Fetching the paper…
Reading the bibliography…
Large language models (LLMs) are having transformative impacts across a wide range of scientific fields, particularly in the biomedical sciences.
Science 164 , 788–798. doi: 10.1126/science.164.3881.788
King, J. L., and Jukes, T. H. (1969). Non-darwinian evolution · 1969
Earlier work this paper cites.
Principles of population genetics vol. 116
Hartl, D. L., Clark, A. G., and Clark, A. G · 1997
Earlier work this paper cites.
The Nature of Statistical Learning Theory
Vapnik, V. N · 1999
Earlier work this paper cites.
Human Molecular Genetics 11 , 2417–2423. doi: 10.1093/hmg/11.20.2417
Pritchard, J. K., and Cox, N. (2002). The allelic architecture of human disease genes: common disease–common variant…or not? · 2002
Earlier work this paper cites.
Genome Research 14 , 708–715
Blanchette, M., Kent, W. J., Riemer, C., Elnitski, L., Smit, A. F., Roskin, K. M., Baertsch, R., Rosenbloom, K., Clawson, H., Green, E. D. et al. (2004). Aligning multiple genomic sequences with the threaded blockset aligner · 2004
Earlier work this paper cites.
arXiv preprint arXiv:2005.08100. https://arxiv.org/abs/2005.08100
Gulati, A., Qin, J., Chiu, C.-C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y. et al. (2020). Conformer: Convolution-augmented transformer for speech recognition · 2005
Earlier work this paper cites.
Genome Research 15 , 1034–1050
Siepel, A., Bejerano, G., Pedersen, J. S., Hinrichs, A. S., Hou, M., Rosenbloom, K., Clawson, H., Spieth, J., Hillier, L. W., Richards, S. et al. (2005). Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes · 2005
Earlier work this paper cites.
Genome Research 20 , 110–121
Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R., and Siepel, A. (2010). Detection of nonneutral substitution rates on mammalian phylogenies · 2010
Earlier work this paper cites.
Algorithms for Molecular Biology 6 , 1–14
Lorenz, R., Bernhart, S. H., Höner zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P. F., and Hofacker, I. L. (2011). ViennaRNA Package 2.0 · 2011
Earlier work this paper cites.
Nucleic Acids Research 40 , D115–D122
Markowitz, V. M., Chen, I.-M. A., Palaniappan, K., Chu, K., Szeto, E., Grechkin, Y., Ratner, A., Jacob, B., Huang, J., Williams, P. et al. (2012). IMG: the integrated microbial genomes database and comparative analysis system · 2012
Earlier work this paper cites.
Nature 489 , 57–74
Consortium, E. P. et al. (2012). An integrated encyclopedia of DNA elements in the human genome · 2012
Earlier work this paper cites.
Nature Biotechnology 33 , 831–838
Alipanahi, B., Delong, A., Weirauch, M. T., and Frey, B. J. (2015). Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning · 2015
Earlier work this paper cites.
Nature Methods 12 , 931–934
Zhou, J., and Troyanskaya, O. G. (2015). Predicting effects of noncoding variants with deep learning–based sequence model · 2015
Earlier work this paper cites.
Nucleic Acids Research 43 , D789–D798
Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F., and Hamosh, A. (2015). OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders · 2015
Earlier work this paper cites.
International Journal of Computer Vision (IJCV) 115 , 211–252. doi: 10.1007/s11263-015-0816-y
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge · 2015
Earlier work this paper cites.
Human Mutation 36 , 513–523
Grimm, D. G., Azencott, C.-A., Aicheler, F., Gieraths, U., MacArthur, D. G., Samocha, K. E., Cooper, D. N., Stenson, P. D., Daly, M. J., Smoller, J. W. et al. (2015). The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity · 2015
Earlier work this paper cites.
Nature 518 , 317–330
Kundaje, A., Meuleman, W., Ernst, J. et al. (2015). Integrative analysis of 111 reference human epigenomes · 2015
Earlier work this paper cites.
Genome Research 26 , 990–999
Kelley, D. R., Snoek, J., and Rinn, J. L. (2016). Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks · 2016
Earlier work this paper cites.
