{"type":"ScholarlyArticle","title":"An Embarrassingly Simple Approach to Enhance Transformer Performance in Genomic Selection for Crop Breeding","authors":[{"name":"Renqi Chen","slug":"renqi-chen","given":"Renqi","family":"Chen","affiliation":"Shanghai Artificial Intelligence Laboratory"},{"name":"Wenwei Han","slug":"wenwei-han","given":"Wenwei","family":"Han","affiliation":"Shanghai Artificial Intelligence Laboratory; Institute of Computing Technology , Chinese Academy of Sciences; School of Computer Science , University of Chinese Academy of Sciences"},{"name":"Haohao Zhang","slug":"haohao-zhang","given":"Haohao","family":"Zhang","affiliation":"School of Computer Science and Artificial Intelligence , Wuhan University of Technology"},{"name":"Haoyang Su","slug":"haoyang-su","given":"Haoyang","family":"Su","affiliation":"Shanghai Artificial Intelligence Laboratory"},{"name":"Zhefan Wang","slug":"zhefan-wang","given":"Zhefan","family":"Wang","affiliation":"Shanghai Artificial Intelligence Laboratory"},{"name":"Xiaolei Liu","slug":"xiaolei-liu","given":"Xiaolei","family":"Liu","affiliation":"School of Animal Science and Technology , Huazhong Agricultural University"},{"name":"Hao Jiang","slug":"hao-jiang","given":"Hao","family":"Jiang","affiliation":"Institute of Computing Technology , Chinese Academy of Sciences; School of Computer Science , University of Chinese Academy of Sciences"},{"name":"Wanli Ouyang","slug":"wanli-ouyang","given":"Wanli","family":"Ouyang","affiliation":"Shanghai Artificial Intelligence Laboratory"},{"name":"Nanqing Dong","slug":"nanqing-dong","given":"Nanqing","family":"Dong","affiliation":"Shanghai Artificial Intelligence Laboratory"}],"abstract":"Genomic selection (GS), as a critical crop breeding strategy, plays a key role in enhancing food production and addressing the global hunger crisis. The predominant approaches in GS currently revolve around employing statistical methods for prediction. However, statistical methods often come with two main limitations: strong statistical priors and linear assumptions. A recent trend is to capture the non-linear relationships between markers by deep learning. However, as crop datasets are commonly long sequences with limited samples, the robustness of deep learning models, especially Transformers, remains a challenge. In this work, to unleash the unexplored potential of attention mechanism for the task of interest, we propose a simple yet effective Transformer-based framework that enables end-to-end training of the whole sequence. Via experiments on rice3k and wheat3k datasets, we show that, with simple tricks such as k-mer tokenization and random masking, Transformer can achieve overall superior performance against seminal methods on GS tasks of interest.","doi":"10.24963/ijcai.2024/798","url":"https://ijcai.org/proceedings/2024/798","pdf":"https://ijcai.org/proceedings/2024/0798.pdf","markdown":"https://ijcai.org/proceedings/2024/798.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/798","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":798,"pages":{"first":7215,"last":7223},"keywords":["Machine Learning: General","Multidisciplinary Topics and Applications: General"],"videos":[],"references":[{"position":1,"raw":"[Aguilar et al., 2010] I Aguilar, I Misztal, DL Johnson, An- dres Legarra, S Tsuruta, and TJ Lawlor. Hot topic: a uni- fied approach to utilize phenotypic, full pedigree, and ge- nomic information for genetic evaluation of holstein final score. Journal of dairy science, 93(2):743-752, 2010.","title":"Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of holstein final score","authors":"Aguilar","year":"2010","venue":"Journal of dairy science","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Boichard et al., 2012] Didier Boichard, Francois Guil- laume, Aurélia Baur, Pascal Croiseau, Marie-Noelle Rossignol, Marie Yvonne Boscher, Tom Druet, LUCIE Genestout, JJ Colleau, L Journaux, et al. Genomic se- lection in french dairy cattle. Animal Production Science, 52(3):115-120, 2012.","title":"Genomic selection in french dairy cattle","authors":"Didier Boichard, Francois Guillaume, Aurélia Baur, Pascal