Key Takeaways Hyperspectral imaging captures extensive spectral data, enabling precise material and tissue differentiation beyond conventional imaging capabilities. Applications span counterfeit detection, environmental monitoring, agriculture, food quality, and medical diagnostics, with significant accuracy improvements. AI and deep learning enhance HSI's analytical potential, addressing challenges like high costs and complex data analysis. The convergence of AI
Advancing biological taxonomy in the AI era: deep learning applications, challenges, and …
References
-
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A.J., Bambrick, J., et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500.
Article CAS PubMed PubMed Central Google Scholar
-
Adjei, K.P., Finstad, A.G., Koch, W., and O’Hara, R.B. (2024). Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance. Ecol Evol 14, 11092.
Article Google Scholar
-
Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaria, J., Fadhel, M.A., Al-Amidie, M., and Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53.
Article PubMed PubMed Central Google Scholar
-
Anonymous. (1860). On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life/by Charles Darwin. Br Foreign Med Chir Rev 25, 367–404.
Google Scholar
-
Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025). AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model. bioRxiv 661532.
Google Scholar
-
Babjac, A.N., Lu, Z., and Emrich, S.J. (2023). CodonBERT: using BERT for sentiment analysis to better predict genes with low expression. In Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (Houston: Association for Computing Machinery), pp. 29.
Google Scholar
-
Barroso, V.R., Xavier, F.C., Ferreira, C.E.L., and Browman, H. (2023). Applications of machine learning to identify and characterize the sounds produced by fish. ICES J Mar Sci 80, 1854–1867.
Article Google Scholar
-
Barta, Z. (2023). Deep learning in terrestrial conservation biology. Biol Futura 74, 359–367.
Article Google Scholar
-
Benegas, G., Albors, C., Aw, A.J., Ye, C., and Song, Y.S. (2025a). A DNA language model based on multispecies alignment predicts the effects of genome-wide variants. Nat Biotechnol doi: https://doi.org/10.1038/s41587-024-02511-w.
Google Scholar
-
Benegas, G., Eraslan, G., and Song, Y.S. (2025b). Benchmarking DNA sequence models for causal regulatory variant prediction in human genetics. bioRxiv 637758.
Book Google Scholar
-
Benegas, G., Ye, C., Albors, C., Li, J.C., and Song, Y.S. (2025c). Genomic language models: opportunities and challenges. Trends Genet 41, 286–302.
Article CAS PubMed Google Scholar
-
Bento, M., Niza, H., Cartaxana, A., Bandeira, S., Paula, J., and Correia, A.M. (2023). Mind the gaps: taxonomic, geographic and temporal data of marine invertebrate databases from Mozambique and Sao Tome and Principe. Diversity 15, 70.
Article Google Scholar
-
Bi, X., Wang, K., Yang, L., Pan, H., Jiang, H., Wei, Q., Fang, M., Yu, H., Zhu, C., Cai, Y., et al. (2021). Tracing the genetic footprints of vertebrate landing in non-teleost ray-finned fishes. Cell 184, 1377–1391.e14.
Article CAS PubMed Google Scholar
-
Boiko, D.A., MacKnight, R., Kline, B., and Gomes, G. (2023). Autonomous chemical research with large language models. Nature 624, 570–578.
Article CAS PubMed PubMed Central Google Scholar
-
Borowiec, M.L., Dikow, R.B., Frandsen, P.B., McKeeken, A., Valentini, G., and White, A.E. (2022). Deep learning as a tool for ecology and evolution. Methods Ecol Evol 13, 1640–1660.
Article Google Scholar
-
Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., Linial, M., and Martelli, P.L. (2022). ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics 38, 2102–2110.
Article CAS PubMed PubMed Central Google Scholar
-
Brixi, G., Durrant, M.G., Ku, J., Poli, M., Brockman, G., Chang, D., Gonzalez, G.A., King, S.H., Li, D.B., Merchant, A.T., et al. (2025). Genome modeling and design across all domains of life with Evo 2. bioRxiv 638918.
Book Google Scholar
-
Chai, J., Zeng, H., Li, A., and Ngai, E.W.T. (2021). Deep learning in computer vision: a critical review of emerging techniques and application scenarios. Machine Learn Appl 6, 100134.
Google Scholar
-
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., and Han, J.D.J. (2023). Transformer for one stop interpretable cell type annotation. Nat Commun 14, 223.
Article CAS PubMed PubMed Central Google Scholar
-
Chen, L., Li, G., Zhang, S., Mao, W., and Zhang, M. (2024a). YOLO-SAG: an improved wildlife object detection algorithm based on YOLOv8n. Ecol Inf 83, 102791.
Article Google Scholar
-
Chen, L., Qiu, Q., Jiang, Y., Wang, K., Lin, Z., Li, Z., Bibi, F., Yang, Y., Wang, J., Nie, W., et al. (2019). Large-scale ruminant genome sequencing provides insights into their evolution and distinct traits. Science 364, eaav6202.
Article CAS PubMed Google Scholar
-
Chen, Z., Ain, N., Zhao, Q., and Zhang, X. (2024b). From tradition to innovation: conventional and deep learning frameworks in genome annotation. Brief Bioinf 25, bbae138.
