Database resources of the National Center for Biotechnology Information: update

Basic local alignment search tool

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302

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

Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288

A general method applicable to the search for similarities in the amino acid sequence of two proteins

Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288

BUSCO Update: Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes

Manni, M., Berkeley, M. R., Seppey, M., Simão, F. A., & Zdobnov, E. M. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab199 · Cited by 6,187

PubChem Substance and Compound databases

Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436

A gene expression atlas of the central nervous system based on bacterial artificial chromosomes

Gong, S., Zheng, C., Doughty, M. L., Losos, K., Didkovsky, N., Schambra, U. B., Nowak, N. J., Joyner, A., Leblanc, G., Hatten, M. E., et al. (2003) · Nature · doi:10.1038/nature02033 · Cited by 2,108

Reactome knowledgebase of human biological pathways and processes

Matthews, L., Gopinath, G., Gillespie, M., Caudy, M., Croft, D., de Bono, B., Garapati, P., Hemish, J., Hermjakob, H., Jassal, B., et al. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn863 · Cited by 873

NCBI Reference Sequences: current status, policy and new initiatives

Pruitt, K. D., Tatusova, T., Klimke, W., & Maglott, D. R. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn721 · Cited by 736

Observed antibody space: a diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences

Olsen, T. H., Boyles, F., & Deane, C. M. (2022) · Protein Science · doi:10.1002/pro.4205 · Cited by 319

The reductive glycine pathway allows autotrophic growth of Desulfovibrio desulfuricans

Sánchez-Andrea, I., Guedes, I. A., Hornung, B., Boeren, S., Lawson, C. E., Sousa, D. Z., Bar-Even, A., Claassens, N. J., & Stams, A. J. M. (2020) · Nature Communications · doi:10.1038/s41467-020-18906-7 · Cited by 306

Assembly Properties of the Human Immunodeficiency Virus Type 1 CA Protein

Ganser-Pornillos, B. K., von Schwedler, U. K., Stray, K. M., Aiken, C., & Sundquist, W. I. (2004) · Journal of Virology · doi:10.1128/jvi.78.5.2545-2552.2004 · Cited by 302

Effects of GC Bias in Next-Generation-Sequencing Data on De Novo Genome Assembly

Chen, Y.-C., Liu, T., Yu, C.-H., Chiang, T.-Y., & Hwang, C.-C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062856 · Cited by 285

Comparative Genomics of Natural Killer Cell Receptor Gene Clusters

Kelley, J., Walter, L., & Trowsdale, J. (2005) · PLoS Genetics · doi:10.1371/journal.pgen.0010027 · Cited by 290

Predicting cell-to-cell communication networks using NATMI

Hou, R., Denisenko, E., Ong, H. T., Ramilowski, J. A., & Forrest, A. R. R. (2020) · Nature Communications · doi:10.1038/s41467-020-18873-z · Cited by 285

Advances in metabolite identification

Wishart, D. S. (2011) · Bioanalysis · doi:10.4155/bio.11.155 · Cited by 284

Expression of multiple horizontally acquired genes is a hallmark of both vertebrate and invertebrate genomes

Crisp, A., Boschetti, C., Perry, M., Tunnacliffe, A., & Micklem, G. (2015) · Genome Biology · doi:10.1186/s13059-015-0607-3 · Cited by 268

Broadening the horizon – level 2.5 of the HUPO-PSI format for molecular interactions

Kerrien, S., Orchard, S., Montecchi-Palazzi, L., Aranda, B., Quinn, A. F., Vinod, N., Bader, G. D., Xenarios, I., Wojcik, J., Sherman, D., et al. (2007) · BMC Biology · doi:10.1186/1741-7007-5-44 · Cited by 266

Current methods for automated filtering of multiple sequence alignments frequently worsen single-gene phylogenetic inference

Tan, G., Muffato, M., Ledergerber, C., Herrero, J., Goldman, N., Gil, M., & Dessimoz, C. (2015) · Systematic Biology · doi:10.1093/sysbio/syv033 · Cited by 261

SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

Stein, R. A., & Mchaourab, H. S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010483 · Cited by 258

