STEM: a tool for the analysis of short time series gene expression data

Gene Ontology: tool for the unification of biology

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works

Cluster analysis and display of genome-wide expression patterns

Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 12,617 · see citing works

In silico prediction of protein-protein interactions in human macrophages

Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785 · see citing works

Significance analysis of microarrays applied to the ionizing radiation response

Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568 · see citing works

TM4: a free, open-source system for microarray data management and analysis

Saeed, A. I., Sharov, V., White, J., Li, J., Liang, W., Bhagabati, N., Braisted, J., Klapa, M., Currier, T., Thiagarajan, M., et al. (2003) · BioTechniques · doi:10.2144/03342mt01 · Cited by 4,837 · see citing works

Comprehensive Identification of Cell Cycle–regulated Genes of the YeastSaccharomyces cerevisiaeby Microarray Hybridization

Spellman, P. T., Sherlock, G. J., Zhang, M. Q., Iyer, V. R., Anders, K. R., Eisen, M. B., Brown, P. O., Botstein, D., & Futcher, B. (1998) · Molecular Biology of the Cell · doi:10.1091/mbc.9.12.3273 · Cited by 4,724 · see citing works

Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation

Tamayo, P., Slonim, D. K., Mesirov, J. P., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S., & Golub, T. R. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.6.2907 · Cited by 2,924 · see citing works

Systematic determination of genetic network architecture

Tavazoie, S., Hughes, J. D., Campbell, M. J., Cho, R. J., & Church, G. M. (1999) · Nature Genetics · doi:10.1038/10343 · Cited by 2,509 · see citing works

NCBI GEO: mining millions of expression profiles--database and tools

Barrett, T. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki022 · Cited by 1,201 · see citing works

Gene Expression During the Life Cycle of Drosophila melanogaster

Arbeitman, M. N., Furlong, E. E. M., Imam, F. B., Johnson, E. A., Null, B. H., Baker, B. S., Krasnow, M. A., Scott, M. P., Davis, R. W., & White, K. P. (2002) · Science · doi:10.1126/science.1072152 · Cited by 926 · see citing works

Ontological analysis of gene expression data: current tools, limitations, and open problems

Khatri, P., & Drăghici, S. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti565 · Cited by 850 · see citing works

Clustering short time series gene expression data

Ernst, J., Nau, G. J., & Bar‐Joseph, Z. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1022 · Cited by 517 · see citing works

Cluster analysis of gene expression dynamics

Ramoni, M. F., Sebastiani, P., & Kohane, I. S. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.132656399 · Cited by 466 · see citing works

EXPANDER – an integrative program suite for microarray data analysis

Ron Shamir, A., Maron‐Katz, A., Tanay, A., Linhart, C., Steinfeld, I., Sharan, R., Shiloh, Y., & Elkon, R. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-232 · Cited by 340 · see citing works

Novel Unsupervised Feature Filtering of Biological Data

Varshavsky, R., Gottlieb, A., Michal Linial, B., & Horn, D. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl214 · Cited by 339 · see citing works

Identifying periodically expressed transcripts in microarray time series data

Wichert, S., Fokianos, K., & Strimmer, K. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg364 · Cited by 292 · see citing works

Toolkit design for interactive structured graphics

Bederson, B. B., Grosjean, J., & Meyer, J. (2004) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2004.44 · Cited by 290 · see citing works

High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID)

Zeebèrg, B. R., Qin, H., Narasimhan, S., Sunshine, M., Cao, H., Kane, D. W., Reimers, M., Stephens, R. M., Bryant, D., Burt, S. K., et al. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-168 · Cited by 279 · see citing works

Identifying periodically expressed transcripts in microarray time series data

Wichert, S., Fokianos, K., & Strimmer, K. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn430 · Cited by 271 · see citing works

EDGE: extraction and analysis of differential gene expression

Leek, J. T., Monsen, E., Dabney, A. R., & Storey, J. D. (2005) · Bioinformatics · doi:10.1093/bioinformatics/btk005 · Cited by 251 · see citing works

Using hidden Markov models to analyze gene expression time course data

Schliep, A., Schönhuth, A., & Steinhoff, C. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg1036 · Cited by 247 · see citing works

Cag pathogenicity island-specific responses of gastric epithelial cells to Helicobacter pylori infection

Guillemin, K. J., Salama, N. R., Tompkins, L. S., & Falkow, S. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.182558799 · Cited by 222 · see citing works

ORIOGEN: order restricted inference for ordered gene expression data

Peddada, S. D., Harris, S. F., Zajd, J., & Harvey, E. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti637 · Cited by 54 · see citing works

Global Gene Expression Analysis of Human Bronchial Epithelial Cells Treated with Tobacco Condensates

Jorgensen, E. D., Dozmorov, I. M., Frank, M. B., Centola, M. B., & Albino, A. P. (2004) · Cell Cycle · doi:10.4161/cc.3.9.1078 · Cited by 39 · see citing works

