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Publications

Lab members are underlined.

denotes equal contribution   * denotes co-corresponding authors.

used Xin Maizie Zhou (X. M. Zhou) for publications after 2022.

Preprints/under review

CNVeil enables accurate and robust tumor subclone identification and copy number estimation from single-cell DNA sequencing data.

W. Yuan†,  C. Luo, Y. Hu, L. Zhang, Z.-H. Wen, Y. H. Liu, X. Mallory, X. M. Zhou.

bioRxiv (2024)

 

VolcanoSV enables accurate and robust structural variant calling in diploid genomes from single-molecule long read sequencing.

C. Luo†, Y. H. Liu, X. M. Zhou.

Preprint (2024)

 

MaskGraphene: Advancing joint embedding, clustering, and batch correction for spatial transcriptomics using graph-based self-supervised learning.

Y. Hu, Y. Li, M. Xie, M. Rao, Y. H. LiuX. M. Zhou.

bioRxiv (2023)

 

SCCNAPolisher: a robust tool to correct absolute copy number inference on scDNA-seq data.

L. Zhang, X. M. Zhou, X. Mallory.

(2023)

 

Parallel signatures of cognitive maturation in primate antisaccade performance and prefrontal activity.

J. Zhu, X. M. Zhou, C. Constantinidis, E. Salinas, T. R. Stanford

(2023)

 

Large indel detection in region-based phased diploid assemblies from linked-reads.

C. Luo, B. A. Peters, X. M. Zhou.

BMC genomics (2023) (under revision) (accepted by ISBRA 2021) (acceptance rate: 17.6%)

 

Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data.

Y. H. LiuC. Luo†, S. G. Golding, J. B. Ioffe, X. M. Zhou.

Preprint (2022)

 

Peer-reviewed Publications

2023

Laminar pattern of adolescent development changes in working memory neuronal activity.

J. Zhu, B. Hammond, X. M. Zhou, C. Constantinidis.

J. Neurophysiology (2023) (accepted)

 

Editorial: predicting high-risk individuals for common diseases using multi-omics and epidemiological data, volume II. 

W. P. Veldsman, X. M. Zhou, Y. Zhang, B. Li, L. Zhang.

Front. Genet. (2023) 14:1280648.

 

ADEPT: autoencoder with differentially expressed genes and imputation for robust spatial transcriptomics clustering.

Y. Hu, Y. Zhao, C. T. Schunk, Y. Ma, T. Derr*, X. M. Zhou*.

RECOMB-Seq 2023, iScience  (2023) 26(6), 106792. 

 

Haplotyping-assisted diploid assembly and variant detection with linked-reads. 

Y. Hu, C. Yang, L. Zhang*, X. Zhou*.

Methods Mol Biol (2023) 2590:161-182. doi: 10.1007/978-1-0716-2819-5_1. PMID: 36335499.

 

2022

Haplotype-phasing of long-read HiFi data to enhance structural variant detection through a Skip-Gram model.

C. Luo, P. A. Datar, Y. H. Liu, X. Zhou.

IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, USA, 2022, pp. 2326-2333, doi: 10.1109/BIBM55620.2022.9995293.

 

A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants.

C. Wang, J. Zhang, X. Zhou, L. Zhang.

Briefings in Bioinformatics (2022) bbac552. https://doi.org/10.1093/bib/bbac552

 

Automated filtering of genome-wide large deletions through an ensemble deep learning framework. 

Y. Hu, S. Mangal, L. Zhang, X. Zhou.

Methods (2022) 206, 77-86.  https://doi.org/10.1016/j.ymeth.2022.08.001

 

Graphing cell relations in spatial transcriptomics.

X. Zhou.

Nature Computational Science (2022) 2, 354-355.  https://doi.org/10.1038/s43588-022-00269-2

 

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA reveals novel spatial biology.

W. Zhang, I. Li, N. E. Reticker-Flynn, Z. Good, S. Chang, N. Samusik, S. Saumyaa, Y. Li, X. Zhou, et al.

Nature Methods (2022) 9, 759–769.  https://doi.org/10.1038/s41592-022-01498-z

 

Strong gamma frequency oscillations in the adolescent prefrontal cortex.