Nucleic Acids Research 44 , 6721–6731
Zhang, Y., An, L., Yue, F., and Hardison, R. C. (2016). Jointly characterizing epigenetic dynamics across multiple human cell types · 2016
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Sennrich, R., Haddow, B., and Birch, A · 2016
Earlier work this paper cites.
Nucleic Acids Research 44 , D862–D868
Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Jang, W. et al. (2016). ClinVar: public archive of interpretations of clinically relevant variants · 2016
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Human Genetics 136 , 665–677. doi: 10.1007/s00439-017-1779-6
Stenson, P. D., Mort, M., Ball, E. V., Evans, K., Hayden, M., Heywood, S., Hussain, M., Phillips, A. D., and Cooper, D. N. (2017). The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies · 2017
Earlier work this paper cites.
Genome Biology 18 , 1–5
Johnson, A. D., Handsaker, R. E., Pulit, S. L., Nizzari, M., O’Donnell, C. J., and de Bakker, P. I. (2017). CAGI: The Critical Assessment of Genome Interpretation · 2017
Earlier work this paper cites.
Nature Methods 15 , 816–822
Riesselman, A. J., Ingraham, J. B., and Marks, D. S. (2018). Deep generative models of genetic variation capture the effects of mutations · 2018
Earlier work this paper cites.
Genome Research 28 , 739–750
Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., and Snoek, J. (2018). Sequential regulatory activity prediction across chromosomes with convolutional neural networks · 2018
Earlier work this paper cites.
Subword regularization: Improving neural network translation models with multiple subword candidates
Kudo, T · 2018
Earlier work this paper cites.
Human Molecular Genetics 27 , R219–R227
Karnuta, J. M., and Scacheri, P. C. (2018). Enhancers: bridging the gap between gene control and human disease · 2018
Earlier work this paper cites.
arXiv preprint arXiv:1811.00416. https://arxiv.org/abs/1811.00416
Shrikumar, A., Tian, K., Avsec, Ž., Shcherbina, A., Banerjee, A., Sharmin, M., Nair, S., and Kundaje, A. (2018). Technical note on transcription factor motif discovery from importance scores (TF-MoDISco) version 0.5. 6.5 · 2018
Earlier work this paper cites.
Nature 562 , 217–222
Findlay, G. M., Daza, R. M., Martin, B., Zhang, M. D., Leith, A. P., Gasperini, M., Janizek, J. D., Huang, X., Starita, L. M., and Shendure, J. (2018). Accurate classification of BRCA1 variants with saturation genome editing · 2018
Earlier work this paper cites.
Proteins: Structure, Function, and Bioinformatics 86 , 7–15
Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T., and Tramontano, A. (2018). Critical assessment of methods of protein structure prediction (CASP)—round XII · 2018
Earlier work this paper cites.
Cell 176 , 535–548
Jaganathan, K., Panagiotopoulou, S. K., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., Kosmicki, J. A., Arbelaez, J., Cui, W., Schwartz, G. B. et al. (2019). Predicting splicing from primary sequence with deep learning · 2019
Earlier work this paper cites.
Generative models for graph-based protein design
Ingraham, J., Garg, V., Barzilay, R., and Jaakkola, T · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Earlier work this paper cites.
Nature Reviews Genetics 20 , 437–455
Schoenfelder, S., and Fraser, P. (2019). Long-range enhancer–promoter contacts in gene expression control · 2019
Earlier work this paper cites.
Transformer-XL: Attentive Language Models beyond a Fixed-Length Context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J. G., Le, Q. V., and Salakhutdinov, R · 2019
Earlier work this paper cites.
Nature Communications 10 . doi: 10.1038/s41467-019-11526-w
Kircher, M., Xiong, C., Martin, B., Schubach, M., Inoue, F., Bell, R. J. A., Costello, J. F., Shendure, J., and Ahituv, N. (2019). Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Earlier work this paper cites.
Nucleic Acids Research 48 , 6403–6412
Wang, Y., Wang, H., Wei, L., Li, S., Liu, L., and Wang, X. (2020). Synthetic promoter design in Escherichia coli based on a deep generative network · 2020
Earlier work this paper cites.