Croiseau, Marie-Noelle Rossignol, Marie Yvonne Boscher, Tom Druet, Lucie Genestout, L Colleau, Journaux","year":"2012","venue":"Animal Production Science","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Dao et al., 2022] Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. Flashattention: Fast and memory-efficient exact attention with io-awareness. Advances in Neural Information Processing Systems, 35:16344-16359, 2022.","title":"Flashattention: Fast and memory-efficient exact attention with io-awareness","authors":"Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, Christopher Ré","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[De Los Campos et al., 2009] Gustavo De Los Campos, Hugo Naya, Daniel Gianola, José Crossa, Andrés Legarra, Eduardo Manfredi, Kent Weigel, and José Miguel Cotes. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics, 182(1):375-385, 2009.","title":"Predicting quantitative traits with regression models for dense molecular markers and pedigree","authors":"Campos De Los","year":"2009","venue":"Genetics","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Endelman, 2011] Jeffrey B Endelman. Ridge regression and other kernels for genomic selection with r package rrblup. The plant genome, 4(3), 2011.","title":"Ridge regression and other kernels for genomic selection with r package rrblup","authors":"Jeffrey B Endelman","year":"2011","venue":"The plant genome","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[García-Ruiz et al., 2016] Adriana García-Ruiz, John B Cole, Paul M VanRaden, George R Wiggans, Felipe J Ruiz-López, and Curtis P Van Tassell. Changes in genetic selection differentials and generation intervals in us hol- stein dairy cattle as a result of genomic selection. Proceed- ings of the National Academy of Sciences, 113(28):E3995- E4004, 2016.","title":"Changes in genetic selection differentials and generation intervals in us holstein dairy cattle as a result of genomic selection","authors":"Adriana García-Ruiz, John B Cole, Paul M Vanraden, George R Wiggans, Felipe J Ruiz-López, Curtis P Van Tassell","year":"2016","venue":"Proceedings of the National Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Goddard et al., 2011] Michael E Goddard, Ben J Hayes, and Theo HE Meuwissen. Using the genomic relation- ship matrix to predict the accuracy of genomic selection. Journal of animal breeding and genetics, 128(6):409-421, 2011.","title":"Using the genomic relationship matrix to predict the accuracy of genomic selection","authors":"Ben J Michael E Goddard, Theo He Hayes, Meuwissen","year":"2011","venue":"Journal of animal breeding and genetics","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Gualdrón Duarte et al., 2020] José Luis Gualdrón Duarte, Ann-Stephan Gori, Xavier Hubin, Daniela Lourenco, Ca- role Charlier, Ignacy Misztal, and Tom Druet. Perfor- mances of adaptive multiblup, bayesian regressions, and weighted-gblup approaches for genomic predictions in belgian blue beef cattle. BMC genomics, 21(1):1-18, 2020.","title":"Performances of adaptive multiblup, bayesian regressions, and weighted-gblup approaches for genomic predictions in belgian blue beef cattle","authors":"Gualdrón Duarte","year":"2020","venue":"BMC genomics","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"He","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ji et al., 2021] Yanrong Ji, Zhihan Zhou, Han Liu, and Ra- mana V Davuluri. Dnabert: pre-trained bidirectional en- coder representations from transformers model for dna- language in genome. Bioinformatics, 37(15):2112-2120, 2021.","title":"Dnabert: pre-trained bidirectional encoder representations from transformers model for dnalanguage in genome","authors":"Ji","year":"2021","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Johnson, 2010] Andrew D Johnson. An extended iupac nomenclature code for polymorphic nucleic acids. Bioin- formatics, 26(10):1386-1389, 2010.","title":"An extended iupac nomenclature code for polymorphic nucleic acids","authors":"Johnson Andrew","year":"2010","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kärkkäinen and Sillanpää, 2012] Hanni P Kärkkäinen and Mikko J Sillanpää. Back