Reviews & Reference Works
Not every paper in CAAIL applies one AI method to one research area, so not every paper lives in the matrix. The entries below are the reviews, position papers, and foundational reference works the matrix builds on but doesn’t cite — everything reachable in Papers.md but invisible in the Explorer.
Reviews & Perspectives
64Review articles, position papers, and commentaries that survey or opine on the field rather than applying a specific AI method — so they sit outside the matrix.
Datta, B., Buehler, M. J., Chow, Y., Gligorić, K., Jurafsky, D., Kaplan, D. L., Ledesma-Amaro, R., Del Missier, G., Neidhardt, L., Pichara, K., Sanchez-Lengeling, B., Schlangen, M., St. Pierre, S. R., Tagkopoulos, I., Thomas, A., Watson, N. J., & Kuhl, E. (2026)
Artificial intelligence for food innovation. Nature Food
Ho, Y. Y., Sivakumar, S., Ho, Y. S., & Lakshmanan, M. (2026)
Rational design of serum-free media for cultivated meat. Nature Reviews Bioengineering
Smith, C. S., Krusinski, L., Cohen, C. A., Kawecki, N. S., Moccio, L., Xie, Q., Cheng, E., Yee, Z., Adler, S., Simpson, D., Marcotte, G., Damoiseaux, R., Park, J. O., Crosbie, R. H., Mayhew, E. J., Garmyn, A. J., Fenton, J. I., & Rowat, A. C. (2026)
Design principles of cells for eatability and scalability of cultivated meat. Trends in Food Science & Technology
Kew, B., Sridharan, S., & Sarkar, A. (2026)
Mouthfeel assessment of alternative proteins: What we know so far?. Current Opinion in Food Science
Qin, Y., He, R., Kong, Z., Chen, Y., Kong, Y., Huang, D., Sun, Q., Xiao, Y., & Peng, Y. (2026)
Sustainable cultivated meat scaffolds from waste bioresources: pioneering the future of cellular agriculture. Food Chemistry
Li, Y., Jin, F., Qian, Y., Wang, S., Ding, S., Li, Y., Hu, L., He, R., Yan, Y., Tao, T., & Bo, M. (2026)
Allergens and allergenicity in cell-cultured meat: Current status and reflections. Trends in Food Science & Technology
Stieberová, B., Žilka, M., Bubeníček, P., & Kubeš, T. (2026)
Life cycle assessment of industrial-scale cultivated meat production: Case study of real market entry via pet food application. The International Journal of Life Cycle Assessment
Orsini, F., Pierini, L., Ardoino, I., & Franchi, C. (2026)
Environmental impact of cultured meat: A systematic review. ACS Food Science & Technology
Kuhl, E. (2025)
AI for food: accelerating and democratizing discovery and innovation. npj Science of Food
McNulty, M. J., Stout, A. J., & Kaplan, D. L. (2025)
Meating the moment. EMBO Reports
Roohani, Y. H., Hua, T. J., Tung, P. Y., Bounds, L. R., Yu, F. B., Dobin, A., Teyssier, N., Adduri, A., Woodrow, A., Plosky, B. S., Mehta, R., Hsu, B., Sullivan, J., Ricci-Tam, C., Li, N., Kazaks, J., Gilbert, L. A., Konermann, S., Hsu, P. D., Goodarzi, H., & Burke, D. P. (2025)
Virtual Cell Challenge: Toward a Turing test for the virtual cell. Cell
Ranpura, S., Maralingannavar, V., Gheorghe, A.-G., Ma, E., Morrissey, J., Betenbaugh, M. J., & Demirhan, D. (2025)
Wheels turning: CHO cell modeling moves into a digital biomanufacturing era. Computational and Structural Biotechnology Journal
Gulati, G. S., D'Silva, J. P., Liu, Y., Wang, L., & Newman, A. M. (2025)
Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nature Reviews Molecular Cell Biology
Mathieu, T., Légaré, S., Nzekoue, A., Jauré, N., Lester, H., Dias, T., & Kusters, R. (2025)
