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Cell identity encompasses various semantic aspects of a cell, including cell type, pathway information, disease information, and more, which are essential for biologists to gain insights into its biological characteristics.
Liii. on lines and planes of closest fit to systems of points in space
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The obo foundry: coordinated evolution of ontologies to support biomedical data integration
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Principal component analysis
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Molecular signatures database (msigdb) 3.0
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The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis
Qiu, X., Wu, H., and Hu, R · 2013
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Accelerating t-sne using tree-based algorithms
van der Maaten, L · 2014
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Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
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Massively parallel digital transcriptional profiling of single cells
Zheng, G. X., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al · 2017
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Comparative analysis of single-cell rna sequencing methods
Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., and Enard, W · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Naught all zeros in sequence count data are the same
Silverman, J. D., Roche, K., Mukherjee, S., and David, L. A · 2018
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Autoimpute: Autoencoder based imputation of single-cell rna-seq data
Talwar, D., Mongia, A., Sengupta, D., and Majumdar, A · 2018
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Cell identity codes: understanding cell identity from gene expression profiles using deep neural networks
Abdolhosseini, F., Azarkhalili, B., Maazallahi, A., Kamal, A., Motahari, S. A., Sharifi-Zarchi, A., and Chitsaz, H · 2019
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. B · 2019
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Current best practices in single-cell rna-seq analysis: a tutorial
Luecken, M. D. and Theis, F. J · 2019
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The evolving concept of cell identity in the single cell era
Morris, S. A · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N. and Gurevych, I · 2019
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Droplet scrna-seq is not zero-inflated
Svensson, V · 2019
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Fast and precise single-cell data analysis using a hierarchical autoencoder
Tran, D., Nguyen, H., Tran, B., la Vecchia, C., Luu, H. N., and Nguyen, T · 2019
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Benchmarking principal component analysis for large-scale single-cell rna-sequencing
Tsuyuzaki, K., Sato, H., Sato, K., and Nikaido, I · 2019
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Sparsely-connected autoencoder (sca) for single cell rnaseq data mining
Alessandri, L., Cordero, F., Beccuti, M., Licheri, N., Arigoni, M., Olivero, M., Renzo, F. D., Sapino, A., and Calogero, R. A · 2020
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Evaluation of cell type annotation r packages on single-cell rna-seq data
Huang, Q., Liu, Y., Du, Y., and Garmire, L. X · 2020
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On the sentence embeddings from pre-trained language models
Li, B., Zhou, H., He, J., Wang, M., Yang, Y., and Li, L · 2020
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SimCSE: Simple contrastive learning of sentence embeddings
Genenames.org: the hgnc resources in 2023
Seal, R. L., Braschi, B., Gray, K. A., Jones, T. E. M., Tweedie, S., Haim-Vilmovsky, L., and Bruford, E. A · 2022
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sccan: single-cell clustering using autoencoder and network fusion
Tran, B., Tran, D., Nguyen, H., Ro, S., and Nguyen, T · 2022
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A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals
Zeng, Z., Yao, Y., Liu, Z., and Sun, M · 2022
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Cz cellxgene discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data
Biology, C. S.-C., Abdulla, S., Aevermann, B., Assis, P., Badajoz, S., Bell, S. M., Bezzi, E., Cakir, B., Chaffer, J., Chambers, S., et al · 2023
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Genept: A simple but hard-to-beat foundation model for genes and cells built from chatgpt
Chen, Y. T. and Zou, J · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Gao, T., Yao, X., and Chen, D · 2021
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Domain-specific language model pretraining for biomedical natural language processing
Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., and Poon, H · 2021
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scsorter: assigning cells to known cell types according to marker genes
Guo, H. and Li, J · 2021
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Dnabert: Pre-trained bidirectional encoder representations from transformers model for dna-language in genome
Ji, Y., Zhou, Z., Liu, H., and Davuluri, R. V · 2021
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cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices
Megill, C., Martin, B., Weaver, C., Bell, S., Prins, L., Badajoz, S., McCandless, B., Pisco, A. O., Kinsella, M., Griffin, F., Kiggins, J., Haliburton, G., Mani, A., Weiden, M., Dunitz, M., Lombardo, M., Huang, T., Smith, T., Chambers, S., Freeman, J., Cool, J., and Carr, A · 2021
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Multi-modal self-supervised pre-training for regulatory genome across cell types, 2021
Mo, S., Fu, X., Hong, C., Chen, Y., Zheng, Y., Tang, X., Shen, Z., Xing, E. P., and Lan, Y · 2021
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Automated methods for cell type annotation on scrna-seq data
Pasquini, G., Arias, J. E. R., Schäfer, P., and Busskamp, V · 2021
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Connell, W., Khan, U., and Keiser, M. J · 2023
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scgpt: Towards building a foundation model for single-cell multi-omics using generative ai
Cui, H., Wang, C. X., Maan, H., Pang, K., Luo, F., and Wang, B · 2023
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xtrimogene: An efficient and scalable representation learner for single-cell rna-seq data
Gong, J., Hao, M., Cheng, X., Zeng, X., Liu, C., Ma, J., Zhang, X., Wang, T., and Song, L · 2023
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Large scale foundation model on single-cell transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Song, L. T., and Zhang, X · 2023
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Cell2sentence: Teaching large language models the language of biology
Levine, D., Lévy, S., Rizvi, S. A., Pallikkavaliyaveetil, N., Chen, X., Zhang, D., Ghadermarzi, S., Wu, R., Zheng, Z., Vrkic, I., et al · 2023
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Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Sayed, Z. R. A., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., and Ellinor, P. T · 2023
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Attention is all you need, 2023
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2023
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Realistic cell type annotation and discovery for single-cell rna-seq data
Zhai, Y., Chen, L., and Deng, M · 2023
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A python library for probabilistic analysis of single-cell omics data
Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., Liu, Y., Samaran, J., Misrachi, G., Nazaret, A., Clivio, O., Xu, C., Ashuach, T., Gabitto, M., Lotfollahi, M., Svensson, V., da Veiga Beltrame, E., Kleshchevnikov, V., Talavera-López, C., Pachter, L., Theis, F. J., Streets, A., Jordan, M. I., Regier, J., and Yosef, N · 2024
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Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm
Li, Y., Liang, F., Zhao, L., Cui, Y., Ouyang, W., Shao, J., Yu, F., and Yan, J · 2024
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scclassify: sample size estimation and multiscale classification of cells using single and multiple reference
Lin, Y., Cao, Y., Kim, H. J., Salim, A., Speed, T. P., Lin, D. M., Yang, P., and Yang, J. Y. H · 2024
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Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., and Theis, F. J · 2024
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What do self-supervised vision transformers learn?
Park, N., Kim, W., Heo, B., Kim, T., and Yun, S · 2024
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Transcriptomic diversity of cell types across the adult human brain
Siletti, K., Hodge, R., Mossi Albiach, A., Lee, K. W., Ding, S.-L., Hu, L., Lönnerberg, P., Bakken, T., Casper, T., Clark, M., Dee, N., Gloe, J., Hirschstein, D., Shapovalova, N. V., Keene, C. D., Nyhus, J., Tung, H., Yanny, A. M., Arenas, E., Lein, E. S., and Linnarsson, S · 2024
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