D. D., Zhang, Q. & Ding, X. D. (2023). Pangenome obtained by long-read sequencing of 11 genomes reveal hidden functional structural variants in pigs. iScience, 26(3), Article 106119. https://doi.org/10.1016/j.isci.2023.106119

Schoeler, T., Speed, D., Porcu, E., Pirastu, N., Pingault, J. B. & Kutalik, Z. (2023). Participation bias in the UK Biobank distorts genetic associations and downstream analyses. Nature Human Behaviour, 7(7), 1216-1227. https://doi.org/10.1038/s41562-023-01579-9

Londhe, D. S., Katpatal, Y. B. & Bokde, N. D. (2023). Performance Assessment of Bias Correction Methods for Precipitation and Temperature from CMIP5 Model Simulation. Applied Sciences (Switzerland), 13(16), Article 9142. https://doi.org/10.3390/app13169142

Wu, Y., Li, D., Hu, Y., Li, H., Ramstein, G., Zhou, S., Zhang, X., Bao, Z., Zhang, Y., Song, B., Zhou, Y., Zhou, Y., Gagnon, E., Särkinen, T., Knapp, S., Zhang, C., Städler, T., Buckler, E. S. & Huang, S. (2023). Phylogenomic discovery of deleterious mutations facilitates hybrid potato breeding. Cell, 186(11), 2313-2328.e15. https://doi.org/10.1016/j.cell.2023.04.008

Zhou, R., Yu, X., Rosenqvist, E. & Ottosen, C. O. (2023). Physiological and genetic studies inspired insights into tomato responding to multiple abiotic stresses. Acta Horticulturae, (1372), 1-7. https://doi.org/10.17660/ActaHortic.2023.1372.1

Kristensen, P. S., Sarup, P., Fé, D., Orabi, J., Snell, P., Ripa, L., Mohlfeld, M., Chu, T. T., Herrström, J., Jahoor, A. & Jensen, J. (2023). Prediction of additive, epistatic, and dominance effects using models accounting for incomplete inbreeding in parental lines of hybrid rye and sugar beet. Frontiers in Plant Science, 14, Article 1193433. https://doi.org/10.3389/fpls.2023.1193433

Stephansen, R. B., Manzanilla Pech, C. I. V., Gebreyesus, G., Sahana, G. & Lassen, J. (2023). Prediction of body condition in Jersey dairy cattle from 3D images using machine learning techniques. Abstract from ICAR 2023, Toledo, Spain. https://www.icar.org/wp-content/uploads/2024/01/ICAR-Technical-Series-27-Toledo-2023-Proceedings.pdf

Stephansen, R. B., Manzanilla Pech, C. I. V., Gebreyesus, G., Sahana, G. & Lassen, J. (2023). Prediction of body condition in Jersey dairy cattle from 3D-images using machine learning techniques. Journal of Animal Science, 101, Article skad376. https://doi.org/10.1093/jas/skad376

Salunke, S., Shrivastava, A. K., Hashmi, M. F., Ahuja, B. & Bokde, N. D. (2023). Quad Key-Secured 3D Gauss Encryption Compression System with Lyapunov Exponent Validation for Digital Images. Applied Sciences (Switzerland), 13(3), Article 1616. https://doi.org/10.3390/app13031616

Sørensen, J. T. (Ed.), Jensen, M. B., Thomsen, P. T., Kongsted, H., Pedersen, L. J., Riber, A. B., Kargo, M., Hein, L., Villumsen, T. M., Manzanilla Pech, C. I. V., Buitenhuis, A. J. & Christensen, O. F. (2023). Samspil mellem klima, miljø og dyrevelfærd: En vurdering af konsekvenser for dyrevelfærden af tiltag på besætningsniveau til reduktion af husdyrproduktionens klima- og miljøpåvirkning. DCA - Nationalt Center for Fødevarer og Jordbrug. DCA rapport No. 222 https://dcapub.au.dk/djfpublikation/djfpdf/DCArapport222.pdf

Eiríksson, J. H., Su, G., Strandén, I. & Christensen, O. F. (2023). Segregation between breeds and local breed proportions in genetic and genomic models for crossbreds. Genetics Selection Evolution, 55(1), Article 45. https://doi.org/10.1186/s12711-023-00810-5

Nielsen, B., Horndrup, L. V., Turner, S. P., Christensen, O. F., Nielsen, H. M. & Ask, B. (2023). Selection for social genetic effects in purebred pigs improves behaviour and handling of their crossbred progeny. Genetics, selection, evolution : GSE, 55(1), 54. Article 54. https://doi.org/10.1186/s12711-023-00828-9

Slagboom, M., Nielsen, H. M., Kargo, M., Henryon, M. & Hansen, L. S. (2023). Simulating breeding programs based on mass selection in black soldier fly (Hermetia illucens). Abstract from
74th Annual Meeting of European Federation of Animal Science, Lyon, France.