Dynamic querying for pattern identification in microarray and genomic data

Hochheiser, H., Baehrecke, E. H., Mount, S. M., & Shneiderman, B. (2003) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2003.1221346 · Cited by 31 · see citing works

Gene Time Eχpression Warper: a tool for alignment, template matching and visualization of gene expression time series

Criel, J., & Tsiporkova, E. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti787 · Cited by 30 · see citing works

The Graphical Query Language: a tool for analysis of gene expression time-courses

Costa, I. G., Schönhuth, A., & Schliep, A. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti311 · Cited by 26 · see citing works

Mapping and analysis of chromatin state dynamics in nine human cell types

Ernst, J., Kheradpour, P., Mikkelsen, T. S., Shoresh, N., Ward, L. D., Epstein, C. B., Zhang, X., Wang, L., Issner, R., Coyne, M., et al. (2011) · Nature · doi:10.1038/nature09906 · Cited by 2,898 · see citing works

Discovery and characterization of chromatin states for systematic annotation of the human genome

Ernst, J., & Kellis, M. (2010) · Nature Biotechnology · doi:10.1038/nbt.1662 · Cited by 1,033 · see citing works

Studying and modelling dynamic biological processes using time-series gene expression data

Bar‐Joseph, Z., Gitter, A., & Simon, I. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3244 · Cited by 478 · see citing works

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430 · see citing works

Macrophages directly contribute collagen to scar formation during zebrafish heart regeneration and mouse heart repair

Simões, F. C., Cahill, T. J., Kenyon, A., Gavriouchkina, D., Vieira, J. M., Sun, X., Pezzolla, D., Ravaud, C., Masmanian, E., Weinberger, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14263-2 · Cited by 390 · see citing works

Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states

Furman, D., Chang, J., Lartigue, L., Bolen, C. R., Haddad, F., Gaudilliere, B., Ganio, E. A., Fragiadakis, G. K., Spitzer, M. H., Douchet, I., et al. (2017) · Nature Medicine · doi:10.1038/nm.4267 · Cited by 389 · see citing works

Comprehensive transcriptional profiling of NaCl-stressed Arabidopsis roots reveals novel classes of responsive genes

Jiang, Y., & Deyholos, M. K. (2006) · BMC Plant Biology · doi:10.1186/1471-2229-6-25 · Cited by 384 · see citing works

Multiple Rice MicroRNAs Are Involved in Immunity against the Blast Fungus Magnaporthe oryzae

Li, Y., Lu, Y., Shi, Y., Wu, L., Xu, Y., Huang, F., Guo, X., Zhang, Y., Fan, J., Zhao, J., et al. (2013) · PLANT PHYSIOLOGY · doi:10.1104/pp.113.230052 · Cited by 381 · see citing works

Temporal and spatial interplay of microbiota and intestinal mucosa drive establishment of immune homeostasis in conventionalized mice

Aidy, S. E., van Baarlen, P., Derrien, M., Lindenbergh-Kortleve, D. J., Hooiveld, G. J. E. J., Levenez, F., Dore, J. M., Dekker, J. P., Samsom, J. N., Nieuwenhuis, E. E. S., et al. (2012) · Mucosal Immunology · doi:10.1038/mi.2012.32 · Cited by 247 · see citing works

A long noncoding RNA associated with susceptibility to celiac disease

Castellanos–Rubio, A., Fernández‐Jiménez, N., Kratchmarov, R., Luo, X., Bhagat, G., Green, P. H. R., Schneider, R. J., Kiledjian, M., Bilbao, J. R., & Ghosh, S. (2016) · Science · doi:10.1126/science.aad0467 · Cited by 245 · see citing works

Integrated multi-omics framework of the plant response to jasmonic acid

Zander, M., Lewsey, M. G., Clark, N. M., Yin, L., Bartlett, A., Guzmán, J. P. S., Hann, E. C., Langford, A. E., Jow, B., Wise, A. S., et al. (2020) · Nature Plants · doi:10.1038/s41477-020-0605-7 · Cited by 245 · see citing works

Longitudinal shift in diabetic wound microbiota correlates with prolonged skin defense response

Grice, E. A., Snitkin, E. S., Yockey, L. J., Bermudez, D. M., Program, N. C. S., Liechty, K. W., Segre, J. A., Mullikin, J., Blakesley, R., Young, A., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1004204107 · Cited by 227 · see citing works

Evidence of abundant stop codon readthrough in Drosophila and other metazoaPreprint

Jungreis, I., Lin, M. F., Spokony, R. F., Chan, C. S., Negre, N., Victorsen, A., White, K. P., & Kellis, M. (2011) · Genome Research · doi:10.1101/gr.119974.110 · Cited by 223 · see citing works

Reconstructing dynamic regulatory maps

Ernst, J., Vainas, O., Harbison, C. T., Simon, I., & Bar‐Joseph, Z. (2007) · Molecular Systems Biology · doi:10.1038/msb4100115 · Cited by 216 · see citing works

Comparative Transcriptome Analysis between Low- and High-Cadmium-Accumulating Genotypes of Pakchoi (Brassica chinensis L.) in Response to Cadmium Stress