Integrative multi-omics modeling for cultivated meat production, quality, and safety. Trends in Food Science & Technology
Zhou, J., Jiang, J., Han, Z., Wang, Z., & Gao, X. (2025)
Streamline automated biomedical discoveries with agentic bioinformatics. Briefings in Bioinformatics
Bloor, M., Mowbray, M., Del Rio Chanona, E. A., & Tsay, C. (2025)
Survey and tutorial of reinforcement learning methods in process systems engineering. arXiv
Zhou, L., Ling, H., Fu, C., Huang, Y., Sun, M., Yu, W., Wang, X., Li, X., Su, X., Zhang, J., Chen, X., Liang, C., Qian, X., Ji, H., Wang, W., Zitnik, M., & Ji, S. (2025)
Autonomous agents for scientific discovery: Orchestrating scientists, language, code, and physics. arXiv
Ramos, M. C., Collison, C. J., & White, A. D. (2025)
A review of large language models and autonomous agents in chemistry. Chemical Science
Yu, J., Yao, D., Wang, L., & Xu, M. (2025)
Machine learning in predicting and optimizing polymer printability for 3D bioprinting. Polymers
Lee, Y.-S., Lee, H. J., & Jo, C. (2025)
Integrative adipogenic engineering of cultured fat for cell based meat. Comprehensive Reviews in Food Science and Food Safety
Gao, W., Bai, R., & Ling, S. (2025)
Artificial intelligence-enabled cellular agriculture: Multiscale modeling, process optimization, and future directions. Trends in Food Science & Technology
Ng, W. L., & Tan, J. S. (2025)
Machine learning in cultivated meat: Enhancing sustainability, efficiency, quality, and scalability across the production pipeline. Food and Bioprocess Technology
Saraswat, S., Bhargava, T., Landge, J., & Tibrewal, K. (2025)
Towards intelligent cultivated/cultured meat factories: The synergy of AI, 3D bioprinting and automation in next-gen food manufacturing. Bioprinting
Peng, J., Khuat, T. T., Musial, K., & Gabrys, B. (2025)
Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review. arXiv
Ham, J.-H., Lee, Y.-J., Lee, I., & Kim, H.-Y. (2025)
Allergenicity in cultured meat: Assessment and strategic management. Critical Reviews in Food Science and Nutrition
Risner, D., Negulescu, P., Kim, Y., Nguyen, C., Siegel, J. B., & Spang, E. S. (2025)
Environmental impacts of cultured meat: A cradle-to-gate life cycle assessment. ACS Food Science & Technology
Blackstone, N. T., Pavlova, A., Trinidad, K. R., Nikkhah, A., Sinke, P., Heller, M., Duncan-Duggal, J., Ridoutt, B., Smetana, S., Makov, T., Shabtai, S., Green, A., Barnes, W., Bhattarai, I., Goyal, S., Imholz, N., Meshulam, T., Nadar, C. G., Norris, G. A., Quandt, J., Ronco, N., & Tuomisto, H. L. (2025)
Guidelines for environmental life cycle assessment of cultivated meat. The International Journal of Life Cycle Assessment
Tavan, M., Smith, N. W., McNabb, W. C., & Wood, P. (2025)
Reassessing the sustainability promise of cultured meat: A critical review with new data perspectives. Critical Reviews in Food Science and Nutrition
Alasi, S. O., Sanusi, M. S., Sunmonu, M. O., Odewole, M. M., & Adepoju, A. L. (2024)
Exploring recent developments in novel technologies and AI integration for plant-based protein functionality: A review. Journal of Agriculture and Food Research
Todhunter, M. E., Jubair, S., Verma, R., Saqe, R., Shen, K., & Duffy, B. (2024)
Artificial intelligence and machine learning applications for cultured meat. Frontiers in Artificial Intelligence
