Bioinformatics and Computational Biology Solutions Using R and Bioconductor

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

Greedy function approximation: A gradient boosting machine

Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970

Bayesian Methods for Neural Networks and Related Models

Titterington, D. M. (2004) · Statistical Science · doi:10.1214/088342304000000099 · Cited by 162 · cited 1×

limma powers differential expression analyses for RNA-sequencing and microarray studies

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458

WGCNA: an R package for weighted correlation network analysis

Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252

Predicting effective microRNA target sites in mammalian mRNAs

Agarwal, V., Bell, G. W., Nam, J.-W., & Bartel, D. P. (2015) · eLife · doi:10.7554/elife.05005 · Cited by 7,318

Gene ontology analysis for RNA-seq: accounting for selection bias

Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-2-r14 · Cited by 7,107

voom: precision weights unlock linear model analysis tools for RNA-seq read counts

Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155

Depletion of Carcinoma-Associated Fibroblasts and Fibrosis Induces Immunosuppression and Accelerates Pancreas Cancer with Reduced Survival

Özdemir, B. C., Pentcheva-Hoang, T., Carstens, J. L., Zheng, X., Wu, C.-C., Simpson, T. R., Laklai, H., Sugimoto, H., Kahlert, C., Novitskiy, S. V., et al. (2014) · Cancer Cell · doi:10.1016/j.ccr.2014.04.005 · Cited by 2,681

TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis

Tauriello, D. V. F., Palomo-Ponce, S., Stork, D., Berenguer-Llergo, A., Badia-Ramentol, J., Iglesias, M., Sevillano, M., Ibiza, S., Cañellas, A., Hernando-Momblona, X., et al. (2018) · Nature · doi:10.1038/nature25492 · Cited by 1,851

Enhancing CD8 T-cell memory by modulating fatty acid metabolism

Pearce, E. L., Walsh, M. C., Cejas, P. J., Harms, G. M., Shen, H., Wang, L.-S., Jones, R. G., & Choi, Y. (2009) · Nature · doi:10.1038/nature08097 · Cited by 1,559

A data-driven approach to preprocessing Illumina 450K methylation array data

Pidsley, R., Y Wong, C. C., Volta, M., Lunnon, K., Mill, J., & Schalkwyk, L. C. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-293 · Cited by 1,143

Apoptotic Caspases Suppress mtDNA-Induced STING-Mediated Type I IFN Production

White, M. J., McArthur, K., Metcalf, D., Lane, R. M., Cambier, J. C., Herold, M. J., van Delft, M. F., Bedoui, S., Lessene, G., Ritchie, M. E., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.036 · Cited by 983

A comparison of background correction methods for two-colour microarrays

Ritchie, M. E., Silver, J., Oshlack, A., Holmes, M., Diyagama, D., Holloway, A., & Smyth, G. K. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm412 · Cited by 907

Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants.

Shabat, S. K. B., Sasson, G., Doron-Faigenboim, A., Durman, T., Yaacoby, S., Berg Miller, M. E., White, B. A., Shterzer, N., & Mizrahi, I. (2016) · The ISME Journal · doi:10.1038/ismej.2016.62 · Cited by 823

SWAN: Subset-quantile Within Array Normalization for Illumina Infinium HumanMethylation450 BeadChips

Maksimovic, J., Gordon, L., & Oshlack, A. (2012) · Genome Biology · doi:10.1186/gb-2012-13-6-r44 · Cited by 826

Comprehensive comparative analysis of strand-specific RNA sequencing methods

Levin, J. Z., Yassour, M., Adiconis, X., Nusbaum, C., Thompson, D. A., Friedman, N., Gnirke, A., & Regev, A. (2010) · Nature Methods · doi:10.1038/nmeth.1491 · Cited by 753

Intestinal label-retaining cells are secretory precursors expressing Lgr5

Buczacki, S. J. A., Zecchini, H. I., Nicholson, A. M., Russell, R., Vermeulen, L., Kemp, R., & Winton, D. J. (2013) · Nature · doi:10.1038/nature11965 · Cited by 761

Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms.