Z. Wang, B. Singh, X. Zhou*, C. Constantinidis*.   (* co-corresponding and co-senior authors)

Journal of Neuroscience (2022) 42 (14) 2917-2929.  https://doi.org/10.1523/JNEUROSCI.1604-21.2022

 

Benchmarking challenging small variants with linked and long reads. 

J. Wagner, N. D. Olson…, X. Zhou,…, J. Zook.

Cell Genomics (2022) 2(5), 100128.   https://doi.org/10.1016/j.xgen.2022.100128

 

Neural mechanisms of working memory accuracy revealed by recurrent neural networks.

Y. Xie, Y. H. Liu, C. Constantinidis, X. Zhou.

Front. Syst. Neurosci. (2022) 16:760864. https://doi.org/10.3389/fnsys.2022.760864

 

A Bayesian factorization method to recover single-cell RNA sequencing data.

Z.-H. Wen, J. L. Langsam, L. Zhang, W. Shen*, X. Zhou*.

Cell Reports Methods (2022) 2, 100133. https://doi.org/10.1016/j.crmeth.2021.100133

 

2021

An ensemble deep learning framework to refine large deletions in linked-reads.

Y. Hu, S. V. Mangal, L. Zhang, X. Zhou.

IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2021), pp. 288-295, doi: 10.1109/BIBM52615.2021.9669571.  (acceptance rate: 19.3%)

 

The epithelial and stromal immune microenvironment in gastric cancer: a comprehensive analysis reveals prognostic factors with digital cytometry.

W. Shen, G. Wang, G. R. Cooper, Y. Jiang, X. Zhou.

Cancers (Basel) (2021) 13(21):5382. https://doi.org/10.3390/cancers13215382

 

Emergence of prefrontal neuron maturation properties by training recurrent neural networks in cognitive tasks.

Y. H. Liu, J. Zhu, C. Constantinidis, X. Zhou.

iScience (2021) 24(10):103178. https://doi.org/10.1016/j.isci.2021.103178

 

Editorial: predicting high-risk individuals for common diseases using multi-omics and epidemiological data.

D. Chowdhury, X. Zhou, B. Li, Y. Zhang, W. K. Cheung, A. Lyu, L. Zhang.

Front. Genet. (2021) 12:737598.

 

DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data.

J. Chen, C. Cheong, L. Lan, X. Zhou, J. Liu, A. Lyu, W. K. Cheung, L. Zhang.

Briefings in Bioinformatics (2021), 22(6):bbab325. https://doi.org/10.1093/bib/bbab325

 

Text mining of gene-phenotype associations reveals new phenotypic profiles of autism-associated genes.

S. Li, Z. Guo, J. B. Ioffe, Y. Hu, Y. Zhen*, X. Zhou*.       ( equal contribution)

Scientific Reports (2021) 11(1):15269. https://doi.org/10.1038/s41598-021-94742-z

 

Aquila_stLFR: diploid genome assembly based structural variant calling package for stLFR linked-reads.

Y.H. Liu, G. L. Grubbs, L. Zhang, X. Fang, D. L. Dill, A. Sidow, X. Zhou.

Bioinformatics Advances (2021) 1, vbab007. https://doi.org/10.1093/bioadv/vbab007

 

 

2020 and earlier

Aquila enables reference-assisted diploid personal genome assembly and comprehensive variant detection based on linked-reads. 

X. Zhou*, L. Zhang,  Z. Weng, D. L. Dill, A. Sidow*.   (* co-corresponding authors)

Nature Communications (2021) 12:1077.

 

A comprehensive investigation of metagenome assembly by linked-read sequencing.

L. Zhang, X. Fang, H. Liao, Z. Zhang, X. Zhou, L. Han, Y. Chen, Q. Qiu, S. C. Li.

Microbiome (2020) 8(1), 1-11.

 

A diploid assembly-based benchmark for variants in the major histocompatibility complex. 

C. Chin, J. Wagner,…, X. Zhou,…, J. Zook.

Nature Communications (2020) 11:4794.

 

De novo diploid genome assembly for genome-wide structural variant detection. 

L. ZhangX. Zhou, Z. Weng, A. Sidow.       ( equal contribution)

NAR Genomics and Bioinformatics (2020) 2(1)1-10.

 

Plasticity of persistent activity and its constraints.