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., Califano, A., Cool, J., Dernburg, A. F., Ewing, K., Fox, E. B., Haury, M., Herr, A. E., Horvitz, E., Hsu, P. D., Jain, V., Johnson, G. R., Kalil, T., Kelley, D. R., Kelley, S. O., Kreshuk, A., Mitchison, T., Otte, S., Shendure, J., Sofroniew, N. J., Theis, F., Theodoris, C. V., Upadhyayula, S., Valer, M., Wang, B., Xing, E., Yeung-Levy, S., Zitnik, M., Karaletsos, T., Regev, A., Lundberg, E., Leskovec, J., & Quake, S. R. (2024)
How to build the virtual cell with artificial intelligence: Priorities and opportunities. Cell
Hashizume, T., & Ying, B.-W. (2024)
Challenges in developing cell culture media using machine learning. Biotechnology Advances
Cai, D., Li, X., Liu, H., Wen, L., & Qu, D. (2024)
Machine learning and flavoromics-based research strategies for determining the characteristic flavor of food: A review. Trends in Food Science & Technology
Boiarsky, R., Singh, N. M., Buendia, A., Amini, A. P., Getz, G., & Sontag, D. (2024)
Deeper evaluation of a single-cell foundation model. Nature Machine Intelligence
Yang, F., Wang, F., Huang, L., Liu, L., Huang, J., & Yao, J. (2024)
Reply to: Deeper evaluation of a single-cell foundation model. Nature Machine Intelligence
Riquelme-Guzmán, C., Stout, A. J., Kaplan, D. L., & Flack, J. E. (2024)
Unlocking the potential of cultivated meat through cell line engineering. iScience
Ng, W. L., & Tan, J. S. (2024)
Application of machine learning in 3D bioprinting of cultivated meat. International Journal of AI for Materials and Design
Karimi Alavijeh, M., Lee, Y. Y., & Gras, S. L. (2024)
A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments. Engineering in Life Sciences
Isaac, K. S., Combe, M., Potter, G., & Sokolenko, S. (2024)
Machine learning tools for peptide bioactivity evaluation – Implications for cell culture media optimization and the broader cultivated meat industry. Current Research in Food Science
Pasitka, L., Wissotsky, G., Ayyash, M., Yarza, N., Rosoff, G., Kaminker, R., & Nahmias, Y. (2024)
Empirical economic analysis shows cost-effective continuous manufacturing of cultivated chicken using animal-free medium. Nature Food
El Wali, M., Karinen, H., Rønning, S. B., Skrivergaard, S., Dorca-Preda, T., Rasmussen, M. K., Young, J. F., Therkildsen, M., Mogensen, L., Ryynänen, T., & Tuomisto, H. L. (2024)
Life cycle assessment of culture media with alternative compositions for cultured meat production. The International Journal of Life Cycle Assessment
Goodwin, C. M., Aimutis, W. R., & Shirwaiker, R. A. (2024)
A scoping review of cultivated meat techno-economic analyses to inform future research directions for scaled-up manufacturing. Nature Food
Gomez Romero, S., & Boyle, N. (2023)
Systems biology and metabolic modeling for cultivated meat: A promising approach for cell culture media optimization and cost reduction. Comprehensive Reviews in Food Science and Food Safety
Zhou, T., Reji, R., Kairon, R. S., & Chiam, K. H. (2023)
A review of algorithmic approaches for cell culture media optimization. Frontiers in Bioengineering and Biotechnology
Ning, H., Zhou, T., & Joo, S. W. (2023)
Machine learning boosts three-dimensional bioprinting. International Journal of Bioprinting
Sugii, S., Wong, C. Y. Q., Lwin, A. K. O., & Chew, L. J. M. (2023)
Alternative fat: Redefining adipocytes for biomanufacturing cultivated meat. Trends in Biotechnology
