Variable combinatorial gap-filling method for single-cell RNA-seq data

[1] Sa, J. M., Cannon, M. V., Caleon, R. L., et al. (2020). Single-cell transcription analysis of Plasmodium vivax blood-stage parasites identifies stage- And species-specific profiles of expression. PLoS Biology, 18(5), e3000711. Search in Google Scholar

[2] Adey, A. C. (2021). Single-cell multiomics to probe relationships between histone modifications and transcription. Nature Methods, 18(6), -. Search in Google Scholar

[3] Pokhilko, A., Handel, A. E., Curion, F., et al. (2021). Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories. Genome Research, 31(6). Search in Google Scholar

[4] Darden, D. B., Bacher, R., Brusko, M. A., et al. (2020). Single Cell RNA-SEQ of Human Myeloid Derived Suppressor Cells in Late Sepsis Reveals Multiple Subsets with Unique Transcriptional Responses: A Pilot Study. Shock, publish ahead of print. Search in Google Scholar

[5] Turnbull, T., Thierry, B., Kempson, I. (2019). A quantitative study of intercellular heterogeneity in gold nanoparticle uptake across multiple cell lines. Analytical and Bioanalytical Chemistry, 411(28), 7529-7538. Search in Google Scholar

[6] Eiro, Gonzalez, Fraile, et al. (2019). Breast Cancer Tumor Stroma: Cellular Components, Phenotypic Heterogeneity, Intercellular Communication, Prognostic Implications and Therapeutic Opportunities. Cancers, 11(5), 664-. Search in Google Scholar

[7] Feng, Z., Li, J., Zhang, J. R., et al. (2015). qDNAmod: a statistical model-based tool to reveal intercellular heterogeneity of DNA modification from SMRT sequencing data. Nucleic Acids Research, 6. Search in Google Scholar

[8] J, Zhang, J, et al. (2015). qDNAmod: a statistical model-based tool to reveal intercellular heterogeneity of DNA modification from SMRT sequencing data (vol 42, pg 13488, 2014). Nucleic Acids Research, 43(6), 3406-3406. Search in Google Scholar

[9] Gilad, Y., Eliaz, Y., Yang, Y., et al. (2021). A genome-scale CRISPR Cas9 dropout screen identifies synthetically lethal targets in SRC-3 inhibited cancer cells. Communications Biology, 4(1). Search in Google Scholar

[10] Yang, H. Y., Staib, L. H. (2019). Leukemic B-Lymphoblast Cell Detection with Monte Carlo Dropout Ensemble Models. Search in Google Scholar

[11] Jeong, H., Liu, Z. (2020). PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing. Oxford Academic, 13. Search in Google Scholar

[12] Wang, L., Zho, H., Liu, W., et al. (2021). High-resolution seismic acoustic impedance inversion with the sparsity-based statistical model. Geophysics: Journal of the Society of Exploration Geophysicists, 86-4. Search in Google Scholar

[13] Hu, X., Zhang, J., Li, J., et al. (2014). Sparsity-Regularized HMAX for Visual Recognition. Plos One, 9(1), e81813. Search in Google Scholar

[14] Cui, P. F., Zhang, J. A., Lu, W. J., et al. (2019). Statistical Sparse Channel Modeling for Measured and Simulated Wireless Temporal Channels. IEEE Transactions on Wireless Communications, PP(99), 1-1. Search in Google Scholar

[15] Kostal, L., Shinomoto, S. (2017). Efficient information transfer by Poisson neurons. Mathematical Biosciences & Engineering, 13(3), 509-520. Search in Google Scholar

[16] Wada, T., Wataya, M., Fujii, M., et al. (2019). Single-cell information analysis reveals small intra- and large intercellular variations increase cellular information capacity. Cold Spring Harbor Laboratory. Search in Google Scholar

[17] Jalasvuori, M. (2013). doi:10.1155/2012/874153 Research Article Vehicles, Replicators, and Intercellular Movement of Genetic Information: Evolutionary Dissection of a Bacterial Cell. Search in Google Scholar