't Hoen, P. A. C., Ariyurek, Y., Thygesen, H. H., Vreugdenhil, E., Vossen, R. H. A. M., de Menezes, R. X., Boer, J. M., van Ommen, G.-J. B., & den Dunnen, J. T. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn705 · Cited by 742

T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection

McKinney, E. F., Lee, J. C., Jayne, D. R. W., Lyons, P. A., & Smith, K. G. C. (2015) · Nature · doi:10.1038/nature14468 · Cited by 685

Functional annotation of native enhancers with a Cas9–histone demethylase fusion

Kearns, N. A., Pham, H., Tabak, B., Genga, R. M., Silverstein, N. J., Garber, M., & Maehr, R. (2015) · Nature Methods · doi:10.1038/nmeth.3325 · Cited by 656

Target analysis by integration of transcriptome and ChIP-seq data with BETA

Wang, S., Sun, H., Ma, J., Zang, C., Wang, C., Wang, J., Tang, Q., Meyer, C. A., Zhang, Y., & Liu, X. S. (2013) · Nature Protocols · doi:10.1038/nprot.2013.150 · Cited by 544

BET inhibitor resistance emerges from leukaemia stem cells

Fong, C. Y., Gilan, O., Lam, E. Y. N., Rubin, A. F., Ftouni, S., Tyler, D., Stanley, K., Sinha, D., Yeh, P., Morison, J., et al. (2015) · Nature · doi:10.1038/nature14888 · Cited by 545

Naive pluripotency is associated with global DNA hypomethylation

Leitch, H. G., McEwen, K. R., Turp, A., Encheva, V., Carroll, T., Grabole, N., Mansfield, W., Nashun, B., Knezovich, J. G., Smith, A., et al. (2013) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2510 · Cited by 516

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

Wang, C., Gong, B., Bushel, P. R., Thierry-Mieg, J., Thierry-Mieg, D., Xu, J., Fang, H., Hong, H., Shen, J., Su, Z., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3001 · Cited by 512

Lactobacillus plantarum MB452 enhances the function of the intestinal barrier by increasing the expression levels of genes involved in tight junction formation

Anderson, R. C., Cookson, A. L., McNabb, W. C., Park, Z., McCann, M. J., Kelly, W. J., & Roy, N. C. (2010) · BMC Microbiology · doi:10.1186/1471-2180-10-316 · Cited by 510

Tet1 is dispensable for maintaining pluripotency and its loss is compatible with embryonic and postnatal development

Dawlaty, M. M., Ganz, K., Powell, B. E., Hu, Y.-C., Markoulaki, S., Cheng, A. W., Gao, Q., Kim, J., Choi, S.-W., Page, D. C., et al. (2011) · Cell stem cell · doi:10.1016/j.stem.2011.07.010 · Cited by 508

Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state

Verfaillie, A., Imrichova, H., Atak, Z. K., Dewaele, M., Rambow, F., Hulselmans, G., Christiaens, V., Svetlichnyy, D., Luciani, F., Van den Mooter, L., et al. (2015) · Nature Communications · doi:10.1038/ncomms7683 · Cited by 489

PresenceAbsence : An R Package for Presence Absence Analysis

Freeman, E. A., & Moisen, G. (2008) · Journal of Statistical Software · doi:10.18637/jss.v023.i11 · Cited by 461

Quantifying the local adaptive landscape of a nascent bacterial community

Ascensao, J. A., Wetmore, K. M., Good, B. H., Arkin, A. P., & Hallatschek, O. (2023) · Nature Communications · doi:10.1038/s41467-022-35677-5 · Cited by 447

Using control genes to correct for unwanted variation in microarray data

Gagnon-Bartsch, J. A., & Speed, T. P. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr034 · Cited by 442

A Systems Level Analysis of Transcriptional Changes in Alzheimer's Disease and Normal Aging

Miller, J. A., Oldham, M. C., & Geschwind, D. H. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4098-07.2008 · Cited by 385

DNA methylation profiling identifies epigenetic dysregulation in pancreatic islets from type 2 diabetic patients