Big Bird: Transformers for Longer Sequences
Zaheer, M., Guruganesh, G., Dubey, K. A., Ainslie, J., Alberti, C., Ontanon, S., Pham, P., Ravula, A., Wang, Q., Yang, L., and Ahmed, A · 2020
Earlier work this paper cites.
Journal of Machine Learning Research 21 , 1–67. http://jmlr.org/papers/v21/20-074.html
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer · 2020
Earlier work this paper cites.
arXiv preprint arXiv:2101.00027. https://arxiv.org/abs/2101.00027
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., and Leahy, C. (2020). The Pile: An 800GB Dataset of Diverse Text for Language Modeling · 2020
Earlier work this paper cites.
Nature 587 , 246–251
Armstrong, J., Hickey, G., Diekhans, M., Fiddes, I. T., Novak, A. M., Deran, A., Fang, Q., Xie, D., Feng, S., Stiller, J. et al. (2020). Progressive Cactus is a multiple-genome aligner for the thousand-genome era · 2020
Earlier work this paper cites.
Entropy 23 , 18
Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods · 2020
Cited alongside, same era.
Nature 581 , 434–443. doi: 10.1038/s41586-020-2308-7
Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., Collins, R. L., Laricchia, K. M., Ganna, A., Birnbaum, D. P., Gauthier, L. D., Brand, H., Solomonson, M., Watts, N. A., Rhodes, D., Singer-Berk, M., England, E. M., Seaby, E. G., Kosmicki, J. A., Walters, R. K., Tashman, K., Farjoun, Y., Banks, E., Poterba, T., Consortium, G. A. D., and MacArthur, D. G. (2020). The mutational constraint spectrum quantified from variation in 141,456 humans · 2020
Cited alongside, same era.
Language models enable zero-shot prediction of the effects of mutations on protein function
Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., and Rives, A · 2021
Cited alongside, same era.
Cell Systems 12 , 654–669
Bepler, T., and Berger, B. (2021). Learning the protein language: Evolution, structure, and function · 2021
Cited alongside, same era.
Nature 599 , 91–95
Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J. K., Brock, K., Gal, Y., and Marks, D. S. (2021). Disease variant prediction with deep generative models of evolutionary data · 2021
Cited alongside, same era.
Nucleic Acids Research 51 , D753–D759
Richardson, L., Allen, B., Baldi, G., Beracochea, M., Bileschi, M. L., Burdett, T., Burgin, J., Caballero-Pérez, J., Cochrane, G., Colwell, L. J. et al. (2023). MGnify: the microbiome sequence data analysis resource in 2023 · 2023
Later among the works it cites.
Science 380 , eabn2937
Sullivan, P. F., Meadows, J. R., Gazal, S., Phan, B. N., Li, X., Genereux, D. P., Dong, M. X., Bianchi, M., Andrews, G., Sakthikumar, S. et al. (2023). Leveraging base-pair mammalian constraint to understand genetic variation and human disease · 2023
Later among the works it cites.
bioRxiv preprint. https://www.biorxiv.org/content/10.1101/2023.08.30.555582v1
Linder, J., Srivastava, D., Yuan, H., Agarwal, V., and Kelley, D. R. (2023). Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation · 2023
Later among the works it cites.
MEGABYTE: Predicting million-byte sequences with multiscale transformers
Yu, L., Simig, D., Flaherty, C., Aghajanyan, A., Zettlemoyer, L., and Lewis, M · 2023
Later among the works it cites.
Hyena Hierarchy: Towards larger convolutional language models
Poli, M., Massaroli, S., Nguyen, E., Fu, D. Y., Dao, T., Baccus, S., Bengio, Y., Ermon, S., and Ré, C · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nature Methods 18 , 1196–1203
Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., and Kelley, D. R. (2021). Effective gene expression prediction from sequence by integrating long-range interactions · 2021
Cited alongside, same era.
Nature Communications 12 , 2403
Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., and Marks, D. S. (2021). Protein design and variant prediction using autoregressive generative models · 2021
Cited alongside, same era.
