Varshney RK, Roorkiwal M, Sun S, Bajaj P, Chitikineni A, Thudi M, Singh NP, Du X, Upadhyaya HD, Khan AW, Wang Y, Garg V, Fan G, Cowling WA, Crossa J, Gentzbittel L, Voss-Fels KP, Valluri VK, Sinha P, Singh VK, Ben C, Rathore A, Punna R, Singh MK, Tar'an B, Bharadwaj C, Yasin M, Pithia MS, Singh S, Soren KR, Kudapa H, Jarquín D, Cubry P, Hickey LT, Dixit GP, Thuillet AC, Hamwieh A, Kumar S, Deokar AA, Chaturvedi SK, Francis A, Howard R, Chattopadhyay D, Edwards D, Lyons E, Vigouroux Y, Hayes BJ, von Wettberg E, Datta SK, Yang H, Nguyen HT, Wang J, Siddique KHM, Mohapatra T, Bennetzen JL, Xu X, Liu X. A chickpea genetic variation map based on the sequencing of 3,366 genomes. Nature. 2021 Nov;599(7886):622-627. doi: 10.1038/s41586-021-04066-1. Epub 2021 Nov 10. Erratum in: Nature. 2022 Apr;604(7905):E12. PMID: 34759320; PMCID: PMC8612933.

Gasanov, M., Merkulov, D., Nikitin, A., Matveev, S., Stasenko, N., Petrovskaia, A., Pukalchik, M., Oseledets, I., 2021. A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History, in: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (Eds.), Computational Science – ICCS 2021, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 75–88. https://doi.org/10.1007/978-3-030-77970-2_7

Illarionova, S., Nesteruk, S., Shadrin, D., Ignatiev, V., Pukalchik, M., Oseledets, I., 2021. MixChannel: Advanced Augmentation for Multispectral Satellite Images. Remote Sensing 13, 2181. https://doi.org/10.3390/rs13112181

Nesteruk, S., Shadrin, D., Pukalchik, M., Somov, A., Zeidler, C., Zabel, P., Schubert, D., 2021. Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case. IEEE Sensors J. 1–1. https://doi.org/10.1109/JSEN.2021.3050084

Shadrin, D., Nikitin, A., Tregubova, P., Terekhova, V., Jana, R., Matveev, S., Pukalchik, M., 2021. An Automated Approach to Groundwater Quality Monitoring—Geospatial Mapping Based on Combined Application of Gaussian Process Regression and Bayesian Information Criterion. Water 13, 400. https://doi.org/10.3390/w13040400

Stasenko, N., Chernova, E., Shadrin, D., Ovchinnikov, G., Krivolapov, I., Pukalchik, M., 2021. Deep Learning for improving the storage process: Accurate and automatic segmentation of spoiled areas on apples, in: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Presented at the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, Glasgow, United Kingdom, pp. 1–6. https://doi.org/10.1109/I2MTC50364.2021.9460071

Vypirailenko, D., Kiseleva, E., Shadrin, D., Pukalchik, M., 2021. Deep learning techniques for enhancement of weeds growth classification, in: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Presented at the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, Glasgow, United Kingdom, pp. 1–6. https://doi.org/10.1109/I2MTC50364.2021.9459976

Yudina, E., Petrovskaia, A., Shadrin, D., Tregubova, P., Chernova, E., Pukalchik, M., Oseledets, I., 2021. Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case. Water 13, 888. https://doi.org/10.3390/w13070888

Chaouachi, M., Marzouk, T., Jallouli, S., Elkahoui, S., Gentzbittel, L., Ben, C., & Djébali, N. (2021). Activity assessment of tomato endophytic bacteria bioactive compounds for the postharvest biocontrol of Botrytis cinerea. Postharvest Biology and Technology, 172, 111389.

Skoltech AGRO

Shadrin, D., Pukalchik, M., Kovaleva, E., & Fedorov, M. (2020). Artificial intelligence models to predict acute phytotoxicity in petroleum contaminated soils. Ecotoxicology and Environmental Safety, 194, 110410.

Gasanov, M., Petrovskaia, A., Nikitin, A., Matveev, S., Tregubova, P., Pukalchik, M., & Oseledets, I. Sensitivity analysis of soil parameters in crop model supported with high-throughput computing, ICCS, 2020

Shadrin, D., Pukalchik, M., Uryasheva, A., Rodichenko, N., & Tsetserukou, D. (2020). Hyper-spectral NIR and MIR data and optimal wavebands for detecting of apple trees diseases. arXiv preprint arXiv:2004.02325, ICLR 2020

Matvienko, I., Gasanov, M., Petrovskaia, A., Jana, R. B., Pukalchik, M., & Oseledets, I. (2020). Bayesian aggregation improves traditional single image crop classification approaches. arXiv preprint arXiv:2004.03468, ICLR 2020

Petrovskaia, A., Jana, R. B., & Oseledets, I. V. (2020). A single image deep learning approach to restoration of corrupted remote sensing products. arXiv preprint arXiv:2004.04209, ICLR 2020

Shashkova, T. I., Martynova, E. U., Ayupova, A. F., Shumskiy, A. A., Ogurtsova, P. A., Kostyunina, O. V., ... & Zinovieva, N. A. (2020). Development of a low-density panel for genomic selection of pigs in Russia. Translational animal science, 4(1), 264-274.