Zhou, Q., Guo, J.-J., He, C., Shen, C., Huang, Y., Chen, J., Guo, J., Yuan, J., & Yang, Z.-Y. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.5b06326 · Cited by 208 · see citing works

Pericyte TIMP3 and ADAMTS1 Modulate Vascular Stability after Kidney Injury

Schrimpf, C., Xin, C., Campanholle, G., Gill, S. E., Stallcup, W. B., Lin, S., Davis, G. E., Gharib, S. A., Humphreys, B. D., & Duffield, J. S. (2012) · Journal of the American Society of Nephrology · doi:10.1681/asn.2011080851 · Cited by 204 · see citing works

Single cell RNA analysis identifies cellular heterogeneity and adaptive responses of the lung at birth

Guo, M., Du, Y., Gokey, J. J., Ray, S., Bell, S. M., Adam, M., Sudha, P., Perl, A. T., Deshmukh, H. S., Potter, S. S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07770-1 · Cited by 201 · see citing works

Defective HNF4alpha-dependent gene expression as a driver of hepatocellular failure in alcoholic hepatitis

Argemí, J., Latasa, M. U., Atkinson, S. R., Blokhin, I. O., Massey, V. L., Gue, J. P., Cabezas, J., Lozano, J. J., Booven, D. V., Bell, A. W., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11004-3 · Cited by 195 · see citing works

Macrophage phenotypic switch orchestrates the inflammation and repair/regeneration following acute pancreatitis injury

Wu, J., Zhang, L., Shi, J., He, R., Yang, W., Habtezion, A., Niu, N., Lu, P., & Xue, J. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.102920 · Cited by 189 · see citing works

Transcriptomic profiling of microglia and astrocytes throughout aging

Pan, J., Ma, N., Yu, B., Zhang, W., & Wan, J. (2020) · Journal of Neuroinflammation · doi:10.1186/s12974-020-01774-9 · Cited by 188 · see citing works

Comprehensive analysis of differentially expressed genes and transcriptional regulation induced by salt stress in two contrasting cotton genotypes

Peng, Z., He, S., Gong, W., Sun, J., Pan, Z., Xu, F., Lu, Y., & Du, X. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-760 · Cited by 187 · see citing works

Bacterial persistence is an active σS stress response to metabolic flux limitation

Radzikowski, J. L., Vedelaar, S. R., Siegel, D. J., Ortega, A. D., Schmidt, A., & Heinemann, M. (2016) · Molecular Systems Biology · doi:10.15252/msb.20166998 · Cited by 186 · see citing works

RNA-Seq analysis and annotation of a draft blueberry genome assembly identifies candidate genes involved in fruit ripening, biosynthesis of bioactive compounds, and stage-specific alternative splicing

Gupta, V., Estrada, A. D., Blakley, I., Reid, R. W., Patel, K. S., Meyer, M. D., Andersen, S. U., Brown, A., Lila, M. A., & Loraine, A. E. (2015) · GigaScience · doi:10.1186/s13742-015-0046-9 · Cited by 182 · see citing works

The Arabidopsis Transcriptome Responds Specifically and Dynamically to High Light Stress

Huang, J., Zhao, X., & Chory, J. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.051 · Cited by 178 · see citing works

Mechanical control of innate immune responses against viral infection revealed in a human lung alveolus chip

Bai, H., Si, L., Jiang, A., Belgur, C., Zhai, Y., Plebani, R., Oh, C. Y., Rodas, M., Patil, A., Nurani, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29562-4 · Cited by 171 · see citing works

Single-cell genomics and regulatory networks for 388 human brains

Emani, P. S., Liu, J. J., Clarke, D., Jensen, M., Warrell, J., Gupta, C., Meng, R., Lee, C.-Y., Xu, S., Dursun, C., et al. (2024) · Science · doi:10.1126/science.adi5199 · Cited by 168 · see citing works

High-throughput data integration of RNA–miRNA–circRNA reveals novel insights into mechanisms of benzo[a]pyrene-induced carcinogenicity

Caiment, F., Gaj, S., Claessen, S. M. H., & Kleinjans, J. C. S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv115 · Cited by 163 · see citing works

Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells

Bar‐Joseph, Z., Siegfried, Z., Brandeis, M., Brors, B., Lu, Y. C., Eils, R., Dynlacht, B. D., & Simon, I. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0704723105 · Cited by 162 · see citing works

Transcriptomic atlas of mushroom development reveals conserved genes behind complex multicellularity in fungi

Krizsán, K., Almási, É. D. H., Merényi, Z., Sahu, N., Virágh, M., Kószó, T., Mondo, S. J., Kiss, B., Bálint, B., Kües, U., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1817822116 · Cited by 156 · see citing works

Apomictic and Sexual Ovules ofBoecheraDisplay Heterochronic Global Gene Expression Patterns

Sharbel, T. F., Voigt, M.-L., del Corral, J. M. M., Galla, G., Kumlehn, J., Klukas, C., Schreiber, F., Vogel, H., & Rotter, B. (2010) · The Plant Cell · doi:10.1105/tpc.109.072223 · Cited by 153 · see citing works