Article CAS Google Scholar
-
Chin, S.Y., Dong, J., Hasikin, K., Ngui, R., Lai, K.W., Yeoh, P.S.Q., and Wu, X. (2024). Bacterial image analysis using multi-task deep learning approaches for clinical microscopy. PeerJ Comput Sci 10, e2180.
Article PubMed PubMed Central Google Scholar
-
Cinar, I., and Taspinar, Y.S. (2023). Detection of fungal infections from microscopic fungal images using deep learning techniques. ICAT: ICAT23.
Book Google Scholar
-
Ciranni, M., Murino, V., Odone, F., and Pastore, V.P. (2024). Computer vision and deep learning meet plankton: milestones and future directions. Image Vision Comput 143, 104934.
Article Google Scholar
-
Cordier, T., Forster, D., Dufresne, Y., Martins, C.I.M., Stoeck, T., and Pawlowski, J. (2018). Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring. Mol Ecol Resour 18, 1381–1391.
Article CAS PubMed Google Scholar
-
Cordier, T., Lanzén, A., Apothéloz-Perret-Gentil, L., Stoeck, T., and Pawlowski, J. (2019). Embracing environmental genomics and machine learning for routine biomonitoring. Trends Microbiol 27, 387–397.
Article CAS PubMed Google Scholar
-
Cui, S., Gao, Y., Huang, Y., Shen, L., Zhao, Q., Pan, Y., and Zhuang, S. (2023). Advances and applications of machine learning and deep learning in environmental ecology and health. Environ Pollut 335, 122358.
Article CAS PubMed Google Scholar
-
Dalla-Torre, H., Gonzalez, L., Mendoza-Revilla, J., Lopez Carranza, N., Grzywaczewski, A.H., Oteri, F., Dallago, C., Trop, E., de Almeida, B.P., Sirelkhatim, H., et al. (2025). Nucleotide Transformer: building and evaluating robust foundation models for human genomics. Nature Methods 22, 287–297.
Article CAS PubMed Google Scholar
-
de Medeiros, B.A.S., Cai, L., Flynn, P.J., Yan, Y., Duan, X., Marinho, L.C., Anderson, C., and Davis, C.C. (2025). A composite universal DNA signature for the tree of life. Nat Ecol Evol 9, 1426–1440.
Article PubMed PubMed Central Google Scholar
-
Ditria, E.M., Lopez-Marcano, S., Sievers, M., Jinks, E.L., Brown, C.J., and Connolly, R. M. (2020). Automating the analysis of fish abundance using object detection: optimizing animal ecology with deep learning. Front Mar Sci 7, 429.
Article Google Scholar
-
Dong, X., Yan, N., and Wei, Y. (2018). Insect sound recognition based on convolutional neural network. In Proceedings of 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) (Chongqing: China). 855–859.
Chapter Google Scholar
-
Erfanian, N., Heydari, A.A., Feriz, A.M., Iañez, P., Derakhshani, A., Ghasemigol, M., Farahpour, M., Razavi, S.M., Nasseri, S., Safarpour, H., et al. (2023). Deep learning applications in single-cell genomics and transcriptomics data analysis. Biomed Pharmacother 165, 115077.
Article CAS PubMed Google Scholar
-
Feng, C., Tang, Y., Liu, S., Tian, F., Zhang, C., and Zhao, K. (2019). Multiple convergent events created a nominal widespread species: triplophysa stoliczkae (Steindachner, 1866) (Cobitoidea: Nemacheilidae). BMC Evol Biol 19, 177.
Article PubMed PubMed Central Google Scholar
-
Feng, C., Wang, K., Xu, W., Yang, L., Wanghe, K., Sun, N., Wu, B., Wu, F., Yang, L., Qiu, Q., et al. (2023). Monsoon boosted radiation of the endemic East Asian carps. Sci China Life Sci 66, 563–578.
Article PubMed Google Scholar
-
Fernandez Garcia, G., Corpetti, T., Nevoux, M., Beaulaton, L., and Martignac, F. (2023). AcousticIA, a deep neural network for multi-species fish detection using multiple models of acoustic cameras. Aquat Ecol 57, 881–893.
Article Google Scholar
-
Fišer, C., Robinson, C.T., and Malard, F. (2018). Cryptic species as a window into the paradigm shift of the species concept. Mol Ecol 27, 613–635.
Article PubMed Google Scholar
-
Fishman, V., Kuratov, Y., Shmelev, A., Petrov, M., Penzar, D., Shepelin, D., Chekanov, N., Kardymon, O., and Burtsev, M. (2025). GENA-LM: a family of open-source foundational DNA language models for long sequences. Nucleic Acids Res 53, gkae1310.
Article CAS PubMed PubMed Central Google Scholar
-
Flück, B., Mathon, L., Manel, S., Valentini, A., Dejean, T., Albouy, C., Mouillot, D., Thuiller, W., Murienne, J., Brosse, S., et al. (2022). Applying convolutional neural networks to speed up environmental DNA annotation in a highly diverse ecosystem. Sci Rep 12, 10247.