Persichilli, C., Karaman, E., Finocchiaro, R., Pilla, F., Fabris, A., Visentin, G. & Cassandro, M. (2023). Study of fertility traits in Italian Jersey Cattle. Italian Journal of Animal Science, 22(sup1), 41. Article 0563. https://aspacongress2023.com/wp-content/uploads/2023/06/ASPA-25th-Congress-Book-of-Abstract_compressed.pdf

Gebreyesus, G., Milkevych, V., Lassen, J. & Sahana, G. (2023). Supervised learning techniques for dairy cattle body weight prediction from 3D digital images. Frontiers in Genetics, 13, Article 947176. https://doi.org/10.3389/fgene.2022.947176

Zhao, P., Peng, C., Fang, L., Wang, Z. & Liu, G. E. (2023). Taming transposable elements in livestock and poultry: a review of their roles and applications. Genetics Selection Evolution, 55(1), Article 50. https://doi.org/10.1186/s12711-023-00821-2

Christensen, B., Buitenhuis, A. J., Jacobsen, L. N., Ostenfeld, M. S. & Sørensen, E. S. (2023). The Effect of Human and Bovine Milk Osteopontin on Intestinal Caco-2 Cells: A Transcriptome Comparison. Nutrients, 15(5), Article 1166. https://doi.org/10.3390/nu15051166

Jayakodi, M., Golicz, A. A., Kreplak, J., Fechete, L. I., Angra, D., Bednář, P., Bornhofen, E., Zhang, H., Boussageon, R., Kaur, S., Cheung, K., Čížková, J., Gundlach, H., Hallab, A., Imbert, B., Keeble-Gagnère, G., Koblížková, A., Kobrlová, L., Krejčí, P. ... Andersen, S. U. (2023). The giant diploid faba genome unlocks variation in a global protein crop. Nature, 615(7953), 652-659. https://doi.org/10.1038/s41586-023-05791-5

Liu, T., Nielsen, B., Christensen, O. F., Lund, M. S. & Su, G. (2023). The impact of genotyping strategies and statistical models on accuracy of genomic prediction for survival in pigs. Journal of Animal Science and Biotechnology, 14(1), Article 1. https://doi.org/10.1186/s40104-022-00800-5

Thorup, V. M., Munksgaard, L., Terré, M., Henriksen, J. C. S., Weisbjerg, M. R. & Lovendahl, P. (2023). The relationship between feed efficiency and behaviour differs between lactating Holstein and Jersey cows. Journal of Dairy Research, 90(3), 257-260. https://doi.org/10.1017/S0022029923000420

Wang, X., Gao, Y., Li, C. J., Fang, L., Liu, G. E., Zhao, X., Zhang, Y., Cai, G., Xue, G., Liu, Y., Wang, L., Zhang, F., Wang, K., Zhang, M., Li, R., Gao, Y. & Li, J. (2023). The single-cell transcriptome and chromatin accessibility datasets of peripheral blood mononuclear cells in Chinese holstein cattle. BMC genomic data, 24(1), Article 39. https://doi.org/10.1186/s12863-023-01139-0

Hein, L., Thomasen, J. R., Kargo, M., Nielsen, H. M., Bouquet, A. E. R., Dahl, M. & Liu, H. (2023). The value of increased heterozygosity in dairy cattle. In Proceedings of the 2023 Interbull Meeting https://journal.interbull.org/index.php/ib/article/view/1902

Bolormaa, S., Haile-Mariam, M., Marett, L., Miglior, F., Baes, C. F., Schenkel, F. S., Connor, E. E., Manzanilla Pech, C. I. V., Wall, E., Coffey, M., Goddard, M., MacLeod, I. & Pryce, J. E. (2023). Use of dry-matter intake recorded at multiple time periods during lactation increases the accuracy of genomic prediction for dry-matter intake and residual feed intake in dairy cattle. Animal Production Science, 63(11), 1113-1125. Article AN23022. https://doi.org/10.1071/AN23022

Hsu, Y. H. H., Pintacuda, G., Liu, R., Nacu, E., Kim, A., Tsafou, K., Petrossian, N., Crotty, W., Suh, J. M., Riseman, J., Martin, J. M., Biagini, J. C., Mena, D., Ching, J. K. T., Malolepsza, E., Li, T., Singh, T., Ge, T., Egri, S. B. ... Wellcome Trust Case Control Consortium (2023). Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia. iScience, 26(5), Article 106701. https://doi.org/10.1016/j.isci.2023.106701