Volkmar, M., Dedeurwaerder, S., Cunha, D. A., Ndlovu, M. N., Defrance, M., Deplus, R., Calonne, E., Volkmar, U., Igoillo‐Esteve, M., Naamane, N., et al. (2012) · The EMBO Journal · doi:10.1038/emboj.2011.503 · Cited by 420

Genome-wide Analysis of Chromatin Regulation by Cocaine Reveals a Role for Sirtuins

Renthal, W., Kumar, A., Xiao, G., Wilkinson, M., Covington, H. E., Maze, I., Sikder, D., Robison, A. J., LaPlant, Q., Dietz, D. M., et al. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.026 · Cited by 409

An Antioxidant Response Phenotype Shared between Hereditary and Sporadic Type 2 Papillary Renal Cell Carcinoma

Ooi, A., Wong, J.-C., Petillo, D., Roossien, D., Perrier-Trudova, V., Whitten, D., Min, B. W. H., Tan, M.-H., Zhang, Z., Yang, X. J., et al. (2011) · Cancer Cell · doi:10.1016/j.ccr.2011.08.024 · Cited by 401

A general modular framework for gene set enrichment analysis

Ackermann, M., & Strimmer, K. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-47 · Cited by 358

MicroRNA-regulated pathways associated with endometriosis

Ohlsson Teague, E. M. C., Van der Hoek, K. H., Van der Hoek, M. B., Perry, N., Wagaarachchi, P., Robertson, S. A., Print, C. G., & Hull, L. M. (2009) · Molecular Endocrinology · doi:10.1210/me.2008-0387 · Cited by 357

Drug repositioning: a machine-learning approach through data integration

Napolitano, F., Zhao, Y., Moreira, V. M., Tagliaferri, R., Kere, J., D’Amato, M., & Greco, D. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-30 · Cited by 349

Expression differences for genes involved in lignin, glutathione and sulphate metabolism in response to cadmium in Arabidopsis thaliana and the related Zn/Cd‐hyperaccumulator Thlaspi caerulescens

VAN DE MORTEL, J. E., SCHAT, H., MOERLAND, P. D., VAN THEMAAT, E. V. L., VAN DER ENT, S., BLANKESTIJN, H., GHANDILYAN, A., TSIATSIANI, S., & AARTS, M. G. M. (2008) · Plant, Cell & Environment · doi:10.1111/j.1365-3040.2007.01764.x · Cited by 326

Restricted diet delays accelerated ageing and genomic stress in DNA-repair-deficient mice

Vermeij, W. P., Dollé, M. E. T., Reiling, E., Jaarsma, D., Payan-Gomez, C., Bombardieri, C. R., Wu, H., Roks, A. J. M., Botter, S. M., van der Eerden, B. C., et al. (2016) · Nature · doi:10.1038/nature19329 · Cited by 315

Empirical array quality weights in the analysis of microarray data

Ritchie, M. E., Diyagama, D., Neilson, J., van Laar, R., Dobrovic, A., Holloway, A., & Smyth, G. K. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-261 · Cited by 312

microRNA‐101 is a potent inhibitor of autophagy

Frankel, L. B., Wen, J., Lees, M., Høyer‐Hansen, M., Farkas, T., Krogh, A., Jäättelä, M., & Lund, A. H. (2011) · The EMBO Journal · doi:10.1038/emboj.2011.331 · Cited by 312

IRF-3, IRF-5, and IRF-7 coordinately regulate the type I IFN response in myeloid dendritic cells downstream of MAVS signaling

Lazear, H. M., Lancaster, A., Wilkins, C., Suthar, M. S., Huang, A., Vick, S. C., Clepper, L., Thackray, L., Brassil, M. M., Virgin, H. W., et al. (2013) · PLoS Pathogens · doi:10.1371/journal.ppat.1003118 · Cited by 296

Gene expression in human brain implicates sexually dimorphic pathways in autism spectrum disorders

Werling, D. M., Parikshak, N. N., & Geschwind, D. H. (2016) · Nature Communications · doi:10.1038/ncomms10717 · Cited by 296