Performance in Omics Analyses of Blood Samples in Long-Term Storage: Opportunities for the Exploitation of Existing Biobanks in Environmental Health Research

Hebels, D. G. A. J., Georgiadis, P., Keun, H. C., Athersuch, T. J., Vineis, P., Vermeulen, R. C. H., Portengen, L., Bergdahl, I. A., Hallmans, G., Palli, D., et al. (2013) · Environmental Health Perspectives · doi:10.1289/ehp.1205657 · Cited by 149 · see citing works

Developmental trajectories of gene expression reveal candidates for diapause termination: a key life-history transition in the apple maggot flyRhagoletis pomonella

Ragland, G. J., Egan, S. P., Feder, J. L., Berlocher, S. H., & Hahn, D. A. (2011) · Journal of Experimental Biology · doi:10.1242/jeb.061085 · Cited by 149 · see citing works

mRNA poly(A)-tail changes specified by deadenylation broadly reshape translation in Drosophila oocytes and early embryos

Eichhorn, S. W., Subtelny, A. O., Kronja, I., Kwasnieski, J. C., Orr‐Weaver, T. L., & Bartel, D. P. (2016) · eLife · doi:10.7554/elife.16955 · Cited by 149 · see citing works

Immunological Outcome in Haploidentical-HSC Transplanted Patients Treated with IL-10-Anergized Donor T Cells

Bacchetta, R., Lucarelli, B., Sartirana, C., Gregori, S., Stanghellini, M. T. L., Miqueu, P., Tomiuk, S., Hernández-Fuentes, M. P., Gianolini, M. E., Greco, R., et al. (2014) · Frontiers in Immunology · doi:10.3389/fimmu.2014.00016 · Cited by 147 · see citing works

Transcriptional profiling of VEGF-A and VEGF-C target genes in lymphatic endothelium reveals endothelial-specific molecule-1 as a novel mediator of lymphangiogenesis

Shin, J. W., Huggenberger, R., & Detmar, M. J. (2008) · Blood · doi:10.1182/blood-2008-05-156331 · Cited by 147 · see citing works

On the selection of appropriate distances for gene expression data clustering

Jaskowiak, P. A., Campello, R. J. G. B., & Costa, I. G. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s2-s2 · Cited by 144 · see citing works

Dissecting Interferon-Induced Transcriptional Programs in Human Peripheral Blood Cells

Waddell, S. J., Popper, S. J., Rubins, K., Griffiths, M. J., Brown, P. O., Levin, M., & Relman, D. A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009753 · Cited by 142 · see citing works

Two-Step Maternal-to-Zygotic Transition with Two-Phase Parental Genome Contributions

Zhao, P., Zhou, X., Shen, K., Liu, Z., Cheng, T., Liu, D., Cheng, Y., Peng, X., & Sun, M. (2019) · Developmental Cell · doi:10.1016/j.devcel.2019.04.016 · Cited by 140 · see citing works

PD-1 signaling affects cristae morphology and leads to mitochondrial dysfunction in human CD8+ T lymphocytes

Ogando, J., Sáez, M. E., Santos, J. V., Nuevo‐Tapioles, C., Gut, M., Esteve‐Codina, A., Heath, S. C., González‐Pérez, A., Cuezva, J. M., Lacalle, R. A., et al. (2019) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-019-0628-7 · Cited by 136 · see citing works

The gut microbiota elicits a profound metabolic reorientation in the mouse jejunal mucosa during conventionalisation

Aidy, S. E., Merrifield, C. A., Derrien, M., van Baarlen, P., Hooiveld, G. J. E. J., Levenez, F., Dore, J. M., Dekker, J. P., Holmes, E. C., Claus, S. P., et al. (2012) · Gut · doi:10.1136/gutjnl-2011-301955 · Cited by 134 · see citing works

Stress-Driven Transposable Element De-repression Dynamics and Virulence Evolution in a Fungal Pathogen

Fouché, S., Badet, T., Oggenfuss, U., Plissonneau, C., Francisco, C. S., & Croll, D. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz216 · Cited by 134 · see citing works

Transcriptional profiling of Medicago truncatula under salt stress identified a novel CBF transcription factor MtCBF4 that plays an important role in abiotic stress responses

Li, D., Zhang, Y., Hu, X., Shen, X., Ma, L., Su, Z., Wang, T., & Dong, J. (2011) · BMC Plant Biology · doi:10.1186/1471-2229-11-109 · Cited by 132 · see citing works

The gut microbiota and mucosal homeostasis: Colonized at birth or at adulthood, does it matter?