to basics for bayesian model building in genomic selection. Genetics, 191(3):969-987, 2012.","title":"Back to basics for bayesian model building in genomic selection","authors":"P Hanni, Mikko J Kärkkäinen, Sillanpää","year":"2012","venue":"Genetics","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ke et al., 2017] Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. Lightgbm: A highly efficient gradient boost- ing decision tree. Advances in neural information process- ing systems, 30, 2017.","title":"Lightgbm: A highly efficient gradient boosting decision tree","authors":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kingsolver and Pfennig, 2007] Joel G Kingsolver and David W Pfennig. Patterns and power of phenotypic selection in nature. Bioscience, 57(7):561-572, 2007.","title":"Patterns and power of phenotypic selection in nature","authors":"G Joel, David W Kingsolver, Pfennig","year":"2007","venue":"Bioscience","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lilin et al., 2022] Yin Lilin, Zhang Haohao, Li Xinyun, Zhao Shuhong, and Liu Xiaolei. hibayes: An r pack- age to fit individual-level, summary-level and single-step bayesian regression models for genomic prediction and genome-wide association studies. bioRxiv, 2022.","title":"hibayes: An r package to fit individual-level, summary-level and single-step bayesian regression models for genomic prediction and genome-wide association studies","authors":"Yin Lilin, Zhang Haohao, Li Xinyun, Zhao Shuhong, Liu Xiaolei","year":"2022","venue":"bioRxiv","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Liu et al., 2019] Yang Liu, Duolin Wang, Fei He, Juexin Wang, Trupti Joshi, and Dong Xu. Phenotype prediction and genome-wide association study using deep convolu- tional neural network of soybean. Frontiers in genetics, 10:1091, 2019.","title":"Phenotype prediction and genome-wide association study using deep convolutional neural network of soybean","authors":"Yang Liu, Duolin Wang, Fei He, Juexin Wang, Trupti Joshi, Dong Xu","year":"2019","venue":"Frontiers in genetics","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ma et al., 2017] Wenlong Ma, Zhixu Qiu, Jie Song, Qian Cheng, and Chuang Ma. Deepgs: Predicting pheno- types from genotypes using deep learning. BioRxiv, page 241414, 2017.","title":"Deepgs: Predicting phenotypes from genotypes using deep learning","authors":"Wenlong Ma, Zhixu Qiu, Jie Song, Qian Cheng, Chuang Ma","year":"2017","venue":"BioRxiv","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Manor and Segal, 2013] Ohad Manor and Eran Segal. Pre- dicting disease risk using bootstrap ranking and clas- sification algorithms. PLoS computational biology, 9(8):e1003200, 2013.","title":"Predicting disease risk using bootstrap ranking and classification algorithms","authors":"Segal Manor, Ohad Manor, Eran Segal","year":"2013","venue":"PLoS computational biology","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Meuwissen et al., 2001] Theo HE Meuwissen, Ben J Hayes, and ME1461589 Goddard. Prediction of total genetic value using genome-wide dense marker maps. genetics, 157(4):1819-1829, 2001.","title":"Prediction of total genetic value using genome-wide dense marker maps","authors":"Ben J Theo He Meuwissen, Hayes, Goddard","year":"2001","venue":"genetics","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Mittag et al., 2015] Florian Mittag, Michael Römer, and Andreas Zell. Influence of feature encoding and choice of classifier on disease risk prediction in genome-wide as- sociation studies. PloS one, 10(8):e0135832, 2015.","title":"Influence of feature encoding and choice of classifier on disease risk prediction in genome-wide association studies","authors":"Mittag","year":"2015","venue":"PloS one","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Muhammad et al., 2021] Ali Muhammad, Jianguo Li, We- ichen Hu, Jinsheng Yu, Shahid Ullah Khan, Muham- mad Hafeez Ullah Khan, Guosheng Xie, Jibin Wang, and Lingqiang Wang. Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using different gwas models. Scientific reports, 11(1):6767, 2021.","title":"Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using different gwas models","authors":"Ali Muhammad, Jianguo Li, Weichen Hu, Jinsheng Yu, Shahid Ullah Khan, Muhammad Hafeez, Ullah Khan, Guosheng Xie, Jibin Wang, Lingqiang Wang","year":"2021","venue":"Scientific reports","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Quyet Nguyen et al., 2024] Chien Quyet Nguyen, Tuyen Thi Tran, Trang Thanh Thi Nguyen, Thuy Ha Thi Nguyen, et al. Mapping of soil erosion susceptibility using ad- vanced machine learning models at nghe an, vietnam. Journal of Hydroinformatics, 26(1):72-87, 2024.","title":"Mapping of soil erosion susceptibility using advanced machine learning models at nghe an, vietnam","authors":"Tuyen Chien Quyet Nguyen, Trang Thi Tran, Thi Thanh, Thuy Nguyen, Thi Ha, Nguyen","year":"2024","venue":"Journal of Hydroinformatics","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Ramakrishnan et al., 2022] Muthusamy Ramakrishnan, Lakkakula Satish, Anket Sharma, Kunnummal Kurun- gara Vinod, Abolghassem Emamverdian, Mingbing Zhou, and Qiang Wei. Transposable elements in plants: Recent advancements, tools and prospects. Plant Molecular Biology Reporter, 40(4):628-645, 2022.","title":"Transposable elements in plants: Recent advancements, tools and prospects","authors":"Lakkakula Muthusamy Ramakrishnan, Anket Satish, Sharma, Kurungara Kunnummal, Abolghassem Vinod, Mingbing Emamverdian, Qiang Zhou, Wei","year":"2022","venue":"Plant Molecular Biology Reporter","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ribaut and Hoisington, 1998] Jean-Marcel Ribaut and David Hoisington. Marker-assisted selection: new tools and strategies. Trends in Plant Science, 3(6):236-239, 1998.","title":"Marker-assisted selection: new tools and strategies","authors":"Hoisington ; Jean-Marcel Ribaut, David Ribaut, Hoisington","year":"1998","venue":"Trends in Plant Science","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Siepielski et al., 2013] Adam M Siepielski, Kiyoko M Gotanda, Michael B Morrissey, Sarah E Diamond, Joseph D DiBattista, and Stephanie M Carlson. The spa- tial patterns of directional phenotypic selection. Ecology letters, 16(11):1382-1392, 2013.","title":"The spatial patterns of directional phenotypic selection","authors":"Kiyoko M Adam M Siepielski, Michael B Gotanda, Sarah E Morrissey, Joseph D Diamond, Stephanie M Dibattista, Carlson","year":"2013","venue":"Ecology letters","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Tipping and Faul, 2003] Michael E Tipping and Anita C Faul. Fast marginal likelihood maximisation for sparse bayesian models. In International workshop on artificial intelligence and statistics, pages 276-283. PMLR, 2003.","title":"Fast marginal likelihood maximisation for sparse bayesian models","authors":"E Michael, Anita C Tipping, Faul","year":"2003","venue":"International workshop on artificial intelligence and statistics","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[United Nations, 2023b] United Nations. Leave no one behind. https://unsdg.un.org/2030-agenda/universal- values/leave-no-one-behind, 2023.","title":"United Nations. Leave no one behind","authors":null,"year":"2023","venue":null,"doi":null,"url":"https://unsdg.un.org/2030-agenda/universal-values/leave-no-one-behind,2023","internal_url":null},{"position":29,"raw":"[United Nations, 2023c] United Nations. The State of Food Security and Nutrition in the World 2023. FAO, Rome, 2023. Urbanization, agrifood systems transformation and healthy diets across the rural-urban continuum.","title":"The State of Food Security and Nutrition in the World 2023. FAO, Rome, 2023. Urbanization, agrifood systems transformation and healthy diets across the rural-urban continuum","authors":null,"year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[VanRaden, 2008] Paul M VanRaden. Efficient methods to compute genomic predictions. Journal of dairy science, 91(11):4414-4423, 2008.","title":"Efficient methods to compute genomic predictions","authors":"M Paul, Vanraden","year":"2008","venue":"Journal of dairy science","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wang et al., 2018] Wensheng Wang, Ramil Mauleon, Zhiqiang Hu, Dmytro Chebotarov, Shuaishuai