A critical assessment of Mus musculusgene function prediction using integrated genomic evidence

Peña-Castillo, L., Tasan, M., Myers, C. L., Lee, H., Joshi, T., Zhang, C., Guan, Y., Leone, M., Pagnani, A., Kim, W. K., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-s1-s2 · Cited by 248

Observed antibody space: a resource for data mining next-generation sequencing of antibody repertoires

Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C. M., & Krawczyk, K. (2018) · The Journal of Immunology · doi:10.4049/jimmunol.1800708 · Cited by 247

Nrros negatively regulates reactive oxygen species during host defence and autoimmunity

Noubade, R., Wong, K., Ota, N., Rutz, S., Eidenschenk, C., Valdez, P. A., Ding, J., Peng, I., Sebrell, A., Caplazi, P., et al. (2014) · Nature · doi:10.1038/nature13152 · Cited by 246

Signatures of positive selection in genes associated with human skin pigmentation as revealed from analyses of single nucleotide polymorphisms

Lao, O., de Gruijter, J. M., van Duijn, K., Navarro, A., & Kayser, M. (2007) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2006.00341.x · Cited by 231

Protein interaction networks revealed by proteome coevolution

Cong, Q., Anishchenko, I., Ovchinnikov, S., & Baker, D. (2019) · Science · doi:10.1126/science.aaw6718 · Cited by 241

Identifying biases and their potential solutions in human microbiome studies

Nearing, J. T., Comeau, A. M., & Langille, M. G. I. (2021) · Microbiome · doi:10.1186/s40168-021-01059-0 · Cited by 240

Linking genes to literature: text mining, information extraction, and retrieval applications for biology

Krallinger, M., Valencia, A., & Hirschman, L. (2008) · Genome Biology · doi:10.1186/gb-2008-9-s2-s8 · Cited by 231

Consensus protein design

Porebski, B. T., & Buckle, A. M. (2016) · Protein Engineering Design and Selection · doi:10.1093/protein/gzw015 · Cited by 215

Comparison of Gene Expression Patterns Between Mouse Models of Nonalcoholic Fatty Liver Disease and Liver Tissues From Patients

Teufel, A., Itzel, T., Erhart, W., Brosch, M., Wang, X. Y., Kim, Y. O., von Schönfels, W., Herrmann, A., Brückner, S., Stickel, F., et al. (2016) · Gastroenterology · doi:10.1053/j.gastro.2016.05.051 · Cited by 229

Zebrafish information network, the knowledgebase for Danio rerio research

Bradford, Y. M., Van Slyke, C. E., Ruzicka, L., Singer, A., Eagle, A., Fashena, D., Howe, D. G., Frazer, K., Martin, R., Paddock, H., et al. (2022) · Genetics · doi:10.1093/genetics/iyac016 · Cited by 229

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity

Ciallella, H. L., & Zhu, H. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.8b00393 · Cited by 227

The IUPHAR/BPS Guide to PHARMACOLOGY in 2024

Harding, S. D., Armstrong, J. F., Faccenda, E., Southan, C., Alexander, S. P. H., Davenport, A. P., Spedding, M., & Davies, J. A. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad944 · Cited by 226

Computational approaches to therapeutic antibody design: established methods and emerging trends

Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219

The BRENDA enzyme information system–From a database to an expert system

Schomburg, I., Jeske, L., Ulbrich, M., Placzek, S., Chang, A., & Schomburg, D. (2017) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2017.04.020 · Cited by 222

A genome-wide positioning systems network algorithm for in silico drug repurposing

Cheng, F., Lu, W., Liu, C., Fang, J., Hou, Y., Handy, D. E., Wang, R., Zhao, Y., Yang, Y., Huang, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10744-6 · Cited by 215

OMA 2011: orthology inference among 1000 complete genomes

Altenhoff, A. M., Schneider, A., Gonnet, G. H., & Dessimoz, C. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1238 · Cited by 215

“Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks

Gillis, J., & Pavlidis, P. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002444 · Cited by 214