El Aidy, S., Hooiveld, G., Tremaroli, V., Bäckhed, F., & Kleerebezem, M. (2013) · Gut Microbes · doi:10.4161/gmic.23362 · Cited by 131 · see citing works

Analysis of Time-Series Gene Expression Data: Methods, Challenges, and Opportunities

Androulakis, I. P., Yang, E., & Almon, R. R. (2007) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.9.060906.151904 · Cited by 130 · see citing works

Essential Gene Profiles for Human Pluripotent Stem Cells Identify Uncharacterized Genes and Substrate Dependencies

Mair, B., Tomic, J., Masud, S. N., Tonge, P. J., Weiss, A., Ušaj, M., Tong, A. H. Y., Kwan, J. J., Brown, K. R., Titus, E., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.041 · Cited by 123 · see citing works

Heart Regeneration in the Mexican Cavefish

Stockdale, W. T., Lemieux, M. E., Killen, A. C., Zhao, J., Hu, Z., Riepsaame, J., Hamilton, N., Kudoh, T., Riley, P. R., van Aerle, R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.10.072 · Cited by 119 · see citing works

Global transcriptomic profiling of the bone marrow stromal microenvironment during postnatal development, aging, and inflammation

Helbling, P. M., Piñeiro-Yáñez, E., Gerosa, R., Boettcher, S., Al-Shahrour, F., Manz, M. G., & Nombela-Arrieta, C. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.004 · Cited by 116 · see citing works

Gene expression patterns through oral squamous cell carcinoma development: PD-L1 expression in primary tumor and circulating tumor cells

Oliveira–Costa, J. P., de Carvalho, A. F., da Silveira, G. G., Amaya, P., Wu, Y., Park, K. J., Pinilla, M. G., Lustberg, M. B., Buim, M. E. C., Ferreira, E. N., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.3939 · Cited by 114 · see citing works

PAN-cancer analysis of S-phase enriched lncRNAs identifies oncogenic drivers and biomarkers

Ali, M. M., Akhade, V. S., Kosalai, S. T., Subhash, S., Statello, L., Meryet‐Figuière, M., Abrahamsson, J., Mondal, T., & Kanduri, C. (2018) · Nature Communications · doi:10.1038/s41467-018-03265-1 · Cited by 114 · see citing works

Seed Transcriptomics Analysis in Camellia oleifera Uncovers Genes Associated with Oil Content and Fatty Acid Composition

Lin, P., Wang, K., Zhou, C., Xie, Y., Yao, X., & Yin, H. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19010118 · Cited by 113 · see citing works

The Asian corn borer Ostrinia furnacalis feeding increases the direct and indirect defence of mid‐whorl stage commercial maize in the field

Guo, J., Qi, J., He, K., Wu, J., Bai, S., Zhang, T., Zhao, J., & Wang, Z. (2018) · Plant Biotechnology Journal · doi:10.1111/pbi.12949 · Cited by 113 · see citing works

Gut bacteria–host metabolic interplay during conventionalisation of the mouse germfree colon

Aidy, S. E., Derrien, M., Merrifield, C. A., Levenez, F., Dore, J. M., Boekschoten, M. V., Dekker, J. P., Holmes, E. C., Zoetendal, E. G., van Baarlen, P., et al. (2012) · The ISME Journal · doi:10.1038/ismej.2012.142 · Cited by 112 · see citing works

PbbHLH4 regulates floral monoterpene biosynthesis in Phalaenopsis orchids

Chuang, Y.-C., Hung, Y.-C., Tsai, W., Chen, W., & Chen, H. (2018) · Journal of Experimental Botany · doi:10.1093/jxb/ery246 · Cited by 112 · see citing works

Assessment of Pharmacologic Area Under the Curve When Baselines are Variable

Scheff, J. D., Almon, R. R., DuBois, D. C., Jusko, W. J., & Androulakis, I. P. (2011) · Pharmaceutical Research · doi:10.1007/s11095-010-0363-8 · Cited by 108 · see citing works

Reversal of senescence by N resupply to N-starved Arabidopsis thaliana: transcriptomic and metabolomic consequences

Balazadeh, S., Schildhauer, J., Araújo, W. L., Munné‐Bosch, S., Fernie, A. R., Proost, S., Humbeck, K., & Mueller‐Roeber, B. (2014) · Journal of Experimental Botany · doi:10.1093/jxb/eru119 · Cited by 104 · see citing works

Kinetic CRAC uncovers a role for Nab3 in determining gene expression profiles during stress

van Nues, R. W., Schweikert, G., de Leau, E., Selega, A., Langford, A., Franklin, R., Iosub, I. A., Wadsworth, P., Sanguinetti, G., & Granneman, S. (2017) · Nature Communications · doi:10.1038/s41467-017-00025-5 · Cited by 104 · see citing works

Transcriptionally dynamic progenitor populations organised around a stable niche drive axial patterning

Wymeersch, F. J., Skylaki, S., Huang, Y., Watson, J. A., Economou, C., Marek-Johnson, C., Tomlinson, S. R., & Wilson, V. A. (2018) · Development · doi:10.1242/dev.168161 · Cited by 103 · see citing works

A Semi-Supervised Method for Predicting Transcription Factor–Gene Interactions in Escherichia coli

Ernst, J., Beg, Q. K., Kay, K. A., Balázsi, G., Oltvai, Z. N., & Bar‐Joseph, Z. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000044 · Cited by 103 · see citing works