Saedi, N., Lund, M. S. & Karaman, E. (2023). Using genomic markers and microbiome data to predict methane emissions from Holstein cows. In Book of Abstracts of the 74th Annual Meeting of the European Federation of Animal Science Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-936-7

Balbuena, L., Peters, E. & Speed, D. (2023). Using polygenic risk scores to investigate the evolution of smoking and mental health outcomes in UK biobank participants. Acta Psychiatrica Scandinavica, 148(5), 447-456. https://doi.org/10.1111/acps.13601, https://doi.org/10.1111/acps.13601

Long, E. M., Romay, M. C., Ramstein, G., Buckler, E. S. & Robbins, K. R. (2023). Utilizing evolutionary conservation to detect deleterious mutations and improve genomic prediction in cassava. Frontiers in Plant Science, 13, Article 1041925. https://doi.org/10.3389/fpls.2022.1041925

Jain, S. K., Bhatia, R., Shrivastava, N., Salunke, S., Hashmi, M. F. & Bokde, N. D. (2023). Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks. Future Internet, 15(8), Article 259. https://doi.org/10.3390/fi15080259

Zhao, P., Gu, L., Gao, Y., Pan, Z., Liu, L., Li, X., Zhou, H., Yu, D., Han, X., Qian, L., Liu, G. E., Fang, L. & Wang, Z. (2023). Young SINEs in pig genomes impact gene regulation, genetic diversity, and complex traits. Communications Biology, 6(1), Article 894. https://doi.org/10.1038/s42003-023-05234-x

Salunke, S., Ahuja, B., Hashmi, M. F., Marriboyina, V. & Bokde, N. D. (2022). 5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation. Applied Sciences, 12(11), Article 5321. https://doi.org/10.3390/app12115321

Gredler-Grandl, B., Raymond, B., Schopen, G. C. B., Chitneedi, P. K., Cai, Z., Manzanilla Pech, C. I. V., Iso-Touru, T., Fischer, D., Bolormaa, S., Chud, T. C. S., Schenkel, F. S., Wang, Y., Li, C., Hailemariam, D., Villanueva, B., Fernandez, A., Kuehn, C., Sahana, G., Lidauer, M. ... Bouwman, A. C. (2022). Accuracy of genomic prediction of dry matter intake in Dutch Holsteins using sequence variants from meta-analyses. In WCGALP 2022 Programme book Wageningen Academic Publishers. https://www.wageningenacademic.com/pb-assets/wagen/WCGALP2022/02_010.pdf

Croiseau, P., Saintilan, R., Baur, A., Croue, I., Ducrocq, V., Thomasen, J. R., Karaman, E., Boichard, D., Leclerc, H. & Cuyabano, B. C. D. (2022). Accuracy of prediction for a genomic evaluation in rotational crossbreeding scheme (Montbéliarde x Holstein x Red Danish). In WCGALP 2022 Programme book Wageningen Academic Publishers. https://www.wageningenacademic.com/pb-assets/wagen/WCGALP2022/02_001.pdf

Saintilan, R., Croiseau, P., Baur, A., Croue, I., Ducrocq, V., Thomasen, J. R., Karaman, E., Boichard, D., Leclerc, H. & Cuyabano, B. C. D. (2022). Accuracy of prediction for a genomic evaluation in rotational crossbreeding scheme (Montbéliarde x Holstein x Red Danish). In Proceedings of the 2022 Interbull Meeting (pp. 18-22) https://journal.interbull.org/index.php/ib

Goutam, B., Hashimi, M. F., Geem, Z. W. & Bokde, N. (2022). A Comprehensive Review of Deep Learning Strategies in Retinal Disease Diagnosis Using Fundus Images. IEEE Access, 10, 57796-57823. https://doi.org/10.1109/ACCESS.2022.3178372

Liu, S., Gao, Y., Canela-Xandri, O., Wang, S., Yu, Y., Cai, W., Li, B., Xiang, R., Chamberlain, A. J., Pairo-Castineira, E., D'Mellow, K., Rawlik, K., Xia, C., Yao, Y., Navarro, P., Rocha, D., Li, X., Yan, Z., Li, C. ... Fang, L. (2022). A multi-tissue atlas of regulatory variants in cattle. Nature Genetics, 54(9), 1438-1447. https://doi.org/10.1038/s41588-022-01153-5

Khare, S. K., Gaikwad, N. & Bokde, N. D. (2022). An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets. Sensors (Switzerland), 22(21), Article 8128. https://doi.org/10.3390/s22218128

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