Computational and analytical challenges in single-cell transcriptomics

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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

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An integrated encyclopedia of DNA elements in the human genome

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Molecular portraits of human breast tumours

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Evaluation of Factors Related to Late Recurrence - Later than 10 Years after the Initial Treatment - in Primary Breast Cancer

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RNA-Seq: a revolutionary tool for transcriptomics

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Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions

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Systems biology and network medicine: An integrated approach to redox biology and pathobiology

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scASK: A Novel Ensemble Framework for Classifying Cell Types Based on Single-cell RNA-seq Data

Liu, B., Wu, F., & Zou, X. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3050963 · Cited by 5

Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision

Xie, J., Yin, Y., Yang, F., Sun, J., & Wang, J. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-020-00415-2 · Cited by 5

DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

Ghannoum, S., Netto, W. L., Fantini, D., Ragan-Kelley, B., Parizadeh, A., Jonasson, E., Ståhlberg, A., Farhan, H., & Köhn‐Luque, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22031399 · Cited by 5

Single-Cell Quantitative Phenotyping via the Aptamer-Mounted Nest-PCR (Apt-nPCR)

Gao, T., Zhao, S., Sun, J., Huang, Q., Long, S., Lv, M., Ma, J., Guo, Z., & Li, G. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.1c03865 · Cited by 5

SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their TransitionsPreprint

Peng, T., & Qing, N. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/124693 · Cited by 6

Dynamic Analysis of Alternative Polyadenylation from Single-Cell RNA-Seq (scDaPars) Reveals Cell Subpopulations Invisible to Gene Expression AnalysisPreprint

Gao, Y., Li, L., Amos, C. I., & Li, W. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.23.310649 · Cited by 5

Unsupervised Ground Metric Learning using Wasserstein Singular VectorsPreprint

Huizing, G.-J., Cantini, L., & Gabriel Peyré, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06278 · Cited by 5

Multimodal foundation model predicts zero-shot functional perturbations and cell fate dynamicsPreprint

Yang, Z., Fan, X., Lan, M., Tang, X., Zheng, Z., Liu, B., You, Y., Tian, L., Church, G. M., Liu, X., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.19.629561 · Cited by 5

Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting Calling Cards data

Guo, J., Zhang, W., Chen, X., Yen, A., Chen, L. L., Shively, C. A., Li, D., Wang, T., Dougherty, J. D., & Mitra, R. D. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae070 · Cited by 4

Decoding NY-ESO-1 TCR T cells: transcriptomic insights reveal dual mechanisms of tumor targeting in a melanoma murine xenograft model

Alsalloum, A., Alrhmoun, S., Perik-Zavosdkaia, O., Fisher, M. S., Volynets, M., Lopatnikova, J., Perik-Zavodskii, R., Shevchenko, J. A., Philippova, J., Solovieva, O. I., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1507218 · Cited by 4

Genetic features for drug responses in cancer — Investigating an ensemble-feature-selection approach

Schlüter, J., & Schönhuth, A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110572 · Cited by 4

A survey of biclustering and clustering methods in clustering different types of single-cell RNA sequencing data

Lan, C., Tang, X., & Liu, C. (2025) · Briefings in Functional Genomics · doi:10.1093/bfgp/elaf010 · Cited by 4

A message passing framework for precise cell state identification with scClassify2

Ding, W., Cao, Y., Fu, X., Torkel, M., & Yang, J. Y. H. (2025) · Genome biology · doi:10.1186/s13059-025-03722-3 · Cited by 4

Materiobiology in the omics era

Song, P., Wu, Y., Zhang, C., Zhou, F., Bai, L., & Su, J. (2025) · Materials Today Bio · doi:10.1016/j.mtbio.2025.102539 · Cited by 5

Biological big data analysis in the post-genomic era: applications and limitations

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Single-Cell Genomics: Enabling the Functional Elucidation of Infectious Diseases in Multi-Cell Genomes

Sahni, S., Chattopadhyay, P., Khare, K., & Pandey, R. (2021) · Pathogens · doi:10.3390/pathogens10111467 · Cited by 4

BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data

O’Callaghan, A., Eling, N., Marioni, J. C., & Vallejos, C. A. (2022) · F1000Research · doi:10.12688/f1000research.74416.1 · Cited by 4

Robust scRNA-seq Cell Types Identification by Self-Guided Deep Clustering Network

Wang, Y., & Wang, M. (2024) · IEEE Access · doi:10.1109/access.2024.3394038 · Cited by 4

Preoperative and Intraoperative Identification of Sentinel Lymph Nodes in Melanoma Surgery

Leong, S. P. L., Nosrati, M., Wu, M. C., Torre, D. M., Bartley, T. F., Kim, K. B., Soon, C. W. M., Moretto, J. C., & Kashani–Sabet, M. (2024) · Cancers · doi:10.3390/cancers16152767 · Cited by 4

CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data

Juan, W., Ahn, K. W., Chen, Y., & Lin, C. (2025) · Bioengineering · doi:10.3390/bioengineering12010031 · Cited by 4

Simplicity within biological complexity

Pržulj, N., & Malod‐Dognin, N. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae164 · Cited by 4

Enabling scalable single-cell transcriptomic analysis through distributed computing with Apache spark

Adil, A., Bhattacharya, N., Aadam, Khan, N. J., & Asger, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-12897-5 · Cited by 4

Inference of genetic regulatory network for stem cell using single cells expression data

Wei, J., Hu, X. T., Zou, X., & Tian, T. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822521 · Cited by 4

Single-cell insights into transcriptomic diversity in immunity

Miragaia, R. J., Teichmann, S. A., & Hagai, T. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.08.003 · Cited by 5

Innovative Molecular Testing Strategies for Adjunctive Investigations in Hemostasis and Thrombosis

Ghorbanpour, E., & Lillicrap, D. (2019) · Seminars in Thrombosis and Hemostasis · doi:10.1055/s-0039-1692977 · Cited by 4

Reconstructing and analysing cellular states, space and time from gene expression profiles of many cells and single cells

Francesconi, M., & Lehner, B. (2015) · Molecular BioSystems · doi:10.1039/c5mb00339c · Cited by 6

Single-cell Quantitation of mRNA and Surface Protein Expression in Simian Immunodeficiency Virus-infected CD4+ T Cells Isolated from Rhesus macaques

Tokarev, A., Creegan, M., Eller, M. A., Roederer, M., & Bolton, D. L. (2018) · Journal of Visualized Experiments · doi:10.3791/57776 · Cited by 4

Normalization for Single-Cell RNA-Seq Data Analysis

Bacher, R. L. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9057-3_2 · Cited by 4

Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions

Chan, T. E., Stumpf, M. P. H., & Babtie, A. C. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9224-9_10 · Cited by 4

Single-Cell Sequencing of T cell Receptors: A Perspective on the Technological Development and Translational Application

Gupta, S., Witas, R., Voigt, A., Semenova, T., & Nguyen, C. Q. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-981-15-4494-1_3 · Cited by 4

Cell-specific gene association network construction from single-cell RNA sequence

Azim, R., & Wang, S. (2021) · Cell Cycle · doi:10.1080/15384101.2021.1978265 · Cited by 4

Optical Cell Tagging for Spatially Resolved Single‐Cell RNA Sequencing

Tang, Q., Liu, L., Guo, Y., Zhang, X., Zhang, S., Yan, J., Du, Y., Cheng, B., Yang, L. C., Huang, Y., et al. (2021) · Angewandte Chemie · doi:10.1002/ange.202113929 · Cited by 4

Genetic Epidemiology of Complex Phenotypes

O’Rielly, D. D., & Rahman, P. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1138-8_19 · Cited by 4

Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix

Jiang, H.-J., Huang, Y., & Li, Q. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac113 · Cited by 4

GAN-Based Training of Semi-Interpretable Generators for Biological Data Interpolation and Augmentation

Tsourtis, A., Παπουτσόγλου, Γ., & Pantazis, Y. (2022) · Applied Sciences · doi:10.3390/app12115434 · Cited by 4

Enhancing European capabilities for application of multi-omics studies in biology and biomedicine space research

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FEED: a feature selection method based on gene expression decomposition for single cell clustering