Jara, T. C., Park, K., Vahmani, P., Van Eenennaam, A. L., Smith, L. R., & Denicol, A. C. (2023)
Stem cell-based strategies and challenges for production of cultivated meat. Nature Food
Negulescu, P. G., Risner, D., Spang, E. S., Sumner, D., Block, D., Nandi, S., & McDonald, K. A. (2023)
Techno-economic modeling and assessment of cultivated meat: Impact of production bioreactor scale. Biotechnology and Bioengineering
Sinke, P., Swartz, E., Sanctorum, H., van der Giesen, C., & Odegard, I. (2023)
Ex-ante life cycle assessment of commercial-scale cultivated meat production in 2030. The International Journal of Life Cycle Assessment
Wang, Y., Tuccillo, F., Lampi, A.-M., Knaapila, A., Pulkkinen, M., Kariluoto, S., Coda, R., Edelmann, M., Jouppila, K., Sandell, M., Piironen, V., & Katina, K. (2022)
Flavor challenges in extruded plant-based meat alternatives: A review. Comprehensive Reviews in Food Science and Food Safety
Freeman, S., Calabro, S., Williams, R., Jin, S., & Ye, K. (2022)
Bioink formulation and machine learning-empowered bioprinting optimization. Frontiers in Bioengineering and Biotechnology
Tuomisto, H. L., Allan, S. J., & Ellis, M. J. (2022)
Prospective life cycle assessment of a bioprocess design for cultured meat production in hollow fiber bioreactors. Science of the Total Environment
Mittermeier-Kleßinger, V. K., Hofmann, T., & Dawid, C. (2021)
Mitigating Off-Flavors of Plant-Based Proteins. Journal of Agricultural and Food Chemistry
O'Neill, E. N., Cosenza, Z. A., Baar, K., & Block, D. E. (2021)
Considerations for the development of cost-effective cell culture media for cultivated meat production. Comprehensive Reviews in Food Science and Food Safety
Soice, E., & Johnston, J. (2021)
Immortalizing cells for human consumption. International Journal of Molecular Sciences
Humbird, D. (2021)
Scale-up economics for cultured meat. Biotechnology and Bioengineering
Risner, D., Li, F., Fell, J. S., Pace, S. A., Siegel, J. B., Tagkopoulos, I., & Spang, E. S. (2021)
Preliminary techno-economic assessment of animal cell-based meat. Foods
Suthers, P. F., & Maranas, C. D. (2020)
Challenges of cultivated meat production and applications of genome-scale metabolic modeling. AIChE Journal
Fang, X., Lloyd, C. J., & Palsson, B. O. (2020)
Reconstructing organisms in silico: genome-scale models and their emerging applications. Nature Reviews Microbiology
Gu, C., Kim, G. B., Kim, W. J., Kim, H. U., & Lee, S. Y. (2019)
Current status and applications of genome-scale metabolic models. Genome Biology
Mattick, C. S., Landis, A. E., Allenby, B. R., & Genovese, N. J. (2015)
Anticipatory life cycle analysis of in vitro biomass cultivation for cultured meat production in the United States. Environmental Science & Technology
Suman, S. P., & Joseph, P. (2013)
Myoglobin chemistry and meat color. Annual Review of Food Science and Technology
Tuomisto, H. L., & Teixeira de Mattos, M. J. (2011)
Environmental impacts of cultured meat production. Environmental Science & Technology
Sparkes, A., Aubrey, W., Byrne, E., Clare, A., Khan, M. N., Liakata, M., Markham, M., Rowland, J., Soldatova, L. N., Whelan, K. E., Young, M., & King, R. D. (2010)
Towards Robot Scientists for autonomous scientific discovery. Automated Experimentation
Sensory & Flavor Reference Work
13Foundational sensory-science, flavor-chemistry, and sensomics papers the AI × cell-ag work builds on.