Article PubMed PubMed Central Google Scholar
-
Frühe, L., Cordier, T., Dully, V., Breiner, H., Lentendu, G., Pawlowski, J., Martins, C., Wilding, T.A., and Stoeck, T. (2021). Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes. Mol Ecol 30, 2988–3006.
Article PubMed Google Scholar
-
Fu, X., Jiang, J., Wu, X., Huang, L., Han, R., Li, K., Liu, C., Roy, K., Chen, J., Mahmoud, N.T.A., et al. (2024). Deep learning in water protection of resources, environment, and ecology: achievement and challenges. Environ Sci Pollut Res 31, 14503–14536.
Article Google Scholar
-
Gao, Y., Yin, F., Hong, C., Chen, X., Deng, H., Liu, Y., Li, Z., and Yao, Q. (2024). Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky trap images and an improved Cascade R-CNN. J Integr Agr doi: https://doi.org/10.1016/j.jia.2024.06.017.
Google Scholar
-
Ge, S., Sun, S., Xu, H., Cheng, Q., and Ren, Z. (2025). Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective. Brief Bioinf 26, bbaf136.
Article Google Scholar
-
Gemma Team. (2025). Gemma 3 technical report. arXiv: 2503.19786.
Google Scholar
-
Godfray, H.C.J., Knapp, S., and Wilson, E.O. (2004). Taxonomy as a fundamental discipline. Phil Trans R Soc Lond B 359, 739.
Article Google Scholar
-
Grešová, K., Martinek, V., Čechák, D., Šimeček, P., and Alexiou, P. (2023). Genomic benchmarks: a collection of datasets for genomic sequence classification. BMC Genom Data 24, 25.
Article PubMed PubMed Central Google Scholar
-
Guo, D., Yang, D., Zhang, H., Song, J., Wang, P., Zhu, Q., Xu, R., Zhang, R., Ma, S., Bi, X., et al. (2025). DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning. Nature 645, 633–638.
Article CAS PubMed PubMed Central Google Scholar
-
Guo, F., Guan, R., Li, Y., Liu, Q., Wang, X., Yang, C., and Wang, J. (2025). Foundation models in bioinformatics. Natl Sci Rev 12, nwaf028.
Article CAS PubMed PubMed Central Google Scholar
-
Hayes, T., Rao, R., Akin, H., Sofroniew, N.J., Oktay, D., Lin, Z., Verkuil, R., Tran, V.Q., Deaton, J., Wiggert, M., et al. (2025). Simulating 500 million years of evolution with a language model. Science 387, 850–858.
Article CAS PubMed Google Scholar
-
He, Y., Fang, P., Shan, Y., Pan, Y., Wei, Y., Chen, Y., Chen, Y., Liu, Y., Zeng, Z., Zhou, Z., et al. (2025). Generalized biological foundation model with unified nucleic acid and protein language. Nat Mach Intell 7, 942–953.
Article Google Scholar
-
He, Y., Mulqueeney, J.M., Watt, E.C., Salili-James, A., Barber, N.S., Camaiti, M., Hunt, E.S.E., Kippax-Chui, O., Knapp, A., Lanzetti, A., et al. (2024). Opportunities and challenges in applying AI to evolutionary morphology. Integr Organismal Biol 6, obae036.
Article CAS Google Scholar
-
Hebert, P.D.N., Cywinska, A., Ball, S.L., and deWaard, J.R. (2003). Biological identifications through DNA barcodes. Proc R Soc Lond B 270, 313–321.
Article CAS Google Scholar
-
Helaly, M.A., Rady, S., and Aref, M.M. (2022). BERT contextual embeddings for taxonomic classification of bacterial DNA sequences. Expert Syst Appl 208, 117972.
Article Google Scholar
-
Horn, G.V., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., and Belongie, S.J. (2017). The iNaturalist species classification and detection dataset. CVPR 2017, 8769–8778.
Google Scholar
-
Hou, X., He, Y., Fang, P., Mei, S.Q., Xu, Z., Wu, W.C., Tian, J.H., Zhang, S., Zeng, Z.Y., Gou, Q.Y., et al. (2024). Using artificial intelligence to document the hidden RNA virosphere. Cell 187, 6929–6942.e16.
Article CAS PubMed Google Scholar
-
Hu, H., Wei, X.Y., Liu, L., Wang, Y.B., Jia, H.J., Bu, L.K., and Pei, D.S. (2023). Supervised machine learning improves general applicability of eDNA metabarcoding for reservoir health monitoring. Water Res 246, 120686.
Article CAS PubMed Google Scholar
-
Hu, W.C., Wu, H.T., Zhang, Y.F., Zhang, S.H., and Lo, C.H. (2020). Shrimp recognition using ShrimpNet based on convolutional neural network. J Ambient Intell Hum Comput doi: https://doi.org/10.1007/s12652-020-01727-3.