[18] Eckford, A. W., Thomas, P. J. (2016). Information theory of intercellular signal transduction. In Asilomar Conference on Signals. IEEE. Search in Google Scholar

[19] Liu, J., Su, R., Zhang, J., et al. (2021). Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network. Briefings in Bioinformatics. Search in Google Scholar

[20] Way, G. P., Greene, C. S. (2017). Evaluating deep variational autoencoders trained on pan-cancer gene expression. Search in Google Scholar

[21] Shah, S. H., Iqbal, M. J., Ahmad, I., et al. (2020). Optimized gene selection and classification of cancer from microarray gene expression data using deep learning. Neural Computing and Applications, 3/4, 1-12. Search in Google Scholar

[22] Deng, Y., Bao, F., Dai, Q., et al. (2019). Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning. Nature Methods, 16(4), 311-314. Search in Google Scholar

[23] Arisdakessian, C., Poirion, O., Yunits, B., et al. (2019). DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data. Genome Biology, 20(1), 211-. Search in Google Scholar

[24] Talwar, D., Mongia, A., Sengupta, D., et al. (2018). AutoImpute: Autoencoder based imputation of single-cell RNA-seq data. Scientific Reports, 8(1). Search in Google Scholar

[25] Eraslan, G., Simon, L. M., Mircea, M., et al. (Year). Single-cell RNA-seq denoising using a deep count autoencoder. Nature Communications. Search in Google Scholar

[26] Zhang, X. F. (2019). EnImpute: Imputing dropout events in single-cell RNA-sequencing data via ensemble learning. Bioinformatics, 35(22). Search in Google Scholar

[27] Zhang, X., Ou-Yang, L., Yang, S., et al. (2019). EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning. Bioinformatics (Oxford, England), 35(22), 4827-4829. Search in Google Scholar

[28] Torres-Vanegas, J. D., Cruz, J. C., Reyes, L. H. (2021). Delivery Systems for Nucleic Acids and Proteins: Barriers, Cell Capture Pathways and Nanocarriers. Pharmaceutics (Basel, Switzerland), 13(3). Search in Google Scholar

[29] Bastian, F., Bastian, F., Bastian, F., et al. (2021). Eukaryotic Cell Capture by Amplified Magnetic in situ Hybridization Using Yeast as a Model. Frontiers in Microbiology, 12, 3344-. Search in Google Scholar

[30] Mt, A., Eqlb, C., Uncb, C., et al. (2020). Integration of rare cell capture technology into cytologic evaluation of cerebrospinal fluid specimens from patients with solid tumors and suspected leptomeningeal metastasis. Journal of the American Society of Cytopathology, 9(1), 45-54. Search in Google Scholar

[31] Zhang, Z., Tian, H., Lv, P., et al. (2015). Transcriptional Factor DLX3 Promotes the Gene Expression of Enamel Matrix Proteins during Amelogenesis. Plos One, 10(3), e0121288. Search in Google Scholar

[32] Youssefi, R., Mazaheri, et al. (2015). Effect of extracellular matrix on bovine spermatogonial stem cells and gene expression of niche factors regulating their development in vitro. Animal Reproduction Science, 157, 22-31. Search in Google Scholar

[33] Jiayin, Ma, Dan, et al. (2015). Cyclic stretch induced gene expression of extracellular matrix and adhesion molecules in human periodontal ligament cells. Archives of Oral Biology, 60(11), 1625-1635. Search in Google Scholar

[34] Park, J., Choi, J., Seol, S. J., et al. (2021). A method for adequate selection of training data sets to reconstruct seismic field data using a convolutional U-Net. Geophysics, 1-103. Search in Google Scholar

[35] Paheding, S., Reyes, A. A., Kasaragod, A., et al. (2022). GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification. In Proceedings of the conference name (pp. 1-6). Search in Google Scholar

[36] Chen, H., Liu, J., Lu, L., et al. (2022). Volumetric segmentation of ground glass nodule based on 3D attentional cascaded residual U-Net and conditional random field. Medical Physics, 49. Search in Google Scholar