Comparative transcriptome analysis of two contrasting watermelon genotypes during fruit development and ripening

Zhu, Q., Gao, P., Liu, S., Zhu, Z., Amanullah, S., Davis, A. R., & Luan, F. (2017) · BMC Genomics · doi:10.1186/s12864-016-3442-3 · Cited by 101 · see citing works

High‐spatiotemporal‐resolution transcriptomes provide insights into fruit development and ripening in Citrus sinensis

Feng, G., Wu, J., Xu, Y., Lu, L., & Yi, H. (2021) · Plant Biotechnology Journal · doi:10.1111/pbi.13549 · Cited by 101 · see citing works

Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine

Zafar, I., Anwar, S., kanwal, F., Yousaf, W., Nisa, F. U., Kausar, T., Ain, Q. U., Unar, A., Kamal, M. A., Rashid, S., et al. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105263 · Cited by 101 · see citing works

Discretization of Time Series Data

Dimitrova, E., Vera-Licona, M. P., McGee, J., & Laubenbacher, R. (2010) · Journal of Computational Biology · doi:10.1089/cmb.2008.0023 · Cited by 100 · see citing works

Regulation of Neutrophil Degranulation and Cytokine Secretion: A Novel Model Approach Based on Linear Fitting

Naegelen, I., Beaume, N., Plançon, S., Schenten, V., Tschirhart, E. J., & Bréchard, S. (2015) · Journal of Immunology Research · doi:10.1155/2015/817038 · Cited by 100 · see citing works

Transcriptome and physiology analysis identify key metabolic changes in the liver of the large yellow croaker (Larimichthys crocea) in response to acute hypoxia

Ding, J., Liu, C., Luo, S., Zhang, Y., Gao, X., Wu, X., Shen, W., & Zhu, J. (2019) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2019.109957 · Cited by 100 · see citing works

Quantitative proteomics and transcriptomics of potato in response to Phytophthora infestans in compatible and incompatible interactions

Ali, A., Alexandersson, E., Sandin, M., Resjö, S., Lenman, M., Hedley, P. E., Levander, F., & Andréasson, E. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-497 · Cited by 100 · see citing works

The genome and transcriptome of the enteric parasite Entamoeba invadens, a model for encystation

Ehrenkaufer, G. M., Weedall, G. D., Williams, D., Lorenzi, H., Caler, E. V., Hall, N., & Singh, U. (2013) · Genome biology · doi:10.1186/gb-2013-14-7-r77 · Cited by 99 · see citing works

Combined Single-Cell Profiling of lncRNAs and Functional Screening Reveals that H19 Is Pivotal for Embryonic Hematopoietic Stem Cell Development

Zhou, J. R., Xu, J., Zhang, L., Liu, S., Ma, Y., Wen, X., Hao, J., Li, Z., Ni, Y., Li, X., et al. (2019) · Cell stem cell · doi:10.1016/j.stem.2018.11.023 · Cited by 98 · see citing works

Congenital diaphragmatic hernia candidate genes derived from embryonic transcriptomes

Russell, M. K., Longoni, M., Wells, J. M., Maalouf, F. I., Tracy, A. A., Loscertales, M., Ackerman, K. G., Pober, B. R., Lage, K., Bult, C. J., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121621109 · Cited by 97 · see citing works

Metabolite and transcriptome analysis of Campylobacter jejuni in vitro growth reveals a stationary-phase physiological switch

Wright, J. A., Grant, A. J., Hurd, D., Harrison, M., Guccione, E. J., Kelly, D. J., & Maskell, D. J. (2008) · Microbiology · doi:10.1099/mic.0.021790-0 · Cited by 97 · see citing works

Multi‐omics analysis to visualize the dynamic roles of defense genes in the response of tea plants to gray blight

Wang, S., Liu, L., Mi, X., Zhao, S., An, Y., Xia, X., Guo, R., & Wei, C. (2021) · The Plant Journal · doi:10.1111/tpj.15203 · Cited by 96 · see citing works

Genome assemblies of 11 bamboo species highlight diversification induced by dynamic subgenome dominance

Ma, P.-F., Liu, Y.-L., Guo, C., Jin, G., Guo, Z.-H., Mao, L., Yang, Y.-Z., Niu, L.-Z., Wang, Y.-J., Clark, L. G., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01683-0 · Cited by 90 · see citing works

Collier/OLF/EBF-Dependent Transcriptional Dynamics Control Pharyngeal Muscle Specification from Primed Cardiopharyngeal Progenitors

Razy‐Krajka, F., Lam, K., Wang, W., Stolfi, A., Joly, M., Bonneau, R., & Christiaen, L. (2014) · Developmental Cell · doi:10.1016/j.devcel.2014.04.001 · Cited by 95 · see citing works

Gene expression during Drosophila melanogaster egg development before and after reproductive diapause

Baker, D. A., & Russell, S. R. H. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-242 · Cited by 94 · see citing works

Dual functionality of the amyloid protein TasA in Bacillus physiology and fitness on the phylloplane