Google Scholar
-
Ibrahim, A.K., Zhuang, H., Schärer-Umpierre, M., Woodward, C., Erdol, N., and Chérubin, L.M. (2024). Fish Acoustic Detection Algorithm Research: a deep learning app for Caribbean grouper calls detection and call types classification. Front Mar Sci 11, 1378159.
Article Google Scholar
-
Jain, S. (2023). DeepSeaNet: improving underwater object detection using efficientDet. In Proceeding of 2024 4th International Conference on Applied Artificial Intelligence (ICAPAI) (Halden: Norway). 1–11.
Google Scholar
-
Jan, M., Spangaro, A., Lenartowicz, M., and Mattiazzi Usaj, M. (2024). From pixels to insights: machine learning and deep learning for bioimage analysis. Bioessays 46, 2300114.
Article Google Scholar
-
Ji, Y., Zhou, Z., Liu, H., Davuluri, R.V., and Kelso, J. (2021). DNABERT: pre-trained bidirectional encoder representations from transformers model for DNA-language in genome. Bioinformatics 37, 2112–2120.
Article CAS PubMed PubMed Central Google Scholar
-
Jin, L., Yu, J., Yuan, X., and Du, X. (2021). Fish classification using DNA barcode sequences through deep learning method. Symmetry 13, 1599.
Article CAS Google Scholar
-
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589.
Article CAS PubMed PubMed Central Google Scholar
-
Kahl, S., Wood, C.M., Eibl, M., and Klinck, H. (2021). BirdNET: a deep learning solution for avian diversity monitoring. Ecol Inf 61, 101236.
Article Google Scholar
-
Karbstein, K., Kösters, L., Hodač, L., Hofmann, M., Horandl, E., Tomasello, S., Wagner, N.D., Emerson, B.C., Albach, D.C., Scheu, S., et al. (2024). Species delimitation 4.0: integrative taxonomy meets artificial intelligence. Trends Ecol Evol 39, 771–784.
Article PubMed Google Scholar
-
Khan, A., Rauf, Z., Sohail, A., Rehman, A., Asif, H., Asif, A., and Farooq, U. (2023). A survey of the Vision Transformers and their CNN-Transformer based Variants. arXiv: 2305.09880.
Book Google Scholar
-
Korkmaz, A.F., Ekinci, F., Altaş, Ş., Kumru, E., Güzel, M.S., and Akata, I. (2025). A deep learning and explainable AI-based approach for the classification of discomycetes species. Biology 14, 719.
Article PubMed PubMed Central Google Scholar
-
Kress, W.J., García-Robledo, C., Uriarte, M., and Erickson, D.L. (2015). DNA barcodes for ecology, evolution, and conservation. Trends Ecol Evol 30, 25–35.
Article PubMed Google Scholar
-
Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. Commun ACM 60, 84–90.
Article Google Scholar
-
Lam, H.Y.I., Ong, X.E., and Mutwil, M. (2024). Large language models in plant biology. Trends Plant Sci 29, 1145–1155.
Article CAS PubMed Google Scholar
-
Lamperti, L., Sanchez, T., Si Moussi, S., Mouillot, D., Albouy, C., Flück, B., Bruno, M., Valentini, A., Pellissier, L., and Manel, S. (2023). New deep learning-based methods for visualizing ecosystem properties using environmental DNA metabarcoding data. Mol Ecol Resour 23, 1946–1958.
Article PubMed Google Scholar
-
Laplante, J.F., Akhloufi, M.A., and Gervaise, C. (2022). Deep learning for marine bioacoustics and fish classification using underwater sounds. In Proceeding of 2022 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (Halifax: Canada). 288–293.
Chapter Google Scholar
-
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., Kang, J., and Wren, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36, 1234–1240.
Article CAS PubMed Google Scholar
-
Li, D., Wang, Q., Li, X., Niu, M., Wang, H., Liu, C., and Beyan, C. (2022). Recent advances of machine vision technology in fish classification. ICES J Mar Sci 79, 263–284.
Article Google Scholar
-
Li, Q., Hu, Z., Wang, Y., Li, L., Fan, Y., King, I., Jia, G., Wang, S., Song, L., and Li, Y. (2024a). Progress and opportunities of foundation models in bioinformatics. Brief BioInf 25, bbae548.
Article CAS Google Scholar
-
Li, X., Li, F., Min, X., Xie, Y., and Zhang, Y. (2023). Embracing eDNA and machine learning for taxonomy-free microorganisms biomonitoring to assess the river ecological status. Ecol Indic 155, 110948.
Article Google Scholar
-
Li, Y., Tang, M., Lu, S., Zhang, X., Fang, C., Tan, L., Xiong, F., Zeng, H., and He, S. (2024b). A comparative evaluation of eDNA metabarcoding primers in fish community monitoring in the East Lake. Water 16, 631.
Article Google Scholar
-
Linnaeus, C. (1759). Systema Naturae, Vol 2, 10th edn (Stockholm: Laurentius Salvius).
Google Scholar
-
Liu, X., Zeng, H., Wang, C., Bo, J., Gan, X., Fang, C., and He, S. (2022). Improved genome assembly of Chinese sucker (Myxocyprinus asiaticus) provides insights into the identification and characterization of pharyngeal teeth related maker genes in Cyprinoidei. Water Biol Security 1, 100049.