Nature Plants 7 , 842–855
Jores, T., Tonnies, J., Wrightsman, T., Buckler, E. S., Cuperus, J. T., Fields, S., and Queitsch, C. (2021). Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters · 2021
Cited alongside, same era.
arXiv preprint arXiv:2108.07258. https://arxiv.org/abs/2108.07258
Bommasani, R., Hudson, D. A. et al. (2021). On the opportunities and risks of foundation models · 2021
Cited alongside, same era.
Bioinformatics 37 , 2112–2120
Ji, Y., Zhou, Z., Liu, H., and Davuluri, R. V. (2021). DNABERT: pre-trained bidirectional encoder representations from Transformers model for DNA-language in genome · 2021
Cited alongside, same era.
Multi-modal Self-supervised Pre-training for Large-scale Genome Data
Mo, S., Fu, X., Hong, C., Chen, Y., Zheng, Y., Tang, X., Lan, Y., Shen, Z., and Xing, E · 2021
Cited alongside, same era.
arXiv preprint arXiv:2112.07571. https://arxiv.org/abs/2112.07571
Trotter, M. V., Nguyen, C. Q., Young, S., Woodruff, R. T., and Branson, K. M. (2021). Epigenomic language models powered by Cerebras · 2021
Cited alongside, same era.
arXiv preprint arXiv:2312.00752. https://arxiv.org/abs/2312.00752
Gu, A., and Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces · 2023
Later among the works it cites.
Genome Biology 24 , 147
Fowler, D. M., Adams, D. J., Gloyn, A. L., Hahn, W. C., Marks, D. S., Muffley, L. A., Neal, J. T., Roth, F. P., Rubin, A. F., Starita, L. M., and Hurles, M. E. (2023). An Atlas of Variant Effects to understand the genome at nucleotide resolution · 2023
Later among the works it cites.
ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
Notin, P., Kollasch, A. W., Ritter, D., Niekerk, L. V., Paul, S., Spinner, H., Rollins, N. J., Shaw, A., Orenbuch, R., Weitzman, R., Frazer, J., Dias, M., Franceschi, D., Gal, Y., and Marks, D. S · 2023
Later among the works it cites.
bioRxiv preprint. https://www.biorxiv.org/content/10.1101/2023.11.27.568764v2
Gupta, A., Lal, A., Gunsalus, L. M., Biancalani, T., and Eraslan, G. (2023). Polygraph: A software framework for the systematic assessment of synthetic regulatory DNA elements · 2023
Later among the works it cites.
BMC Genomic Data 24 , Article number: 25
Grešová, K., Martinek, V., Čechák, D., Šimeček, P., and Alexiou, P. (2023). Genomic benchmarks: a collection of datasets for genomic sequence classification · 2023
Later among the works it cites.
Nature Biotechnology 42 , 200–202
Ruffolo, J. A., and Madani, A. (2024). Designing proteins with language models · 2024
Closest in time.
Communications Biology 7 , 835. https://doi.org/10.1038/s42003-024-06465-2 . doi: 10.1038/s42003-024-06465-2
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., Laterre, A., Beguir, K., Pierrot, T., and Lopez, M. (2024). A foundational large language model for edible plant genomes · 2024
Closest in time.
bioRxiv preprint. https://www.biorxiv.org/content/early/2024/06/05/2024.06.04.596709 . doi: 10.1101/2024.06.04.596709
Zhai, J., Gokaslan, A., Schiff, Y., Berthel, A., Liu, Z.-Y., Miller, Z. R., Scheben, A., Stitzer, M. C., Romay, C., Buckler, E. S., and Kuleshov, V. (2024). Cross-species plant genomes modeling at single nucleotide resolution using a pre-trained DNA language model · 2024
Closest in time.
bioRxiv preprint ( 2024–07). https://www.biorxiv.org/content/10.1101/2024.07.27.605418v1
Tomaz da Silva, P., Karollus, A., Hingerl, J., Galindez, G., Wagner, N., Hernandez-Alias, X., Incarnato, D., and Gagneur, J. (2024). Nucleotide dependency analysis of DNA language models reveals genomic functional elements · 2024
Closest in time.