Interferon-γ links ultraviolet radiation to melanomagenesis in mice

Zaidi, M. R., Davis, S., Noonan, F. P., Graff-Cherry, C., Hawley, T. S., Walker, R. L., Feigenbaum, L., Fuchs, E., Lyakh, L., Young, H. A., et al. (2011) · Nature · doi:10.1038/nature09666 · Cited by 293

Broad defects in epidermal cornification in atopic dermatitis identified through genomic analysis

Guttman-Yassky, E., Suárez-Fariñas, M., Chiricozzi, A., Nograles, K. E., Shemer, A., Fuentes-Duculan, J., Cardinale, I., Lin, P., Bergman, R., Bowcock, A. M., et al. (2009) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2009.09.031 · Cited by 268

Sex differences in the genome-wide DNA methylation pattern and impact on gene expression, microRNA levels and insulin secretion in human pancreatic islets

Hall, E., Volkov, P., Dayeh, T., Esguerra, J. L. S., Salö, S., Eliasson, L., Rönn, T., Bacos, K., & Ling, C. (2014) · Genome Biology · doi:10.1186/s13059-014-0522-z · Cited by 249

Siglec-1 Is a Novel Dendritic Cell Receptor That Mediates HIV-1 Trans-Infection Through Recognition of Viral Membrane Gangliosides

Izquierdo-Useros, N., Lorizate, M., Puertas, M. C., Rodriguez-Plata, M. T., Zangger, N., Erikson, E., Pino, M., Erkizia, I., Glass, B., Clotet, B., et al. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001448 · Cited by 244

Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study

Li, S., Tighe, S. W., Nicolet, C. M., Grove, D., Levy, S., Farmerie, W., Viale, A., Wright, C., Schweitzer, P. A., Gao, Y., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2972 · Cited by 240

Gene expression profiling in the adult Down syndrome brain

Lockstone, H. E., Harris, L. W., Swatton, J. E., Wayland, M. T., Holland, A. J., & Bahn, S. (2007) · Genomics · doi:10.1016/j.ygeno.2007.08.005 · Cited by 240

Optimizing the noise versus bias trade-off for Illumina Whole Genome Expression Beadchips

Shi, W., Oshlack, A., & Smyth, G. K. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq871 · Cited by 225

Dual targeting of p53 and c-MYC selectively eliminates leukaemic stem cells

Abraham, S. A., Hopcroft, L. E. M., Carrick, E., Drotar, M. E., Dunn, K., Williamson, A. J. K., Korfi, K., Baquero, P., Park, L. E., Scott, M. T., et al. (2016) · Nature · doi:10.1038/nature18288 · Cited by 225

PU.1 target genes undergo Tet2-coupled demethylation and DNMT3b-mediated methylation in monocyte-to-osteoclast differentiation

de la Rica, L., Rodríguez-Ubreva, J., García, M., Islam, A. B., Urquiza, J. M., Hernando, H., Christensen, J., Helin, K., Gómez-Vaquero, C., & Ballestar, E. (2013) · Genome Biology · doi:10.1186/gb-2013-14-9-r99 · Cited by 211

Genome-wide DNA methylation analysis predicts an epigenetic switch for GATA factor expression in endometriosis

Dyson, M. T., Roqueiro, D., Monsivais, D., Ercan, C. M., Pavone, M. E., Brooks, D. C., Kakinuma, T., Ono, M., Jafari, N., Dai, Y., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004158 · Cited by 211

A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity

Tarca, A. L., Bhatti, G., & Romero, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079217 · Cited by 203

Detecting and correcting systematic variation in large-scale RNA sequencing data

Li, S., Łabaj, P. P., Zumbo, P., Sykacek, P., Shi, W., Shi, L., Phan, J., Wu, P.-Y., Wang, M., Wang, C., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3000 · Cited by 175

Childhood abuse is associated with methylation of multiple loci in adult DNA

Suderman, M., Borghol, N., Pappas, J. J., Pinto Pereira, S. M., Pembrey, M., Hertzman, C., Power, C., & Szyf, M. (2014) · BMC Medical Genomics · doi:10.1186/1755-8794-7-13 · Cited by 193

The histone variant macroH2A is an epigenetic regulator of key developmental genes