Article Google Scholar
-
Lopez, R., Regier, J., Cole, M.B., Jordan, M.I., and Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nat Methods 15, 1053–1058.
Article CAS PubMed PubMed Central Google Scholar
-
Lu, C., Lu, C., Lange, R.T., Foerster, J.N., Clune, J., and Ha, D. (2024a). The AI Scientist: towards fully automated open-ended scientific discovery. arXiv: 2408.06292.
Google Scholar
-
Lu, S., Schneider, I., Zeng, H., and He, S. (2023). The use of single-cell sequencing to reveal stem/progenitor cells in animal organ regeneration. Water Biol Security 2, 100081.
Article Google Scholar
-
Lu, S., Zeng, H., Xiong, F., Yao, M., and He, S. (2024b). Advances in environmental DNA monitoring: standardization, automation, and emerging technologies in aquatic ecosystems. Sci China Life Sci 67, 1368–1384.
Article CAS PubMed Google Scholar
-
Lu, Z., Zhu, X., Guo, H., Xie, X., Chen, X., and Quan, X. (2024c). FishFocusNet: an improved method based on YOLOv8 for underwater tropical fish identification. IET Image Process 18, 3634–3649.
Article Google Scholar
-
Mahale, V.P., Chanda, K., Chakraborty, B., Salkar, T., and Sreekanth, G.B. (2023). Biodiversity assessment using passive acoustic recordings from off-reef location—unsupervised learning to classify fish vocalization. J Acoust Soc Am 153, 1534–1553.
Article PubMed Google Scholar
-
Mancusi, M., Zonca, N., Rodolà, E., and Zuffi, S. (2023). Towards the evaluation of marine acoustic biodiversity through data-driven audio source separation. In Proceeding of 2023 Immersive and 3D Audio: from Architecture to Automotive (I3DA) (Bologna: Italy). 1–10.
Google Scholar
-
Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Erckert, K., Bernhofer, M., Nechaev, D., and Rost, B. (2022). Embeddings from protein language models predict conservation and variant effects. Hum Genet 141, 1629–1647.
Article CAS PubMed Google Scholar
-
Meher, P.K., Sahu, T.K., Gahoi, S., Tomar, R., and Rao, A.R. (2019). funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model. BMC Genet 20, 2.
Article PubMed PubMed Central Google Scholar
-
Mendoza-Revilla, J., Trop, E., Gonzalez, L., Roller, M., Dalla-Torre, H., de Almeida, B. P., Richard, G., Caton, J., Lopez Carranza, N., Skwark, M., et al. (2024). A foundational large language model for edible plant genomes. Commun Biol 7, 835.
Article CAS PubMed PubMed Central Google Scholar
-
Miller, T., Michoński, G., Durlik, I., Kozlovska, P., and Biczak, P. (2025). Artificial intelligence in aquatic biodiversity research: a PRISMA-based systematic review. Biology 14, 520.
Article PubMed PubMed Central Google Scholar
-
Misof, B., Liu, S., Meusemann, K., Peters, R.S., Donath, A., Mayer, C., Frandsen, P.B., Ware, J., Flouri, T., Beutel, R.G., et al. (2014). Phylogenomics resolves the timing and pattern of insect evolution. Science 346, 763–767.
Article CAS PubMed Google Scholar
-
Mock, F., Kretschmer, F., Kriese, A., Bocker, S., and Marz, M. (2022). Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks. Proc Natl Acad Sci USA 119, e2122636119.
Article CAS PubMed PubMed Central Google Scholar
-
Moor, M., Banerjee, O., Abad, Z.S.H., Krumholz, H.M., Leskovec, J., Topol, E.J., and Rajpurkar, P. (2023). Foundation models for generalist medical artificial intelligence. Nature 616, 259–265.
Article CAS PubMed Google Scholar
-
Nguyen, E., Poli, M., Durrant, M.G., Kang, B., Katrekar, D., Li, D.B., Bartie, L.J., Thomas, A.W., King, S.H., Brixi, G., et al. (2024). Sequence modeling and design from molecular to genome scale with Evo. Science 386, eado9336.
Article CAS PubMed PubMed Central Google Scholar
-
Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., and Madani, A. (2023). ProGen2: exploring the boundaries of protein language models. Cell Syst 14, 968–978.e3.
Article CAS PubMed Google Scholar
-
Oldenburg, E., Kronberg, R.M., Niehoff, B., Ebenhoh, O., and Popa, O. (2023). DeepLOKI—a deep learning based approach to identify zooplankton taxa on high-resolution images from the optical plankton recorder LOKI. Front Mar Sci 10, 1280510.
Article Google Scholar
-
OpenAi, El-Kishky, A., Wei, A., Saraiva, A., Minaiev, B., Selsam, D., Dohan, D., Song, F., Lightman, H., Clavera, I., et al. (2025). Competitive programming with large reasoning models. arXiv: 2502.06807.