arXiv preprint arXiv:2403.03234. https://arxiv.org/abs/2403.03234
Schiff, Y., Kao, C.-H., Gokaslan, A., Dao, T., Gu, A., and Kuleshov, V. (2024). Caduceus: Bi-directional equivariant long-range DNA sequence modeling · 2024
Closest in time.
regLM: Designing realistic regulatory DNA with autoregressive language models
Lal, A., Garfield, D., Biancalani, T., and Eraslan, G · 2024
Closest in time.
bioRxiv preprint ( 2024–02). https://www.biorxiv.org/content/10.1101/2024.02.27.582234v2
Nguyen, E., Poli, M., Durrant, M. G., Thomas, A. W., Kang, B., Sullivan, J., Ng, M. Y., Lewis, A., Patel, A., Lou, A. et al. (2024). Sequence modeling and design from molecular to genome scale with Evo · 2024
Closest in time.
bioRxiv preprint. https://www.biorxiv.org/content/10.1101/2024.03.19.585716v1
Ratcliff, J. D. (2024). Transformer model generated bacteriophage genomes are compositionally distinct from natural sequences · 2024
Closest in time.
bioRxiv ( 2024–07). https://www.biorxiv.org/content/10.1101/2024.07.10.602933v1
West-Roberts, J., Kravitz, J., Jha, N., Cornman, A., and Hwang, Y. (2024). Diverse genomic embedding benchmark for functional evaluation across the tree of life · 2024
Closest in time.
bioRxiv preprint. https://www.biorxiv.org/content/10.1101/2024.03.14.584712v2
de Almeida, B. P., Dalla-Torre, H., Richard, G., Blum, C., Hexemer, L., Gélard, M., Mendoza-Revilla, J., Pandey, P., Laurent, S., Lopez, M. et al. (2024). SegmentNT: annotating the genome at single-nucleotide resolution with DNA foundation models · 2024
Closest in time.
arXiv preprint. https://arxiv.org/abs/2402.08777
Zhou, Z., Wu, W., Ho, H., Wang, J., Shi, L., Davuluri, R. V., Wang, Z., and Liu, H. (2024). DNABERT-S: Learning species-aware dna embedding with genome foundation models · 2024
Closest in time.
arXiv preprint arXiv:2406.14150
Garau-Luis, J. J., Bordes, P., Gonzalez, L., Roller, M., de Almeida, B. P., Hexemer, L., Blum, C., Laurent, S., Grzegorzewski, J., Lang, M. et al. (2024). Multi-modal transfer learning between biological foundation models · 2024
Closest in time.
BEND: Benchmarking DNA Language Models on Biologically Meaningful Tasks
Marin, F. I., Teufel, F., Horlacher, M., Madsen, D., Pultz, D., Winther, O., and Boomsma, W · 2024
Closest in time.
bioRxiv preprint. https://www.biorxiv.org/content/10.1101/2024.02.29.582810v1
Tang, Z., and Koo, P. K. (2024). Evaluating the representational power of pre-trained DNA language models for regulatory genomics · 2024
Closest in time.
bioRxiv preprint ( 2024–02)
Li, F.-Z., Amini, A. P., Yue, Y., Yang, K. K., and Lu, A. X. (2024). Feature reuse and scaling: Understanding transfer learning with protein language models · 2024
Closest in time.
Briefings in Bioinformatics 25 , bbae163
Chen, K., Zhou, Y., Ding, M., Wang, Y., Ren, Z., and Yang, Y. (2024). Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction · 2024
Closest in time.
Genome Biology 25 , 83
Karollus, A., Hingerl, J., Gankin, D., Grosshauser, M., Klemon, K., and Gagneur, J. (2024). Species-aware DNA language models capture regulatory elements and their evolution · 2024
Closest in time.