S. Li, X. Zhou, C. Constantinidis, X.-L. Qi.

Frontiers in Neural Circuits (2020) 14: 15.

 

Assessment of human diploid genome assembly with 10x Linked-Reads data.

L. ZhangX. Zhou, Z. Weng, A. Sidow.     ( equal contribution)

GigaScience (2019) 8:1-11. 

 

Assessment of network module identification across complex diseases. 

S. Choobdar et al.

Nature Methods (2019) 16:843-852.

 

HAPDeNovo: a haplotype-based approach for filtering and phasing de novo mutations in linked read sequencing data. 

X. Zhou, S. Batzoglou, A. Sidow, L. Zhang.

BMC genomics (2018) 19 (1), 467.

 

Anterior-posterior gradient of plasticity in primate prefrontal cortex. 

M. Riley, X.-L. Qi, X. Zhou, C. Constantinidis.

Nature Communications (2018) 9(1):3790.

 

Fixation target representation in prefrontal cortex during the anti-saccade task. 

X. Zhou, C. Constantinidis.

J. Neurophysiology (2017)  117:2152-2162.

 

Neural correlates of working memeory development in adolescent primates. 

X. Zhou, D. Zhu, X.-L. Qi, S. H. Li, S.G. King, E. Salinas, T. R. Stanford, C. Constantinidis.

Nature Communications (2016) 7:13423.

 

Behavioral response inhibition and maturation of goal representation in prefrontal cortex after puberty. 

X. Zhou, D. Zhu, S. G. King, C. J. Lees, A. J. Bennett, E. Salinas, T. R. Stanford, C. Constantinidis.

Proc. Natl. Acad. Sci. USA (2016) 113(12):3353-8.

 

Distinct roles of the prefrontal and posterior parietal cortices in response inhibition. 

X. Zhou, X.-L. Qi, C. Constantinidis.

Cell Reports (201614(12):2765-73.

 

An evolutionary strategy for resilient cyber defense. 

E. W. Fulp, H. D. Gage, D. J. John, M. McNiece, W. H. Turkett, and X. Zhou [alphabetical].

In Proceedings of the IEEE Global Communications Conference (GLOBECOM) (2015).

 

Age-dependent changes in prefrontal intrinsic connectivity. 

X. ZhouD. Zhu, F. Katsuki, X.-L. Qi, C. J. Lees, A. J. Bennett, E. Salinas, T. R. Stanford, C. Constantinidis.

Proc. Natl. Acad. Sci. USA (2014) 111(10):3853-3858.

 

Working memory performance and neural activity in the prefrontal cortex of peri-pubertal monkeys. 

X. Zhou, D. Zhu, X.-L. Qi, C. J. Lees, A. J. Bennett, E. Salinas, T. R. Stanford, C. Constantinidis.

J. Neurophysiology (2013) 110:2648-2660.

 

Neurons with inverted tuning during the delay periods of working memory tasks in the dorsal prefrontal and posterior parietal cortex. 

X. Zhou, F. Katsuki, X.-L. Qi, and C. Constantinidis.

J. Neurophysiology (2012) 108:31-38.

 

Cholinergic modulation of working memory activity in primate prefrontal cortex. 

X. Zhou, X.-L. Qi, K. Douglas, K. Palaninathan, H. S. Kang, J. J. Buccafusco, D. T. Blake, C. Constantinidis.

J. Neurophysiology (2011) 106:2180:8.

 

Comparison of neural activity related to working memory in primate dorsolateral prefrontal and posterior parietal cortex. 

X.-L. Qi, F. Katsuki, T. Meyer, J. B. Rawley, X. Zhou, K. Douglas, C. Constantinidis.

Front. Syst. Neurosci. (2010) 4:12.

 

Book Chapters

X. Zhou, E. Salinas, T. R. Stanford, C. Constantinidis. Dynamic interactions in prefrontal functional connectivity during adolescence. In: Advances in Cognitive Neurodynamics (V) (2016) R. Wang and X. Pan, Editors. Springer. pp. 193-197

 

X.-L. Qi, X. Zhou, C. Constantinidis. Neurophysiological mechanisms of working memory: cortical specialization & plasticity. In: Attention and Performance XXV (2015). Jolicoeur P., Lefebvre C. and Martinez-Trujillo J., Editors. Academic Press. pp. 171-186