Phylogenetic diversity of stress signalling pathways in fungi

Nikolaou, E., Agrafioti, I., Stumpf, M., Quinn, J., Stansfield, I., & Brown, A. J. (2009) · BMC Evolutionary Biology · doi:10.1186/1471-2148-9-44 · Cited by 206

AutismKB: an evidence-based knowledgebase of autism genetics

Xu, L.-M., Li, J.-R., Huang, Y., Zhao, M., Tang, X., & Wei, L. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1145 · Cited by 179

Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

Blais, E. M., Rawls, K. D., Dougherty, B. V., Li, Z. I., Kolling, G. L., Ye, P., Wallqvist, A., & Papin, J. A. (2017) · Nature Communications · doi:10.1038/ncomms14250 · Cited by 182

Dysbiosis, gut barrier dysfunction and inflammation in dementia: a pilot study

Stadlbauer, V., Engertsberger, L., Komarova, I., Feldbacher, N., Leber, B., Pichler, G., Fink, N., Scarpatetti, M., Schippinger, W., Schmidt, R., et al. (2020) · BMC Geriatrics · doi:10.1186/s12877-020-01644-2 · Cited by 182

PepBank - a database of peptides based on sequence text mining and public peptide data sources.

Shtatland, T., Guettler, D., Kossodo, M., Pivovarov, M., & Weissleder, R. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-280 · Cited by 181

Inflammation and Airway Microbiota during Cystic Fibrosis Pulmonary Exacerbations

Zemanick, E. T., Harris, J. K., Wagner, B. D., Robertson, C. E., Sagel, S. D., Stevens, M. J., Accurso, F. J., & Laguna, T. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062917 · Cited by 180

A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19

Zhou, Y., Hou, Y., Shen, J., Mehra, R., Kallianpur, A., Culver, D. A., Gack, M. U., Farha, S., Zein, J., Comhair, S., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000970 · Cited by 180

GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information

Jin, Q., Yang, Y., Chen, Q., & Lu, Z. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae075 · Cited by 176

Definition, conservation and epigenetics of housekeeping and tissue-enriched genes

She, X., Rohl, C. A., Castle, J. C., Kulkarni, A. V., Johnson, J. M., & Chen, R. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-269 · Cited by 173

Bispecific antibodies targeting mutant RAS neoantigens

Douglass, J., Hsiue, E. H.-C., Mog, B. J., Hwang, M. S., DiNapoli, S. R., Pearlman, A. H., Miller, M. S., Wright, K. M., Azurmendi, P. A., Wang, Q., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abd5515 · Cited by 171

Greywater recycling in buildings using living walls and green roofs: a review of the applicability and challenges

Pradhan, S., Al-Ghamdi, S. G., & Mackey, H. R. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.10.226 · Cited by 168

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour ’rules’

Draper, J., Enot, D. P., Parker, D., Beckmann, M., Snowdon, S., Lin, W., & Zubair, H. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-227 · Cited by 169

A genome-wide in situhybridization map of RNA-binding proteins reveals anatomically restricted expression in the developing mouse brain

McKee, A. E., Minet, E., Stern, C., Riahi, S., Stiles, C. D., & Silver, P. A. (2005) · BMC Developmental Biology · doi:10.1186/1471-213x-5-14 · Cited by 166

Accelerated Recruitment of New Brain Development Genes into the Human Genome

Zhang, Y. E., Landback, P., Vibranovski, M. D., & Long, M. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001179 · Cited by 164

MitoMiner v3.1, an update on the mitochondrial proteomics database

Smith, A. C., & Robinson, A. J. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1001 · Cited by 162

Subterranean mammals show convergent regression in ocular genes and enhancers, along with adaptation to tunneling

Partha, R., Chauhan, B. K., Ferreira, Z., Robinson, J. D., Lathrop, K., Nischal, K. K., Chikina, M., & Clark, N. L. (2017) · eLife · doi:10.7554/elife.25884 · Cited by 160

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

A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1

Sigurdsson, M. I., Jamshidi, N., Steingrimsson, E., Thiele, I., & Palsson, B. Ø. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-140 · Cited by 154

Big data in chemical toxicity research: The use of high-throughput screening assays to identify potential toxicants