Buschbeck, M., Uribesalgo, I., Wibowo, I., Rué, P., Martin, D., Gutierrez, A., Morey, L., Guigó, R., López-Schier, H., & Di Croce, L. (2009) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1665 · Cited by 192

Small molecule activators of SIRT1 replicate signaling pathways triggered by calorie restriction in vivo

Smith, J. J., Kenney, R. D., Gagne, D. J., Frushour, B. P., Ladd, W., Galonek, H. L., Israelian, K., Song, J., Razvadauskaite, G., Lynch, A. V., et al. (2009) · BMC Systems Biology · doi:10.1186/1752-0509-3-31 · Cited by 186

Evolution combined with genomic study elucidates genetic bases of isobutanol tolerance in Escherichia coli

Minty, J. J., Lesnefsky, A. A., Lin, F., Chen, Y., Zaroff, T. A., Veloso, A. B., Xie, B., McConnell, C. A., Ward, R. J., Schwartz, D. R., et al. (2011) · Microbial Cell Factories · doi:10.1186/1475-2859-10-18 · Cited by 185

Distinctive transcriptome alterations of prefrontal pyramidal neurons in schizophrenia and schizoaffective disorder

Arion, D., Corradi, J. P., Tang, S., Datta, D., Boothe, F., He, A., Cacace, A. M., Zaczek, R., Albright, C. F., Tseng, G., et al. (2015) · Molecular Psychiatry · doi:10.1038/mp.2014.171 · Cited by 177

Genes and gene expression modules associated with caloric restriction and aging in the laboratory mouse

Swindell, W. R. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-585 · Cited by 171

Analysis of Global and Absorption, Distribution, Metabolism, and Elimination Gene Expression in the Progressive Stages of Human Nonalcoholic Fatty Liver Disease

Lake, A. D., Novak, P., Fisher, C. D., Jackson, J. P., Hardwick, R. N., Billheimer, D. D., Klimecki, W. T., & Cherrington, N. J. (2011) · Drug Metabolism and Disposition · doi:10.1124/dmd.111.040592 · Cited by 167

The transcriptional response of the islet to pregnancy in mice

Rieck, S., White, P., Schug, J., Fox, A. J., Smirnova, O., Gao, N., Gupta, R. K., Wang, Z. V., Scherer, P. E., Keller, M. P., et al. (2009) · Molecular Endocrinology · doi:10.1210/me.2009-0144 · Cited by 160

A comparison of the effects of three GM corn varieties on mammalian health

de Vendômois, J. S., Roullier, F., Cellier, D., & Séralini, G.-E. (2009) · International Journal of Biological Sciences · doi:10.7150/ijbs.5.706 · Cited by 160

Obesity impairs short-term and working memory through gut microbial metabolism of aromatic amino acids

Arnoriaga-Rodríguez, M., Mayneris-Perxachs, J., Burokas, A., Contreras-Rodríguez, O., Blasco, G., Coll, C., Biarnés, C., Miranda-Olivos, R., Latorre, J., Moreno-Navarrete, J.-M., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.09.002 · Cited by 157

Developmental regulation of human cortex transcription and its clinical relevance at single base resolution

Jaffe, A. E., Shin, J., Collado-Torres, L., Leek, J. T., Tao, R., Li, C., Gao, Y., Jia, Y., Maher, B. J., Hyde, T. M., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.3898 · Cited by 156

Scl Represses Cardiomyogenesis in Prospective Hemogenic Endothelium and Endocardium

Van Handel, B., Montel-Hagen, A., Sasidharan, R., Nakano, H., Ferrari, R., Boogerd, C. J., Schredelseker, J., Wang, Y., Hunter, S., Org, T., et al. (2012) · Cell · doi:10.1016/j.cell.2012.06.026 · Cited by 154

Diverse stresses dramatically alter genome-wide p53 binding and transactivation landscape in human cancer cells

Menendez, D., Nguyen, T.-A., Freudenberg, J. M., Mathew, V. J., Anderson, C. W., Jothi, R., & Resnick, M. A. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt504 · Cited by 154

miR-10b-5p expression in Huntington’s disease brain relates to age of onset and the extent of striatal involvement