Zhang, C., Duan, Z.-W., Xu, Y., Liu, J., & Li, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad389 · Cited by 4

Gene Regulatory Network Inference Using Time-Stamped Cross-Sectional Single Cell Expression Data

Gao, N. P., Ud-Dean, S. M. M., & Gunawan, R. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.12.117 · Cited by 4

Tree‐ensemble analysis assesses presence of multifurcations in single cell data

Macnair, W., Roditi, L. D. V., Ganscha, S., & Claassen, M. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188552 · Cited by 4

FORKS: Finding Orderings Robustly using k-means and Steiner treesPreprint

Sharma, M., Li, H., Sengupta, D., Prabhakar, S., & Jayadeva (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/132811 · Cited by 4

Information topology of gene expression profile in dopaminergic neuronsPreprint

Tapia, M., Baudot, P., Dufour, M. A., Formisano‐Tréziny, C., Temporal, S., Lasserre, M., Gabert, J. A., Kobayashi, K., & Goaillard, J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/168740 · Cited by 4

Quasi-universality in single-cell sequencing dataPreprint

Aparicio, L., Bordyuh, M., Blumberg, A. J., & Rabadán, R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/426239 · Cited by 5

Extraordinary diversity of HLA class I gene expression in single cells contribute to the plasticity and adaptability of human immune systemPreprint

Tian, R., Zhu, H., Pang, Z., Tian, Y., & Liang, C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/725119 · Cited by 4

Metric learning enables synthesis of heterogeneous single-cell modalitiesPreprint

Singh, R., Hie, B., Narayan, A., & Berger, B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/834549 · Cited by 4

Adaptative Machine Translation between paired Single-Cell Multi-Omics DataPreprint

Martinez‐de‐Morentin, X., Khan, S. A., Lehmann, R., Qu, S., Maillo, A., Kiani, N. A., Prósper, F., Tegner, J. N., & Gómez-Cabrero, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.27.428400 · Cited by 4

SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their TransitionsPreprint

Peng, T., & Nie, Q. H. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/124735 · Cited by 4

IGCLAPS: an interpretable graph contrastive learning method with adaptive positive sampling for scRNA-seq data analysis

Zheng, W., Min, W., & Wang, S. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf411 · Cited by 3

Deciphering the Heterogeneity of Pulmonary Macrophages in Response to Fine Particles

Shan, Q., Dong, Z., Li, N., Zhou, Z., Ren, J., Liu, W., Xu, F., Qi, Y., Ma, J., Liu, Y., et al. (2026) · Small · doi:10.1002/smll.202507293 · Cited by 3

SPSID: A Single-Parameter Shrinkage Inverse-Diffusion for Denoising Gene-Regulatory Networks

Chen, H., Han, G., Ding, W., & Grazian, C. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2026.3674779 · Cited by 4

Enhancing statistical accuracy in gene perturbation studies

Kalmotia, V. (2026) · Biosystems · doi:10.1016/j.biosystems.2026.105819 · Cited by 3

A cell marker‐based clustering strategy (cmCluster) for precise cell type identification of scRNA‐seq data

Huang, Y., Chang, H., Chen, X., Meng, J., Han, M., Huang, T., Yuan, L., & Zhang, G. (2023) · Quantitative Biology · doi:10.15302/j-qb-022-0311 · Cited by 3

New generative methods for single-cell transcriptome data in bulk RNA sequence deconvolution

Nishikawa, T., Lee, M. F. R., & Amau, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-54798-z · Cited by 3

Single-cell technology for plant metabolomics

Seth, D., Das, A., & Debnath, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23736-2.00011-3 · Cited by 3

WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq

Wang, Y., & Du, P.-F. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1553352 · Cited by 3

A comparison of integration methods for single‐cell RNA sequencing data and ATAC sequencing data

Kan, Y., Wang, W., Qi, Y., Zhang, Z., Liang, X., & Jin, S. (2025) · Quantitative Biology · doi:10.1002/qub2.91 · Cited by 4

The zebrafish ETS transcription factor Fli1b functions upstream of Scl/Tal1 during embryonic hematopoiesis