Google Scholar
-
Orr, M.C., Ferrari, R.R., Hughes, A.C., Chen, J., Ascher, J.S., Yan, Y.H., Williams, P.H., Zhou, X., Bai, M., Rudoy, A., et al. (2021). Taxonomy must engage with new technologies and evolve to face future challenges. Nat Ecol Evol 5, 3–4.
Article PubMed Google Scholar
-
Palanisamy, V., and Ratnarajah, N. (2021). Detection of wildlife animals using deep learning approaches: a systematic review. In Proceeding of 2021 21st International Conference on Advances in ICT for Emerging Regions (ICter) (Colombo: Sri Lanka). pp. 153–158.
Google Scholar
-
Pearson, D.L., Hamilton, A.L., and Erwin, T.L. (2011). Recovery plan for the endangered taxonomy profession. BioScience 61, 58–63.
Article Google Scholar
-
Petersen, T.K., Speed, J.D.M., Grøtan, V., and Austrheim, G. (2021). Species data for understanding biodiversity dynamics: the what, where and when of species occurrence data collection. Ecol Sol Evid 2, e12048.
Article Google Scholar
-
Pichler, M., and Hartig, F. (2023). Machine learning and deep learning—a review for ecologists. Methods Ecol Evol 14, 994–1016.
Article Google Scholar
-
Popescu, D., Dinca, A., Ichim, L., and Angelescu, N. (2023). New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review. Front Plant Sci 14, 1268167.
Article PubMed Google Scholar
-
Przymus, P., Rykaczewski, K., Martín-Segura, A., Truu, J., Carrillo De Santa Pau, E., Kolev, M., Naskinova, I., Gruca, A., Sampri, A., Frohme, M., et al. (2025). Deep learning in microbiome analysis: a comprehensive review of neural network models. Front Microbiol 15, 1516667.
Article PubMed PubMed Central Google Scholar
-
Rasmussen, J.H., and Širović, A. (2021). Automatic detection and classification of baleen whale social calls using convolutional neural networks. J Acoust Soc Am 149, 3635–3644.
Article PubMed Google Scholar
-
Rasmussen, J.H., Stowell, D., and Briefer, E.F. (2024). Sound evidence for biodiversity monitoring. Science 385, 138–140.
Article CAS PubMed Google Scholar
-
Robinson, D., Robinson, A., and Akrapongpisak, L. (2024). Transferable models for bioacoustics with human language supervision. In Proceeding of ICASSP 2024–2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Seoul: Republic of Korea). pp. 1316–1320.
Google Scholar
-
Roy, G., Prifti, E., Belda, E., and Zucker, J.D. (2024). Deep learning methods in metagenomics: a review. Microb Genomics 10.
Google Scholar
-
Rubbens, P., Brodie, S., Cordier, T., Destro Barcellos, D., Devos, P., Fernandes-Salvador, J.A., Fincham, J.I., Gomes, A., Handegard, N.O., Howell, K., et al. (2023). Machine learning in marine ecology: an overview of techniques and applications. ICES J Mar Sci 80, 1829–1853.
Article Google Scholar
-
Rutz, C., Bronstein, M., Raskin, A., Vernes, S.C., Zacarian, K., and Blasi, D.E. (2023). Using machine learning to decode animal communication. Science 381, 152–155.
Article CAS PubMed Google Scholar
-
Sadad, T., Aurangzeb, R.A., Safran, M., Imran, M., Alfarhood, S., and Kim, J. (2023). Classification of highly divergent viruses from DNA/RNA sequence using transformer-based models. Biomedicines 11, 1323.
Article CAS PubMed PubMed Central Google Scholar
-
Saleh, A., Sheaves, M., and Rahimi Azghadi, M. (2022). Computer vision and deep learning for fish classification in underwater habitats: a survey. Fish Fisheries 23, 977–999.
Article Google Scholar
-
Sarker, I.H. (2021). Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. Sn Comput Sci 2, 420.
Article Google Scholar
-
Sattar, F. (2023). A new acoustical autonomous method for identifying endangered whale calls: a case study of blue whale and fin whale. Sensors 23, 3048.
Article PubMed PubMed Central Google Scholar
-
Shirai, M., Takano, A., Kurosawa, T., Inoue, M., Tagane, S., Tanimoto, T., Koganeyama, T., Sato, H., Terasawa, T., Horie, T., et al. (2022). Development of a system for the automated identification of herbarium specimens with high accuracy. Sci Rep 12, 8066.
Article CAS PubMed PubMed Central Google Scholar
-
Shiu, Y., Palmer, K.J., Roch, M.A., Fleishman, E., Liu, X., Nosal, E.M., Helble, T., Cholewiak, D., Gillespie, D., and Klinck, H. (2020). Deep neural networks for automated detection of marine mammal species. Sci Rep 10, 607.
Article CAS PubMed PubMed Central Google Scholar
-
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al. (2023). Towards expert-level medical question answering with large language models. arXiv: 2305.09617.
Google Scholar
-
Sohsah, G.N., Ibrahimzada, A.R., Ayaz, H., and Cakmak, A. (2020). Scalable classification of organisms into a taxonomy using hierarchical supervised learners. J Bioinform Comput Biol 18, 2050026.