Hoss, A. G., Labadorf, A., Latourelle, J. C., Kartha, V. K., Hadzi, T. C., Gusella, J. F., MacDonald, M. E., Chen, J.-F., Akbarian, S., Weng, Z., et al. (2015) · BMC Medical Genomics · doi:10.1186/s12920-015-0083-3 · Cited by 151

A whole-genome sequence and transcriptome perspective on HER2-positive breast cancers

Ferrari, A., Vincent-Salomon, A., Pivot, X., Sertier, A.-S., Thomas, E., Tonon, L., Boyault, S., Mulugeta, E., Treilleux, I., MacGrogan, G., et al. (2016) · Nature Communications · doi:10.1038/ncomms12222 · Cited by 150

Regulation of tumor angiogenesis and mesenchymal–endothelial transition by p38α through TGF-β and JNK signaling

Batlle, R., Andrés, E., Gonzalez, L., Llonch, E., Igea, A., Gutierrez-Prat, N., Berenguer-Llergo, A., & Nebreda, A. R. (2019) · Nature Communications · doi:10.1038/s41467-019-10946-y · Cited by 142

Spatial Differentiation in the Vegetative Mycelium of Aspergillus niger

Levin, A. M., de Vries, R. P., Conesa, A., de Bekker, C., Talon, M., Menke, H. H., van Peij, N. N. M. E., & Wösten, H. A. B. (2007) · Eukaryotic Cell · doi:10.1128/ec.00244-07 · Cited by 129

Generation of folliculogenic human epithelial stem cells from induced pluripotent stem cells

Yang, R., Zheng, Y., Burrows, M., Liu, S., Wei, Z., Nace, A., Guo, W., Kumar, S., Cotsarelis, G., & Xu, X. (2014) · Nature Communications · doi:10.1038/ncomms4071 · Cited by 125

Genetic diversity in proteolytic enzymes and amino acid metabolism among Lactobacillus helveticus strains

Broadbent, J. R., Cai, H., Larsen, R. L., Hughes, J. E., Welker, D. L., De Carvalho, V. G., Tompkins, T. A., Ardö, Y., Vogensen, F., De Lorentiis, A., et al. (2011) · Journal of Dairy Science · doi:10.3168/jds.2010-4068 · Cited by 124

Cellular, synaptic, and biochemical features of resilient cognition in Alzheimer’s disease

Arnold, S. E., Louneva, N., Cao, K., Wang, L.-S., Han, L.-Y., Wolk, D. A., Negash, S., Leurgans, S. E., Schneider, J. A., Buchman, A. S., et al. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.03.004 · Cited by 123

The effects of perinatal testosterone exposure on the DNA methylome of the mouse brain are late-emerging

Ghahramani, N. M., Ngun, T. C., Chen, P.-Y., Tian, Y., Krishnan, S., Muir, S., Rubbi, L., Arnold, A. P., de Vries, G. J., Forger, N. G., et al. (2014) · Biology of Sex Differences · doi:10.1186/2042-6410-5-8 · Cited by 121

How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets

Peixoto, L., Risso, D., Poplawski, S. G., Wimmer, M. E., Speed, T. P., Wood, M. A., & Abel, T. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv736 · Cited by 105

Microbial-Host Co-metabolites Are Prodromal Markers Predicting Phenotypic Heterogeneity in Behavior, Obesity, and Impaired Glucose Tolerance

Dumas, M.-E., Rothwell, A. R., Hoyles, L., Aranias, T., Chilloux, J., Calderari, S., Noll, E. M., Péan, N., Boulangé, C. L., Blancher, C., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.06.039 · Cited by 114

Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA sequencing in a porcine model

Kogelman, L. J. A., Cirera, S., Zhernakova, D. V., Fredholm, M., Franke, L., & Kadarmideen, H. N. (2014) · BMC Medical Genomics · doi:10.1186/1755-8794-7-57 · Cited by 106

Midbrain dopamine neurons in Parkinson׳s disease exhibit a dysregulated miRNA and target-gene network

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Fetal lung and placental methylation is associated with in utero nicotine exposure

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