Laverde, V., Loges, L. N., & Sumanas, S. (2025) · Biology Open · doi:10.1242/bio.061948 · Cited by 3

Single-cell omics: moving towards a new era in ischemic stroke research

Zeng, J., Zhou, H., Wan, H., & Yang, J. (2025) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2025.177725 · Cited by 3

Regenerating the Senses: Stem Cell‐Derived Sensory Neurons and Functional Genomics in Neurological Disorders

Muthuirulan, P., Nityanand, A., Pandit, A., & Gururajan, V. (2025) · Sensory Neuroscience · doi:10.1002/sen2.70014 · Cited by 3

Accounting for technical noise in Bayesian graphical models of single-cell RNA-sequencing data

Oh, J., Chang, C., & Long, Q. (2021) · Biostatistics · doi:10.1093/biostatistics/kxab011 · Cited by 3

Transcriptomics at the Single Cell Level and Human Diseases: Opportunities and Challenges in Data Processing and Analysis

Maracaja‐Coutinho, V., & Severino, P. (2022) · Springer eBooks · doi:10.1007/978-3-030-87821-4_5 · Cited by 3

Two‐sample test withg‐modeling and its applications

Zhai, J., & Jiang, H. (2022) · Statistics in Medicine · doi:10.1002/sim.9603 · Cited by 3

Computational single-cell methods for predicting cancer risk

Teschendorff, A. E. (2024) · Biochemical Society Transactions · doi:10.1042/bst20231488 · Cited by 3

A comprehensive review of the applications of RNA sequencing in celiac disease research

Shoaran, M., Sabaie, H., Mostafavi, M., & Rezazadeh, M. (2024) · Gene · doi:10.1016/j.gene.2024.148681 · Cited by 3

Systematic benchmark of single-cell hashtag demultiplexing approaches reveals robust performance of a clustering-based method

Sayed, M., Wang, Y. J., & Lim, H. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae039 · Cited by 3

Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data

Vo, D. H. T., & Thorne, T. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05946-9 · Cited by 3

Cell-specific genetic expression profile of antennal glia in Drosophila reveals candidate genes in neuron-glia interactions

Castañeda-Sampedro, A., Alcorta, E., & Gomez‐Diaz, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-87834-7 · Cited by 3

CellUntangler: Separating distinct biological signals in single-cell data with deep generative models

Chen, S., Regev, A., Condon, A., & Ding, J. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101073 · Cited by 6

Direct In Vivo Microbial Transcriptomics During Infection

Hébert, F.-O., Boyle, B., & Lévesque, R. C. (2018) · Trends in Microbiology · doi:10.1016/j.tim.2018.07.002 · Cited by 3

A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data

Liu, Y., Warren, J. L., & Zhao, H. (2019) · The Annals of Applied Statistics · doi:10.1214/19-aoas1250 · Cited by 3

Exploration of the personalized immune checkpoint atlas of plasma cell dyscrasias patients using high‑dimensional single‑cell analysis

Tu, C., Zheng, Y., Zhang, H., & Wang, J. (2020) · Oncology Reports · doi:10.3892/or.2020.7587 · Cited by 3

MKG: a mutual information based method to infer single cell gene regulatory network

Zeng, Y., Yan, X., Liang, Z., Zheng, R., & Li, M. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669880 · Cited by 3

Techniques for Analyzing Genome-wide Expression of Non-coding RNA

Onoguchi‐Mizutani, R., Taniue, K., Kawata, K., Yamada, T., & Akimitsu, N. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91909-8.00039-6 · Cited by 3

An End-to-End Deep Hybrid Autoencoder Based Method for Single-Cell RNA-Seq Data Analysis

Ji, C., Yu, N., Wang, Y., Qi, R., & Zheng, C.-H. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3328029 · Cited by 3

MLBKFD: Probabilistic Model Methods to Infer Pseudo Trajectories from Single-cell Data

Han, C., Cao, W., Li, C., Guo, Y., Wang, Y., Shi, Y.-Z., & Zhang, B. (2023) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416524400015 · Cited by 3

Improving understanding of chromatin regulatory proteins and potential implications for drug discovery