Kostyunina, O. V., Abdelmanova, A. S., Martynova, E. U., & Zinovieva, N. A. A SEARCH FOR GENOMIC REGIONS CARRYING THE LETHAL GENETIC VARIANTS IN THE DUROC PIGS. BIOLOGY AGRICULTURAL, 275.

Kanapin, A. A., Sokolkova, A. B., Samsonova, A. A., Shchegolkov, A. V., Boldyrev, S. V., Aupova, A. F., ... & Samsonova, M. G. (2020). Genetic Variants Associated with Productivity and Contents of Protein and Oil in Soybeans. Biophysics, 65(2), 241-249.

Nikitin, A., Fastovets, I., Shadrin, D., Pukalchik, M., & Oseledets, I. (2019). Bayesian optimization for seed germination. Plant methods, 15(1), 43.

Pukalchik, M., Kydralieva, K., Yakimenko, O., Fedoseeva, E., & Terekhova, V. (2019). Outlining the potential role of humic products in modifying biological properties of the soil—a review. Frontiers in Environmental Science, 7, 80.

Pukalchik, M. A., Katrutsa, A. M., Shadrin, D., Terekhova, V. A., & Oseledets, I. V. (2019). Machine learning methods for estimation the indicators of phosphogypsum influence in soil. Journal of soils and sediments, 19(5), 2265-2276.

Jana, R. B., & Petrovskaia, A. (2019). 3D representation of soil structure using Generative Adversarial Networks. AGUFM, 2019, H31I-1835.

Chernova, A., Mazin, P., Goryunova, S., Goryunov, D., Demurin, Y., Gorlova, L., ... & Khaitovich, P. (2019). Ultra-performance liquid chromatography-mass spectrometry for precise fatty acid profiling of oilseed crops. PeerJ, 7, e6547.

Goryunova, S. V., Goryunov, D. V., Chernova, A. I., Martynova, E. U., Dmitriev, A. E., Boldyrev, S. V., ... & Demurin, Y. N. (2019). Genetic and phenotypic diversity of the sunflower collection of the Pustovoit All-Russia Research Institute of Oil Crops (VNIIMK). Helia, 42(70), 45-60.

Chernova, A., Gubaev, R., Mazin, P., Goryunova, S., Demurin, Y., Gorlova, L., ... & Khaytovich, P. (2019). UPLC–MS Triglyceride Profiling in Sunflower and Rapeseed Seeds. Biomolecules, 9(1), 9.

Goryunov, D. V., Anisimova, I. N., Gavrilova, V. A., Chernova, A. I., Sotnikova, E. A., Martynova, E. U., ... & Goryunova, S. V. (2019). Association mapping of fertility restorer gene for CMS PET1 in sunflower. Agronomy, 9(2), 49.

Chernova, A. I., & Martynova, E. U. (2019). High-throughput technologies for sunflower oil improvement. Current Challenges in Plant Genetics, Genomics, Bioinformatics, and Biotechnology, 24, 227.

Gubaev R, Boldyrev S, Martynova E, Chernova A, Kovalenko T, Peretyagina T, Goryunova S, Goryunov D, Mukhina Z, Ben C, Gentzbittel L, Khaitovich P, Demurin Y. (2022) Genetic mapping of loci involved in oil tocopherol composition control in Russian sunflower (Helianthus annuus L.) lines. G3 Genes|Genomes|Genetics, Volume 12, Issue 4, April 2022, jkac036, https://doi.org/10.1093/g3journal/jkac036

PAG 2020, San Diego- poster. Abstract: "Association studies for cultivated Sunflower oil improvement."

International Sunflower Conference, Serbia 2020 –oral presentation accepted but postponed until 2021_ Abstract: "Genome-wide association studies reveal new genetic loci associated with fatty acid composition in Sunflower" (Accepted, but the conference was postponed till next year)

Gordon research conference on quantitative genetics and genomics 2019, Italy- poster and travel grant. Abstract: "Cultivated sunflower high-throughput genotyping and lipidomic profiling."

Young scientists conference Agrobiotech2019, Moscow – oral presentation. Abstract: Use of high-throughput sequencing and high-throughput molecular phenotyping to improve economically significant characteristics of sunflower oil (On Russian).

Mapping of genetic associations for agronomically important traits in Russian collection of rapeseeds (Brassica napus). R. Gubaev, S. Goryunova, D. Goryunov, P. Mazin, S. Boldyrev, A. Chernova, A. Ayupova, E. Martynova, Y. Demurin, Z. Mukhina, P. Khaitovich. Proceedings of the 9th International Moscow Conference on Computational Molecular Biology МССМВ'19, Moscow, July 27-30, 2019, p 103.

- SNP panel for genotyping and genomic selection of sunflower according to the content of fatty acids in seed oil. (Patent No. 2717642)

- Database of oilseed crops genotypes (State Registration Certificate No. 2018620271)

- Database of oilseed crops phenotypes (State Registration Certificate No. 2019620084)

- Database of lipid composition of adipose and muscle tissue of pork (State Registration Certificate No. 2019622411)

- Database of genomic variability of breeding pigs (State Registration Certificate No. 2019622289).