Cámara-Almirón, J., Navarro, Y., Díaz‐Martínez, L., Magno-Pérez-Bryan, M. C., Molina‐Santiago, C., Pearson, J. R., de Vicente, A., Pérez‐García, A., & Romero, D. (2020) · Nature Communications · doi:10.1038/s41467-020-15758-z · Cited by 94 · see citing works

Stage-Specific Regulation of Reprogramming to Induced Pluripotent Stem Cells by Wnt Signaling and T Cell Factor Proteins

Ho, R., Papp, B., Hoffman, J. A., Merrill, B. J., & Plath, K. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.05.015 · Cited by 92 · see citing works

Spatio-Temporal Transcriptional Dynamics of Maize Long Non-Coding RNAs Responsive to Drought Stress

Pang, J., Zhang, X., Ma, X., & Zhao, J. Y. (2019) · Genes · doi:10.3390/genes10020138 · Cited by 92 · see citing works

Androgen receptor profiling predicts prostate cancer outcome

Stelloo, S., Nevedomskaya, E., van der Poel, H. G., de Jong, J., van Leenders, G. J. L. H., Jenster, G. W., Wessels, L. F. A., Bergman, A. M., & Zwart, W. T. (2015) · EMBO Molecular Medicine · doi:10.15252/emmm.201505424 · Cited by 90 · see citing works

Combined transcriptome and metabolome analyses to understand the dynamic responses of rice plants to attack by the rice stem borer Chilo suppressalis (Lepidoptera: Crambidae)

Liu, Q., Wang, X., Tzin, V., Romeis, J., Peng, Y., & Li, Y. (2016) · BMC Plant Biology · doi:10.1186/s12870-016-0946-6 · Cited by 90 · see citing works

Sex-specific mouse liver gene expression: genome-wide analysis of developmental changes from pre-pubertal period to young adulthood

Conforto, T. L., & Waxman, D. J. (2012) · Biology of Sex Differences · doi:10.1186/2042-6410-3-9 · Cited by 88 · see citing works

Involvement of GABA Transporters in Atropine-Treated Myopic Retina As Revealed by iTRAQ Quantitative Proteomics

Barathi, V. A., Chaurasia, S. S., Poidinger, M., Koh, S. K., Tian, D., Ho, C. E. H., Iuvone, P. M., Beuerman, R. W., & Zhou, L. (2014) · Journal of Proteome Research · doi:10.1021/pr500558y · Cited by 87 · see citing works

Transcriptional analysis and histochemistry reveal that hypersensitive cell death and H2O2 have crucial roles in the resistance of tea plant (Camellia sinensis (L.) O. Kuntze) to anthracnose

Wang, Y., Hao, X., Lu, Q., Wang, L., Qian, W., Li, N., Ding, C., Wang, X., & Yang, Y. (2018) · Horticulture Research · doi:10.1038/s41438-018-0025-2 · Cited by 86 · see citing works

Dynamic EBF1 occupancy directs sequential epigenetic and transcriptional events in B-cell programmingPreprint

Li, R., Cauchy, P., Ramamoorthy, S., Boller, S., Chavez, L., & Grosschedl, R. (2018) · Genes & Development · doi:10.1101/gad.309583.117 · Cited by 86 · see citing works

Exosomal microRNAs in giant panda (Ailuropoda melanoleuca) breast milk: potential maternal regulators for the development of newborn cubs

Ma, J., Wang, C., Long, K., Zhang, H., Zhang, J., Jin, L. G., Tang, Q., Jiang, A., Wang, X., Tian, S., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-03707-8 · Cited by 85 · see citing works

Transcriptome analysis of atemoya pericarp elucidates the role of polysaccharide metabolism in fruit ripening and cracking after harvest

Chen, J., Duan, Y., Hu, Y., Li, W., Sun, D., Hu, H., & Xie, J. (2019) · BMC Plant Biology · doi:10.1186/s12870-019-1756-4 · Cited by 84 · see citing works

Novel insights into host-pathogen interactions of large yellow croakers (Larimichthys crocea) and pathogenic bacterium Pseudomonas plecoglossicida using time-resolved dual RNA-seq of infected spleens

Tang, Y., Xin, G., Zhao, L., Huang, L., Qin, Y., Su, Y., Zheng, W., Wu, B., Lin, N., & Yan, Q. (2020) · 动物学研究 · doi:10.24272/j.issn.2095-8137.2020.035 · Cited by 84 · see citing works

A probabilistic generative model for GO enrichment analysis

Lu, Y. C., Rosenfeld, R., Simon, I., Nau, G. J., & Bar‐Joseph, Z. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn434 · Cited by 84 · see citing works

Transcriptome Analysis Reveals New Insights into the Bacterial Wilt Resistance Mechanism Mediated by Silicon in Tomato

Jiang, N.-H., Fan, X., Lin, W., Wang, G., & Cai, K. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20030761 · Cited by 83 · see citing works

Transcription Factor Arabidopsis Activating Factor1 Integrates Carbon Starvation Responses with Trehalose Metabolism