Rafehi, H., Khan, A. W., & El‐Osta, A. (2016) · Expert Review of Proteomics · doi:10.1586/14789450.2016.1159960 · Cited by 3

Biophysics across time and space

Paluch, E. K. (2018) · Nature Physics · doi:10.1038/s41567-018-0206-9 · Cited by 3

Full speed ahead for single-cell analysis

Burgess, D. J. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0049-3 · Cited by 3

Controlling for confounding effects in single cell RNA sequencing studies using both control and target genesPreprint

Chen, M., & Zhou, X. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/045070 · Cited by 3

Stochastic Modeling and Statistical Inference of Intrinsic Noise in Gene Regulation System via Chemical Master EquationPreprint

Du, C., & Wong, W. H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.07213 · Cited by 3

Reply to The contribution of cell cycle to heterogeneity in single-cell RNA-seq data

McDavid, A., Finak, G., & Gottardo, R. (2016) · Nature Biotechnology · doi:10.1038/nbt.3607 · Cited by 3

Bioinformatics and Biomedical Computing

Cai, H., Chen, J., Zhang, F., & Wang, J. (2024) · Fundamental Research · doi:10.1016/j.fmre.2024.06.001 · Cited by 3

Principled PCA separates signal from noise in omics count dataPreprint

Stanley, J. S., Yang, J., Li, R., Lindenbaum, O., Kobak, D., Landa, B., & Kluger, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.03.636129 · Cited by 3

Cell Type Populations for 3D Anatomical Structures of the Human Reference Atlas

Bueckle, A., Herr, B. W., Chen, L., Bolin, D., Qaurooni, D., Ginda, M., Jain, Y., Puig-Barbe, A., Ardlie, K., Wang, F., et al. (2026) · Scientific Data · doi:10.1038/s41597-026-06642-4 · Cited by 4

Modélisation stochastique de l'expression des gènes et inférence de réseaux de régulation

Herbach, U. (2018) · theses.fr (ABES) · doi:10.70675/891893e9z94a8z4733z97dfz7e3508b89aaa · Cited by 3

SINGLE CELL ANALYSIS, WHAT IS IN THE FUTURE?

Garmire, L. X., Yuan, G.-C., Fan, R., Yeo, G. W., & Quackenbush, J. (2018) · Biocomputing 2019 · doi:10.1142/9789813279827_0030 · Cited by 2

Sequencing-based methods for single-cell multi-omics studies

Qin, S., Liu, S., & Weng, X. (2023) · Science China Chemistry · doi:10.1007/s11426-023-1793-7 · Cited by 2

p-clustval: a novel $$p$$-adic approach for enhanced clustering of high-dimensional single-cell RNASeq data

Sharma, P., Mishra, S. R., Kurban, H., & Dalkilic, M. M. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00709-4 · Cited by 2

scGANCL: Bidirectional Generative Adversarial Network for Imputing scRNA-Seq Data With Contrastive Learning

Shi, W., Long, Y., Luo, J., Liu, Y., Xiong, Z., Wang, B., & Zhong-yuan, X. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3530928 · Cited by 2

Heterogeneity Landscape of Circulating Tumor Cells in Clinical Utility

Alam, M. K., Yanjun, L., Khoo, B. L., Si, T., Zou, H., & Yang, M. (2025) · Advanced Therapeutics · doi:10.1002/adtp.202400438 · Cited by 3

scVGAMF: a novel imputation method for scRNA-seq data by integrating linear and non-linear features

Zhou, Z., Zhang, W., Zheng, X., Shen, J., & Li, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf562 · Cited by 2

scMFE: A multi-view fusion enhanced graph contrastive learning method for scRNA-seq data clustering

Bai, Y., Shu, Z., Tan, K., Zhang, Y., & Yu, Z. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132662 · Cited by 2

Beyond the Cell Atlas

Li, Y., Ma, T., Lu, H., Yan, Y., Xue, Y., Suo, J., Chen, Y., & Peng, Z. (2026) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000001023 · Cited by 2

A machine learning-based megakaryocyte identification system uncovers resident organs, markers, and functional diversity

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A framework for translational research in interstitial lung disease (ILD) using bronchoalveolar lavage (BAL)

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