Ziaikin, E., David, M., Uspenskaya, S., & Niv, M. Y. (2025)
BitterDB: 2024 update on bitter ligands and taste receptors. Nucleic Acids Research
Ali, S., Liao, Z., Cheng, Y., Malhi, I. Y., Wang, Y., Peng, S., & Zhang, L. (2025)
Lipidomics in chicken meat flavor chemistry: Current understanding, integrated omics approaches, and future perspectives. Poultry Science
Zhou, H., Loo, L. S. W., Ong, F. Y. T., Lou, X., Wang, J., Myint, M. K., Thong, A., Seow, D. C. S., Wibowo, M., Ng, S., Lv, Y., Kwang, L. G., Bennie, R. Z., Pang, K. T., Dobson, R. C. J., Domigan, L. J., Kanagasundaram, Y., & Yu, H. (2025)
Cost-effective production of meaty aroma from porcine cells for hybrid cultivated meat. Food Chemistry
Spaccasassi, A., Utz, F., Dunkel, A., Aragao Börner, R., Ye, L., De Franceschi, F., Bogicevic, B., Glabasnia, A., Hofmann, T., & Dawid, C. (2024)
Screening of a Microbial Culture Collection: Empowering Selection of Starters for Enhanced Sensory Attributes of Pea-Protein-Based Beverages. Journal of Agricultural and Food Chemistry
Lew, E., Yuen, J., Zhang, K., Fuller, K., Frost, S., & Kaplan, D. L. (2024)
Chemical and sensory analyses of cultivated pork fat tissue as a flavor enhancer for meat alternatives. Scientific Reports
O'Neill, E. N., Ansel, J. C., Kwong, G. A., Plastino, M. E., Nelson, J., Baar, K., & Block, D. E. (2022)
Spent media analysis suggests cultivated meat media will require species and cell type optimization. npj Science of Food
Muroya, S., Ueda, S., Komatsu, T., Miyakawa, T., & Ertbjerg, P. (2020)
MEATabolomics: Muscle and Meat Metabolomics in Domestic Animals. Metabolites
Nicolotti, L., Mall, V., & Schieberle, P. (2019)
Characterization of Key Aroma Compounds in a Commercial Rum and an Australian Red Wine by Means of a New Sensomics-Based Expert System (SEBES)—An Approach To Use Artificial Intelligence in Determining Food Odor Codes. Journal of Agricultural and Food Chemistry
Dagan-Wiener, A., Di Pizio, A., Nissim, I., Bahia, M. S., Dubovski, N., Margulis, E., & Niv, M. Y. (2019)
BitterDB: taste ligands and receptors database in 2019. Nucleic Acids Research
Ramalingam, V., Song, Z., & Hwang, I. (2019)
The potential role of secondary metabolites in modulating the flavor and taste of the meat. Food Research International
Nissim, I., Dagan-Wiener, A., & Niv, M. Y. (2017)
The taste of toxicity: A quantitative analysis of bitter and toxic molecules. IUBMB Life
Wang, M., Carver, J. J., Phelan, V. V., Sanchez, L. M., Garg, N., Peng, Y., Nguyen, D. D., Watrous, J., Kapono, C. A., Luzzatto-Knaan, T., Porto, C., Bouslimani, A., Melnik, A. V., Meehan, M. J., Liu, W.-T., Crüsemann, M., Boudreau, P. D., Esquenazi, E., Sandoval-Calderón, M., ... Bandeira, N. (2016)
Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nature Biotechnology
Wiener, A., Shudler, M., Levit, A., & Niv, M. Y. (2012)
BitterDB: a database of bitter compounds. Nucleic Acids Research
Metabolic Reference Work
8Genome-scale metabolic models (GEMs) and related metabolic infrastructure — the data resources, not the AI applied to them.