Garapati, P., Feil, R., Lunn, J. E., Dijck, P. V., Balazadeh, S., & Mueller‐Roeber, B. (2015) · PLANT PHYSIOLOGY · doi:10.1104/pp.15.00917 · Cited by 83 · see citing works

Potassium availability triggers Mycobacterium tuberculosis transition to, and resuscitation from, non-culturable (dormant) states

Salina, E. G., Waddell, S. J., Hoffmann, N. A., Rosenkrands, I., Butcher, P. D., & Kaprelyants, A. S. (2014) · Open Biology · doi:10.1098/rsob.140106 · Cited by 82 · see citing works

Temporal, spatial, and phenotypical changes of PDGFRα expressing fibroblasts during late lung development

Endale, M., Ahlfeld, S. K., Bao, E. L., Chen, X., Green, J., Bess, Z., Weirauch, M. T., Xu, Y. J., & Perl, A. T. (2017) · Developmental Biology · doi:10.1016/j.ydbio.2017.03.020 · Cited by 82 · see citing works

COL3A1 and SNAP91: novel glioblastoma markers with diagnostic and prognostic value

Gao, Y., Mao, X., Zhu, T., Mao, C., Liu, Z., Wang, Z., Li, L., Li, X., Yin, J., Zhang, W., et al. (2016) · Oncotarget · doi:10.18632/oncotarget.12038 · Cited by 80 · see citing works

An expression database for roots of the model legume Medicago truncatula under salt stress

Li, D., Su, Z., Dong, J., & Wang, T. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-517 · Cited by 80 · see citing works

Identification of radiation-induced microRNA transcriptome by next-generation massively parallel sequencing

Chaudhry, M. A., Omaruddin, R. A., Brumbaugh, C. D., Tariq, M., & Pourmand, N. (2013) · Journal of Radiation Research · doi:10.1093/jrr/rrt014 · Cited by 80 · see citing works

Acute polyethylene microplastic (PE-MPs) exposure activates the intestinal mucosal immune network pathway in adult zebrafish (Danio rerio)

Yuan, Y., Sepúlveda, M., Bi, B., Huang, Y., Kong, L., Yan, H., & Gao, Y. (2022) · Chemosphere · doi:10.1016/j.chemosphere.2022.137048 · Cited by 79 · see citing works

A survey of transcriptome complexity in Sus scrofa using single-molecule long-read sequencing

Li, Y., Fang, C., Fu, Y., Hu, A., Li, C., Zou, C., Li, X., Zhao, S., Zhang, C., & Li, C. (2018) · DNA Research · doi:10.1093/dnares/dsy014 · Cited by 78 · see citing works

Alveolar Macrophage Transcriptional Programs Are Associated with Outcomes in Acute Respiratory Distress Syndrome

Morrell, E. D., Bhatraju, P. K., Mikacenic, C. R., Radella, F., Manicone, A. M., Stapleton, R. D., Wurfel, M. M., & Gharib, S. A. (2019) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201807-1381oc · Cited by 78 · see citing works

Cold adaptation shapes the robustness of metabolic networks inDrosophila melanogaster

Williams, C. M., Watanabe, M., Guarracino, M. R., Ferraro, M. B., Edison, A. S., Morgan, T. J., Boroujerdi, A. F. B., & Hahn, D. A. (2014) · Evolution · doi:10.1111/evo.12541 · Cited by 77 · see citing works

Characterization of differential transcript abundance through time during Nematostella vectensis development

Helm, R. R., Siebert, S., Tulin, S., Smith, J., & Dunn, C. W. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-266 · Cited by 77 · see citing works

Bmal1 is required for beta cell compensatory expansion, survival and metabolic adaptation to diet-induced obesity in mice

Rakshit, K., Hsu, T. W., & Matveyenko, A. V. (2016) · Diabetologia · doi:10.1007/s00125-015-3859-2 · Cited by 77 · see citing works

Transcriptional Programs Controlling Perinatal Lung Maturation

Xu, Y. J., Wang, Y., Besnard, V., Ikegami, M., Wert, S. E., Heffner, C. S., Murray, S. A., Donahue, L. R., & Whitsett, J. A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037046 · Cited by 76 · see citing works

Extracting biologically significant patterns from short time series gene expression data

Tchagang, A., Bui, K. V., McGinnis, T., & Benos, P. V. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-255 · Cited by 23 · see citing works

A Platform for Processing Expression of Short Time Series (PESTS)

Sinha, A., & Markatou, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-13 · Cited by 21 · see citing works

Short time-series microarray analysis: Methods and challenges

Wang, X., Wu, M., Li, Z., & Chan, C. (2008) · BMC Systems Biology · doi:10.1186/1752-0509-2-58 · Cited by 51 · see citing works

Short time series microarray data analysis and biological annotation

Sokmen, Z., Atalay, V., & Çetin-Atalay, R. (2008) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2008.4632669

der Technischen Universitat Dortmund

Mathematik, D. F. F., Springer, T., Prof, Z., & Ickstadt, K. (2016)