Nature Machine Intelligence 6 , 449–460
Chu, Y., Yu, D., Li, Y., Huang, K., Shen, Y., Cong, L., Zhang, J., and Wang, M. (2024). A 5’ UTR language model for decoding untranslated regions of mRNA and function predictions · 2024
Closest in time.
bioRxiv preprint. https://www.biorxiv.org/content/10.1101/2024.04.30.591835v1
Richard, G., de Almeida, B. P., Dalla-Torre, H., Blum, C., Hexemer, L., Pandey, P., Laurent, S., Lopez, M. P., Laterre, A., Lang, M. et al. (2024). ChatNT: A Multimodal Conversational Agent for DNA, RNA and Protein Tasks · 2024
Closest in time.
bioRxiv preprint ( 2024–05). https://www.biorxiv.org/content/10.1101/2024.05.10.592927v1
He, Y., Fang, P., Shan, Y., Pan, Y., Wei, Y., Chen, Y., Chen, Y., Liu, Y., Zeng, Z., Zhou, Z. et al. (2024). LucaOne: Generalized Biological Foundation Model with Unified Nucleic Acid and Protein Language · 2024
Closest in time.
bioRxiv preprint. https://www.biorxiv.org/content/10.1101/2024.06.24.600337v1
Zhu, X., Qin, C., Wang, F., Yang, F., He, B., Zhao, Y., and Yao, J. (2024). CD-GPT: A Biological Foundation Model Bridging the Gap between Molecular Sequences Through Central Dogma · 2024
Closest in time.
bioRxiv preprint ( 2024–08). https://www.biorxiv.org/content/10.1101/2024.08.14.607850v1
Cornman, A., West-Roberts, J., Camargo, A. P., Roux, S., Beracochea, M., Mirdita, M., Ovchinnikov, S., and Hwang, Y. (2024). The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling · 2024
Closest in time.
arXiv preprint arXiv:2406.16746. https://arxiv.org/abs/2406.16746
Longpre, S., Biderman, S., Albalak, A., Schoelkopf, H., McDuff, D., Kapoor, S., Klyman, K., Lo, K., Ilharco, G., San, N. et al. (2024). The responsible foundation model development cheatsheet: A review of tools & resources · 2024
Closest in time.
Cell Systems 15 , 286–294
Yang, K. K., Fusi, N., and Lu, A. X. (2024). Convolutions are competitive with transformers for protein sequence pretraining · 2024
Closest in time.
Neurocomputing 568 , 127063
Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., and Liu, Y. (2024). Roformer: Enhanced transformer with rotary position embedding · 2024
Closest in time.
bioRxiv preprint. https://www.biorxiv.org/content/10.1101/2024.06.06.597716v1
Cheng, X., Chen, B., Li, P., Gong, J., Tang, J., and Song, L. (2024). Training compute-optimal protein language models · 2024
Closest in time.
arXiv preprint arXiv:2406.04823. https://arxiv.org/abs/2406.04823
Samuel, D. (2024). BERTs are Generative In-Context Learners · 2024
Closest in time.
bioRxiv preprint. https://www.biorxiv.org/content/10.1101/2024.07.01.600583v1
Hayes, T., Rao, R., Akin, H., Sofroniew, N. J., Oktay, D., Lin, Z., Verkuil, R., Tran, V. Q., Deaton, J., Wiggert, M. et al. (2024). Simulating 500 million years of evolution with a language model · 2024
Closest in time.
Trends in Plant Science 29 , 355–369
Song, B., Buckler, E. S., and Stitzer, M. C. (2024). New whole-genome alignment tools are needed for tapping into plant diversity · 2024
Closest in time.
bioRxiv preprint ( 2024–05). https://www.biorxiv.org/content/10.1101/2024.05.13.590087v1
Phan, M. H., Zehnder, T. M., Puntieri, F., Lo, B.-W., Lenhard, B., Mueller, F., Vingron, M., and Ibrahim, D. M. (2024). Conservation of regulatory elements with highly diverged sequences across large evolutionary distances · 2024
Closest in time.
arXiv preprint. https://arxiv.org/abs/2404.10807
Livesey, B. J., Badonyi, M., Dias, M., Frazer, J., Kumar, S., Lindorff-Larsen, K., McCandlish, D. M., Orenbuch, R., Shearer, C. A., Muffley, L. et al. (2024). Guidelines for releasing a variant effect predictor · 2024
Closest in time.
Helfrich, G. (2024). The harms of terminology: why we should reject so-called “frontier AI” · 2024
Closest in time.