Qiu, S., Kratochvilova, E., Huang, W., Cui, Z., Agnew, T., Yang, A., & Ye, H. (2026)
Proteome constrained metabolic modeling of. Sus scrofa
Gomez Romero, S., Vigliotti, M., Ramirez Lopez, V., Nguyen, K., Marchitto, V., & Boyle, N. (2026)
iSsus3744: A genome-scale model-guided strategy for rational media design for cultivated pork. bioRxiv
Lee, J., Kim, J., Bae, H., Kim, M., Jung, B., Kim, J., Lee, S., & Kim, H. (2024)
Multi-omics analysis and genome-scale metabolic reconstruction of cattle Bos taurus for optimal production of cultured meat. bioRxiv
Salehabadi, E., Motamedian, E., & Shojaosadati, S. A. (2022)
Reconstruction of a generic genome-scale metabolic network for chicken: Investigating network connectivity and finding potential biomarkers. PLOS ONE
Zakhartsev, M., Rotnes, F., Gulla, M., Øyås, O., van Dam, J. C. J., Suarez-Diez, M., Grammes, F., Hafþórsson, R., van Helvoirt, W., Koehorst, J. J., Schaap, P. J., Jin, Y., Mydland, L. T., Gjuvsland, A. B., Sandve, S. R., Martins dos Santos, V. A. P., & Vik, J. O. (2022)
SALARECON connects the Atlantic salmon genome to growth and feed efficiency. PLOS Computational Biology
Robinson, J. L., Kocabaş, P., Wang, H., Cholley, P.-E., Cook, D., Nilsson, A., Anton, M., Ferreira, R., Domenzain, I., Billa, V., Limeta, A., Hedin, A., Gustafsson, J., Kerkhoven, E. J., Svensson, L. T., Palsson, B. O., Mardinoglu, A., Hansson, L., Uhlén, M., & Nielsen, J. (2020)
An atlas of human metabolism. Science Signaling
Brunk, E., Sahoo, S., Zielinski, D. C., Altunkaya, A., Dräger, A., Mih, N., Gatto, F., Nilsson, A., Preciat Gonzalez, G. A., Aurich, M. K., Prlić, A., Sastry, A., Danielsdottir, A. D., Heinken, A., Noronha, A., Rose, P. W., Burley, S. K., Fleming, R. M. T., Nielsen, J., Thiele, I., & Palsson, B. O. (2018)
Recon3D enables a three-dimensional view of gene variation in human metabolism. Nature Biotechnology
Hefzi, H., Ang, K. S., Hanscho, M., Bordbar, A., Ruckerbauer, D., Lakshmanan, M., Orellana, C. A., Baycin-Hizal, D., Huang, Y., Ley, D., Martinez, V. S., Kyriakopoulos, S., Jiménez, N. E., Zielinski, D. C., Quek, L.-E., Wulff, T., Arnsdorf, J., Li, S., Lee, J. S., Paglia, G., Loira, N., Spahn, P. N., Pedersen, L. E., Gutierrez, J. M., King, Z. A., Lund, A. M., Nagarajan, H., Thomas, A., Abdel-Haleem, A. M., Zanghellini, J., Kildegaard, H. F., Voldborg, B. G., Gerdtzen, Z. P., Betenbaugh, M. J., Palsson, B. O., Andersen, M. R., Nielsen, L. K., Borth, N., Lee, D.-Y., & Lewis, N. E. (2016)
A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism. Cell Systems
Foundational Methods Reference Work
9Method and theory papers, from machine learning and cell biology, that underlie the matrix rows.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2023)
Attention Is All You Need. arXiv
Moses, L., & Pachter, L. (2022)
Museum of spatial transcriptomics. Nature Methods
Vijayakumar, S., Magazzù, G., Moon, P., Occhipinti, A., Angione, C., Cortassa, S., & Aon, M. A. (2022)
A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling. In. Computational Systems Biology in Medicine and Biotechnology: Methods and Protocols
Petrany, M. J., Swoboda, C. O., Sun, C., Chetal, K., Chen, X., Weirauch, M. T., Salomonis, N., & Millay, D. P. (2020)
Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers. Nature Communications
Neftci, E. O., & Averbeck, B. B. (2019)
Reinforcement learning in artificial and biological systems. Nature Machine Intelligence
Cuperlovic-Culf, M. (2018)
Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling. Metabolites
Grindberg, R. V., Yee-Greenbaum, J. L., McConnell, M. J., Novotny, M., O’Shaughnessy, A. L., Lambert, G. M., Araúzo-Bravo, M. J., Lee, J., Fishman, M., Robbins, G. E., Lin, X., Venepally, P., Badger, J. H., Galbraith, D. W., Gage, F. H., & Lasken, R. S. (2013)
RNA-sequencing from single nuclei. Proceedings of the National Academy of Sciences
Wang, J., Zhang, K., Xu, L., & Wang, E. (2011)
Quantifying the Waddington landscape and biological paths for development and differentiation. Proceedings of the National Academy of Sciences
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010)
What is flux balance analysis?. Nature Biotechnology
Livestock Functional Genomics Reference Work
12FarmGTEx and adjacent multi-tissue atlases and annotation resources for cell-ag-relevant livestock species.