Zhu, H., Zhang, J., Kim, M. T., Boison, A., Sedykh, A., & Moran, K. (2014) · Chemical Research in Toxicology · doi:10.1021/tx500145h · Cited by 154

YMAP: a pipeline for visualization of copy number variation and loss of heterozygosity in eukaryotic pathogens

Abbey, D. A., Funt, J., Lurie-Weinberger, M. N., Thompson, D. A., Regev, A., Myers, C. L., & Berman, J. (2014) · Genome Medicine · doi:10.1186/s13073-014-0100-8 · Cited by 147

The IUPHAR/BPS guide to PHARMACOLOGY in 2022: curating pharmacology for COVID-19, malaria and antibacterials

Harding, S. D., Armstrong, J. F., Faccenda, E., Southan, C., Alexander, S. P. H., Davenport, A. P., Pawson, A. J., Spedding, M., Davies, J. A., & NC-IUPHAR (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1010 · Cited by 149

Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans

Segal, E. S., Gritsenko, V., Levitan, A., Yadav, B., Dror, N., Steenwyk, J. L., Silberberg, Y., Mielich, K., Rokas, A., Gow, N. A. R., et al. (2018) · mBio · doi:10.1128/mbio.02048-18 · Cited by 150

Predicting drug-target interaction network using deep learning model

You, J., McLeod, R. D., & Hu, P. (2019) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2019.03.016 · Cited by 150

Microalgae with Artificial Intelligence: A Digitalized Perspective on Genetics, Systems and Products

Teng, S. Y., Yew, G. Y., Sukačová, K., Show, P. L., Máša, V., & Chang, J.-S. (2020) · Biotechnology Advances · doi:10.1016/j.biotechadv.2020.107631 · Cited by 145

Accurately assessing the risk of schizophrenia conferred by rare copy-number variation affecting genes with brain function

Raychaudhuri, S., Korn, J. M., McCarroll, S. A., The International Schizophrenia Consortium, Altshuler, D., Sklar, P., Purcell, S., & Daly, M. J. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001097 · Cited by 131

Ligand-dependent dynamics of retinoic acid receptor binding during early neurogenesis

Mahony, S., Mazzoni, E. O., McCuine, S., Young, R. A., Wichterle, H., & Gifford, D. K. (2011) · Genome Biology · doi:10.1186/gb-2011-12-1-r2 · Cited by 142

Current Status and Future Prospects of Marine Natural Products (MNPs) as Antimicrobials

Choudhary, A., Naughton, L., Montánchez, I., Dobson, A., & Rai, D. (2017) · Marine Drugs · doi:10.3390/md15090272 · Cited by 138

A structure-informed atlas of human-virus interactions

Lasso, G., Mayer, S. V., Winkelmann, E. R., Chu, T., Elliot, O., Patino-Galindo, J. A., Park, K., Rabadan, R., Honig, B., & Shapira, S. D. (2019) · Cell · doi:10.1016/j.cell.2019.08.005 · Cited by 133

A positive correlation between GC content and growth temperature in prokaryotes

Hu, E.-Z., Lan, X.-R., Liu, Z.-L., Gao, J., & Niu, D.-K. (2022) · BMC Genomics · doi:10.1186/s12864-022-08353-7 · Cited by 135

Phylogenomic analysis of the GIY-YIG nuclease superfamily

Dunin-Horkawicz, S., Feder, M., & Bujnicki, J. M. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-98 · Cited by 133

Lateral gene transfer as a support for the tree of life

Abby, S. S., Tannier, E., Gouy, M., & Daubin, V. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116871109 · Cited by 133

PolyA_DB: a database for mammalian mRNA polyadenylation

Zhang, H. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki055 · Cited by 132

A Human-Specific De Novo Protein-Coding Gene Associated with Human Brain Functions

Li, C.-Y., Zhang, Y., Wang, Z., Zhang, Y., Cao, C., Zhang, P.-W., Lu, S.-J., Li, X.-M., Yu, Q., Zheng, X., et al. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000734 · Cited by 130

Viromes of one year old infants reveal the impact of birth mode on microbiome diversity