Article CAS PubMed Google Scholar
-
Stevens, S., Wu, J., Thompson, M.J., Campolongo, E.G., Song, C.H., Carlyn, D.E., Dong, L., Dahdul, W.M., Stewart, C., Berger-Wolf, T., Chao, W.L., and Su, Y. (2024). BioCLIP: a vision foundation model for the tree of life. In Proceeding of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Seattle: USA). pp. 19412–19424.
Google Scholar
-
Stowell, D. (2022). Computational bioacoustics with deep learning: a review and roadmap. PeerJ 10, e13152.
Article PubMed PubMed Central Google Scholar
-
Sun, S., An, W., Tian, F., Nan, F., Liu, Q., Liu, J., Shah, N., and Chen, P. (2024a). A review of multimodal explainable artificial intelligence: past, present and future. arXiv: 2412.14056.
Google Scholar
-
Sun, Y., Xin, H., Sun, K., Xu, Y.E., Yang, X., Luna Dong, X., Tang, N., and Chen, L. (2024b). Are large language models a good replacement of taxonomies? Proc VLDB Endow 17, 3609–3622.
Article Google Scholar
-
Thalpage, N. (2023). Unlocking the black box: explainable artificial intelligence (XAI) for trust and transparency in AI systems. JDAH 4, 31–36.
Article Google Scholar
-
Theodoris, C.V., Xiao, L., Chopra, A., Chaffin, M.D., Al Sayed, Z.R., Hill, M.C., Mantineo, H., Brydon, E.M., Zeng, Z., Liu, X.S., et al. (2023). Transfer learning enables predictions in network biology. Nature 618, 616–624.
Article CAS PubMed PubMed Central Google Scholar
-
Thomson, S.A., Pyle, R.L., Ahyong, S.T., Alonso-Zarazaga, M., Ammirati, J., Araya, J. F., Ascher, J.S., Audisio, T.L., Azevedo-Santos, V.M., Bailly, N., et al. (2018). Taxonomy based on science is necessary for global conservation. PLoS Biol 16, e2005075.
Article PubMed PubMed Central Google Scholar
-
Valdecasas, A.G. (2024). Can taxonomists think? Reversing the AI equation. Taxonomy 4, 713–722.
Article Google Scholar
-
Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. NeurIPS 2017.
Google Scholar
-
Vega-Sánchez, Y.M., Mendoza-Cuenca, L., and González-Rodríguez, A. (2022). Morphological variation and reproductive isolation in the Hetaerina americana species complex. Sci Rep 12, 10888.
Article PubMed PubMed Central Google Scholar
-
Villon, S., Mouillot, D., Chaumont, M., Darling, E.S., Subsol, G., Claverie, T., and Villéger, S. (2018). A Deep learning method for accurate and fast identification of coral reef fishes in underwater images. Ecol Inf 48, 238–244.
Article Google Scholar
-
Wang, K., Shen, Y., Yang, Y., Gan, X., Liu, G., Hu, K., Li, Y., Gao, Z., Zhu, L., Yan, G., et al. (2019). Morphology and genome of a snailfish from the Mariana Trench provide insights into deep-sea adaptation. Nat Ecol Evol 3, 823–833.
Article PubMed Google Scholar
-
Wang, Q., Song, Y., Du, Y., Yang, Z., Cui, P., and Luo, B. (2024). Hierarchical-taxonomy-aware and attentional convolutional neural networks for acoustic identification of bird species: a phylogenetic perspective. Ecol Inf 80, 102538.
Article Google Scholar
-
Wang, X., Wang, Z., Weng, H., Guo, H., Zhang, Z., Jin, L., Wei, T., and Ren, K. (2023). Counterfactual-based Saliency Map: towards visual contrastive explanations for neural networks. In Proceeding of 2023 IEEE/CVF International Conference on Computer Vision (ICCV) (Paris: France). pp. 2042–2051.
Chapter Google Scholar
-
Weitschek, E., Fiscon, G., and Felici, G. (2014). Supervised DNA Barcodes species classification: analysis, comparisons and results. Biodata Min 7, 4.
Article Google Scholar
-
Weitschek, E., Van Velzen, R., Felici, G., and Bertolazzi, P. (2013). BLOG 2.0: a software system for character-based species classification with DNA Barcode sequences. What it does, how to use it. Mol Ecol Resour 13, 1043–1046.
Article PubMed Google Scholar
-
Wichmann, A., Buschong, E., Müller, A., Jünger, D., Hildebrandt, A., Hankeln, T., and Schmidt, B. (2023). MetaTransformer: deep metagenomic sequencing read classification using self-attention models. NAR Genomics Bioinf 5, lqad082.
Article Google Scholar
-
Wu, J., Ji, W., Liu, Y., Fu, H., Xu, M., Xu, Y., and Jin, Y. (2023). Medical SAM adapter: adapting segment anything model for medical image segmentation. arXiv: 2304.12620.
Google Scholar
-
Xie, J., Zhong, Y., Zhang, J., Liu, S., Ding, C., and Triantafyllopoulos, A. (2023). A review of automatic recognition technology for bird vocalizations in the deep learning era. Ecol Inf 73, 101927.