Pan, X., Gong, W., Cai, X., Teng, J., Cai, J., Zeng, H., Ayalew, W., Shen, Q., Zhong, Z., Wang, Y., Zhang, W., Tian, Y., Xu, D., Gao, Y., Yin, H., Zhang, Y., Hou, J., Zhou, T., Li, J., Fang, L., Yuan, X., & Zhang, Z. (2026)
Multi-Tissue Genetic Regulation of RNA Editing in Pigs. Advanced Science
Chen, L., Li, H., Teng, J., Wang, Z., Qu, X., Chen, Z., Cai, X., Zeng, H., Bai, Z., Li, J., Pan, X., Yan, L., Wang, F., Lin, L., Luo, Y., Sahana, G., Lund, M. S., Ballester, M., Crespo-Piazuelo, D., Karlskov-Mortensen, P., Fredholm, M., Clop, A., Amills, M., Loving, C., Tuggle, C. K., Madsen, O., Li, J., Zhang, Z., Liu, G. E., Jiang, J., Fang, L., & Yi, G. (2026)
Construction of a Multitissue Cell Atlas Reveals Cell-Type-Specific Regulation of Molecular and Complex Phenotypes in Pigs. Advanced Science
Teng, J., Zhang, W., Gong, W., Chen, J., Gao, Y., Fang, L., & Zhang, Z. (2026)
OmiGA for ultra-efficient molecular quantitative trait loci mapping. Nature Communications
Fang, L., Teng, J., Lin, Q., Bai, Z., Liu, S., Guan, D., Li, B., Gao, Y., Hou, Y., Gong, M., Pan, Z., Yu, Y., Clark, E. L., Smith, J., Rawlik, K., Xiang, R., Chamberlain, A. J., Goddard, M. E., Littlejohn, M., ... Liu, G. E. (2025)
The Farm Animal Genotype–Tissue Expression (FarmGTEx) Project. Nature Genetics
Guan, D., Bai, Z., Zhu, X., Zhong, C., Hou, Y., Zhu, D., Li, H., Lan, F., Diao, S., Yao, Y., Zhao, B., Li, X., Pan, Z., Gao, Y., Wang, Y., Zou, D., Wang, R., Xu, T., Sun, C., ... Fang, L. (2025)
Genetic regulation of gene expression across multiple tissues in chickens. Nature Genetics
Han, B., Li, H., Zheng, W., Zhang, Q., Chen, A., Zhu, S., Shi, T., Wang, F., Zou, D., Song, Y., Ye, W., Du, A., Fu, Y., Jia, M., Bai, Z., Yuan, Z., Liu, W., Tuo, W., Hope, J. C., MacHugh, D. E., O'Grady, J. F., Madsen, O., Sahana, G., Luo, Y., Lin, L., Li, C., Cai, Z., Li, B., Huang, J., Liu, L., Zhang, Z., Ma, Z., Hou, Y., Liu, G. E., Jiang, Y., Sun, H. Z., Fang, L., & Sun, D. (2025)
A multi-tissue single-cell expression atlas in cattle. Nature Genetics
Gong, M., Zhuang, Z., Sun, X., Xu, Y., Zhang, H., Wang, Y., Guan, D., Li, R., Lu, X., Bai, Z., Feng, P., Song, M., Tian, M., Lu, J., Wang, M., Lu, X., Wu, D., Su, P., Liu, P., ... Fang, L. (2025)
A multi-tissue atlas of genetic regulatory effects in sheep. bioRxiv
Xu, Z., Lin, Q., Cai, X., Zhong, Z., Teng, J., Li, B., Zeng, H., Gao, Y., Cai, Z., Wang, X., Shi, L., Wang, X., Wang, Y., Zhang, Z., Lin, Y., Liu, S., Yin, H., Bai, Z., Wei, C., ... Zhang, Z. (2025)
Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of complex traits in the pig. National Science Review
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