McCann, A., Ryan, F. J., Stockdale, S. R., Dalmasso, M., Blake, T., Ryan, C. A., Stanton, C., Mills, S., Ross, P. R., & Hill, C. (2018) · PeerJ · doi:10.7717/peerj.4694 · Cited by 129

Bioinformatics opportunities for identification and study of medicinal plants

Sharma, V., & Sarkar, I. N. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs021 · Cited by 121

Functional coordination of alternative splicing in the mammalian central nervous system

Fagnani, M., Barash, Y., Ip, J. Y., Misquitta, C., Pan, Q., Saltzman, A. L., Shai, O., Lee, L., Rozenhek, A., Mohammad, N., et al. (2007) · Genome Biology · doi:10.1186/gb-2007-8-6-r108 · Cited by 120

In vivo commensal control of Clostridioides difficile virulence

Girinathan, B. P., DiBenedetto, N., Worley, J. N., Peltier, J., Arrieta-Ortiz, M. L., Immanuel, S. R. C., Lavin, R., Delaney, M. L., Cummins, C. K., Hoffman, M., et al. (2021) · Cell Host & Microbe · doi:10.1016/j.chom.2021.09.007 · Cited by 114

Transcript and protein expression decoupling reveals RNA binding proteins and miRNAs as potential modulators of human aging

Wei, Y.-N., Hu, H.-Y., Xie, G.-C., Fu, N., Ning, Z.-B., Zeng, R., & Khaitovich, P. (2015) · Genome Biology · doi:10.1186/s13059-015-0608-2 · Cited by 117

FIGL1 and its novel partner FLIP form a conserved complex that regulates homologous recombination

Fernandes, J. B., Duhamel, M., Seguéla-Arnaud, M., Froger, N., Girard, C., Choinard, S., Solier, V., De Winne, N., De Jaeger, G., Gevaert, K., et al. (2018) · PLOS Genetics · doi:10.1371/journal.pgen.1007317 · Cited by 116

Multilaboratory Survey To Evaluate Salmonella Prevalence in Diarrheic and Nondiarrheic Dogs and Cats in the United States between 2012 and 2014

Reimschuessel, R., Grabenstein, M., Guag, J., Nemser, S. M., Song, K., Qiu, J., Clothier, K. A., Byrne, B. A., Marks, S. L., Cadmus, K., et al. (2017) · Journal of Clinical Microbiology · doi:10.1128/jcm.02137-16 · Cited by 105

Cultured microbes represent a substantial fraction of the human and mouse gut microbiota

Lagkouvardos, I., Overmann, J., & Clavel, T. (2017) · Gut Microbes · doi:10.1080/19490976.2017.1320468 · Cited by 104

Development of broad‐spectrum human monoclonal antibodies for rabies post‐exposure prophylaxis

De Benedictis, P., Minola, A., Rota Nodari, E., Aiello, R., Zecchin, B., Salomoni, A., Foglierini, M., Agatic, G., Vanzetta, F., Lavenir, R., et al. (2016) · EMBO Molecular Medicine · doi:10.15252/emmm.201505986 · Cited by 103

Rumen methanogenic genotypes differ in abundance according to host residual feed intake phenotype and diet type

Carberry, C. A., Waters, S. M., Kenny, D. A., & Creevey, C. J. (2014) · Applied and Environmental Microbiology · doi:10.1128/aem.03131-13 · Cited by 100

Computational scoring and experimental evaluation of enzymes generated by neural networks

Johnson, S. R., Fu, X., Viknander, S., Goldin, C., Monaco, S., Zelezniak, A., & Yang, K. K. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02214-2 · Cited by 99

Biomarkers for oralization during long-term proton pump inhibitor therapy predict survival in cirrhosis

Horvath, A., Rainer, F., Bashir, M., Leber, B., Schmerboeck, B., Klymiuk, I., Groselj-Strele, A., Durdevic, M., Freedberg, D. E., Abrams, J. A., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-48352-5 · Cited by 93

Brief Report: A Survey of Autism Research Priorities Across a Diverse Community of Stakeholders

Frazier, T. W., Dawson, G., Murray, D., Shih, A., Sachs, J. S., & Geiger, A. (2018) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-018-3642-6 · Cited by 97

Unraveling the Chinese hamster ovary cell line transcriptome by next-generation sequencing

Becker, J., Hackl, M., Rupp, O., Jakobi, T., Schneider, J., Szczepanowski, R., Bekel, T., Borth, N., Goesmann, A., Grillari, J., et al. (2011) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2011.09.014 · Cited by 98

The evolution and structure prediction of coiled coils across all genomes.