Article Google Scholar
-
Xiong, F., Shu, L., Zeng, H., Gan, X., He, S., and Peng, Z. (2022). Methodology for fish biodiversity monitoring with environmental DNA metabarcoding: the primers, databases and bioinformatic pipelines. Water Biol Secur 1, 100007.
Article Google Scholar
-
Xu, H., Fang, C., Xu, W., Wang, C., Song, Y., Zhu, C., Fang, W., Fan, G., Lv, W., Bo, J., et al. (2025). Evolution and genetic adaptation of fishes to the deep sea. Cell 188, 1393–1408.e13.
Article CAS PubMed Google Scholar
-
Xu, J., Zhou, D., Deng, D., Li, J., Chen, C., Liao, X., Chen, G., and Heng, P.A. (2022). Deep learning in cell image analysis. Intell Comput 2022, 2022/9861263.
Article Google Scholar
-
Xu, M., Yuan, X., Miret, S., and Tang, J. (2023). ProtST: multi-modality learning of protein sequences and biomedical texts. arXiv: 2301.12040.
Google Scholar
-
Xue, Z., Wu, L., Tian, R., Gao, B., Zhao, Y., He, B., Sun, D., Zhao, B., Li, Y., Zhu, K., et al. (2025). Integrative mapping of human CD8 + T cells in inflammation and cancer. Nat Methods 22, 435–445.
Article CAS PubMed Google Scholar
-
Yadav, A., P. K, R., Bhasuran, B., and Oviya, I.R. (2023). A novel approach for classifying DNA barcodes using ensemble NLP models. In Proceeding of 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) (Chennai: India). pp. 1–5.
Google Scholar
-
Yang, C.H., Wu, K.C., Chuang, L.Y., and Chang, H.W. (2022). DeepBarcoding: deep learning for species classification using DNA barcoding. IEEE ACM Trans Comput Biol Bioinf 19, 2158–2165.
Article CAS Google Scholar
-
Yu, C., Qin, F., Watanabe, A., Yao, W., Li, Y., Qin, Z., Liu, Y., Wang, H., Jiangzuo, Q., Hsiang, A.Y., et al. (2024). Artificial intelligence in paleontology. Earth-Sci Rev 252, 104765.
Article Google Scholar
-
Yue, T., Wang, Y., Zhang, L., Gu, C., Xue, H., Wang, W., Lyu, Q., and Dun, Y. (2023). Deep learning for genomics: from early neural nets to modern large language models. Int J Mol Sci 24, 15858.
Article CAS PubMed PubMed Central Google Scholar
-
Zampar, L., and Silva, C.P.d. (2024). I-AM-Bird: a deep learning approach to detect amazonian bird species in residential environments. In Proceeding of 16th International Conference on Agents and Artificial Intelligence (Rome: Italy). pp. 542–548.
Google Scholar
-
Zhang, N., Bi, Z., Liang, X., Cheng, S., Hong, H., Deng, S., Lian, J., Zhang, Q., and Chen, H. (2022). OntoProtein: protein pretraining with gene ontology embedding. arXiv: 2201.11147.
Google Scholar
-
Zhang, P., Wang, H., Xu, H., Wei, L., Liu, L., Hu, Z., and Wang, X. (2023a). Deep flanking sequence engineering for efficient promoter design using DeepSEED. Nat Commun 14, 6309.
Article CAS PubMed PubMed Central Google Scholar
-
Zhang, X., Bu, J., Zhou, X., and Wang, X. (2023b). Automatic pest identification system in the greenhouse based on deep learning and machine vision. Front Plant Sci 14, 1255719.
Article PubMed PubMed Central Google Scholar
-
Zhang, Y., Jiang, H., Ye, T., and Juhas, M. (2021a). Deep learning for imaging and detection of microorganisms. Trends Microbiol 29, 569–572.
Article CAS PubMed Google Scholar
-
Zhang, Y., Lu, Y., Wang, H., Chen, P., and Liang, R. (2021b). Automatic classification of marine plankton with digital holography using convolutional neural network. Optics Laser Tech 139, 106979.
Article Google Scholar
-
Zhao, H., Zhang, S., Qin, H., Liu, X., Ma, D., Han, X., Mao, J., and Liu, S. (2024). DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework. Brief Bioinf 25, bbae157.
Article CAS Google Scholar
-
Zhou, S., Jiang, J., Hong, X., Fu, P., and Yan, H. (2023). Vision meets algae: a novel way for microalgae recognization and health monitor. Front Mar Sci 10.
Google Scholar
-
Zhou, Y., and Jiang, R. (2024). Advancing explainable AI toward human-like intelligence: forging the path to artificial brain. arXiv: 2402.06673.
Google Scholar
-
Zhou, Z., Ji, Y., Li, W., Dutta, P., Davuluri, R.V., and Liu, H. (2024). DNABERT-2: efficient foundation model and benchmark for multi-species genome. In ICLR 2024. arXiv: 2306.15006.
Google Scholar
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