Tai, Zhichao Wu, Min Li, Tianqing Zheng, Roven Rommel Fuentes, Fan Zhang, et al. Genomic variation in 3,010 diverse accessions of asian cultivated rice. Nature, 557(7703):43-49, 2018.","title":"Genomic variation in 3,010 diverse accessions of asian cultivated rice","authors":"Wensheng Wang, Ramil Mauleon, Zhiqiang Hu, Dmytro Chebotarov, Shuaishuai Tai, Zhichao Wu, Min Li, Tianqing Zheng, Roven Rommel Fuentes, Fan Zhang","year":"2018","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wang et al., 2023] Kelin Wang, Muhammad Ali Abid, Awais Rasheed, Jose Crossa, Sarah Hearne, and Huihui Li. Dnngp, a deep neural network-based method for ge- nomic prediction using multi-omics data in plants. Molec- ular Plant, 16(1):279-293, 2023.","title":"Dnngp, a deep neural network-based method for genomic prediction using multi-omics data in plants","authors":"Kelin Wang, Muhammad Ali Abid, Awais Rasheed, Jose Crossa, Sarah Hearne, Huihui Li","year":"2023","venue":"Molecular Plant","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wu et al., 2024] Cuiling Wu, Yiyi Zhang, Zhiwen Ying, Ling Li, Jun Wang, Hui Yu, Mengchen Zhang, Xi- anzhong Feng, Xinghua Wei, and Xiaogang Xu. A transformer-based genomic prediction method fused with knowledge-guided module. Briefings in Bioinformatics, 25(1):bbad438, 2024.","title":"A transformer-based genomic prediction method fused with knowledge-guided module","authors":"Cuiling Wu, Yiyi Zhang, Zhiwen Ying, Ling Li, Jun Wang, Hui Yu, Mengchen Zhang, Xianzhong Feng, Xinghua Wei, Xiaogang Xu","year":"2024","venue":"Briefings in Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Xie et al., 2023] Zhengchao Xie, Xiaogang Xu, Ling Li, Cuiling Wu, Yinxing Ma, Jingjing He, Sidi Wei, Jun Wang, and Xianzhong Feng. Residual networks without pooling layers improve the accuracy of genomic predic- tions. 2023.","title":"Residual networks without pooling layers improve the accuracy of genomic predictions","authors":"Zhengchao Xie, Xiaogang Xu, Ling Li, Cuiling Wu, Yinxing Ma, Jingjing He, Sidi Wei, Jun Wang, Xianzhong Feng","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Xu and Crouch, 2008] Yunbi Xu and Jonathan H Crouch. Marker-assisted selection in plant breeding: From publi- cations to practice. Crop science, 48(2):391-407, 2008.","title":"Marker-assisted selection in plant breeding: From publications to practice","authors":"Crouch Xu, Yunbi Xu, Jonathan H Crouch","year":"2008","venue":"Crop science","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Yin et al., 2023] Lilin Yin, Haohao Zhang, Zhenshuang Tang, Dong Yin, Yuhua Fu, Xiaohui Yuan, Xinyun Li, Xi- aolei Liu, and Shuhong Zhao. HIBLUP: an integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data. Nucleic Acids Research, 51(8):3501-3512, 02 2023.","title":"HIBLUP: an integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data","authors":"Lilin Yin, Haohao Zhang, Zhenshuang Tang, Dong Yin, Yuhua Fu, Xiaohui Yuan, Xinyun Li, Xiaolei Liu, Shuhong Zhao","year":"2023","venue":"Nucleic Acids Research","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhang et al., 2023] Daoan Zhang, Weitong Zhang, Bing He, Jianguo Zhang, Chenchen Qin, and Jianhua Yao. Dnagpt: A generalized pretrained tool for multiple dna sequence analysis tasks. bioRxiv, pages 2023-07, 2023.","title":"Dnagpt: A generalized pretrained tool for multiple dna sequence analysis tasks","authors":"Zhang","year":"2023","venue":"bioRxiv","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhou et al., 2021] Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wan- cai Zhang. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 11106-11115, 2021.","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","authors":"Zhou","year":"2021","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhou et al., 2023] Zhihan Zhou, Yanrong Ji, Weijian Li, Pratik Dutta, Ramana Davuluri, and Han Liu. Dnabert- 2: Efficient foundation model and benchmark for multi- species genome. arXiv preprint arXiv:2306.15006, 2023.","title":"Dnabert-2: Efficient foundation model and benchmark for multispecies genome","authors":"Zhou","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}