Rackham, O. J. L., Madera, M., Armstrong, C. T., Vincent, T. L., Woolfson, D. N., & Gough, J. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.08.032 · Cited by 97

Functionally Enigmatic Genes: A Case Study of the Brain Ignorome

Pandey, A. K., Lu, L., Wang, X., Homayouni, R., & Williams, R. W. (2014) · PLoS ONE · doi:10.1371/journal.pone.0088889 · Cited by 90

Biological mechanisms of aging predict age-related disease co-occurrence in patients

Fraser, H. C., Kuan, V., Johnen, R., Zwierzyna, M., Hingorani, A. D., Beyer, A., & Partridge, L. (2022) · Aging Cell · doi:10.1111/acel.13524 · Cited by 90

Predicting Gene Essentiality Using Genome-Scale in Silico Models

Joyce, A. R., & Palsson, B. Ø. (2008) · Methods in Molecular Biology™ · doi:10.1007/978-1-59745-321-9_30 · Cited by 87

A gene expression profile of stem cell pluripotentiality and differentiation is conserved across diverse solid and hematopoietic cancers

Palmer, N. P., Schmid, P. R., Berger, B., & Kohane, I. S. (2012) · Genome Biology · doi:10.1186/gb-2012-13-8-r71 · Cited by 86

sdAb-DB: the single domain antibody database

Wilton, E. E., Opyr, M. P., Kailasam, S., Kothe, R. F., & Wieden, H.-J. (2018) · ACS Synthetic Biology · doi:10.1021/acssynbio.8b00407 · Cited by 84

De novo protein identification in mammalian sperm using in situ cryoelectron tomography and AlphaFold2 docking

Chen, Z., Shiozaki, M., Haas, K. M., Skinner, W. M., Zhao, S., Guo, C., Polacco, B. J., Yu, Z., Krogan, N. J., Lishko, P. V., et al. (2023) · Cell · doi:10.1016/j.cell.2023.09.017 · Cited by 82

Polymorphisms in the Estrogen Receptor 1 and Vitamin C and Matrix Metalloproteinase Gene Families Are Associated with Susceptibility to Lymphoma

Skibola, C. F., Bracci, P. M., Halperin, E., Nieters, A., Hubbard, A., Paynter, R. A., Skibola, D. R., Agana, L., Becker, N., Tressler, P., et al. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002816 · Cited by 80

Discovery of high-confidence human protein-coding genes and exons by whole-genome PhyloCSF helps elucidate 118 GWAS loci

Mudge, J. M., Jungreis, I., Hunt, T., Gonzalez, J. M., Wright, J. C., Kay, M., Davidson, C., Fitzgerald, S., Seal, R., Tweedie, S., et al. (2019) · Genome Research · doi:10.1101/gr.246462.118 · Cited by 80

Dietary- and host-derived metabolites are used by diverse gut bacteria for anaerobic respiration

Little, A. S., Younker, I. T., Schechter, M. S., Bernardino, P. N., Méheust, R., Stemczynski, J., Scorza, K., Mullowney, M. W., Sharan, D., Waligurski, E., et al. (2024) · Nature Microbiology · doi:10.1038/s41564-023-01560-2 · Cited by 80

The mouse Gene Expression Database (GXD): 2014 update

Smith, C. M., Finger, J. H., Hayamizu, T. F., McCright, I. J., Xu, J., Berghout, J., Campbell, J., Corbani, L. E., Forthofer, K. L., Frost, P. J., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt954 · Cited by 75

A novel Cas family member, HEPL, regulates FAK and cell spreading

Singh, M. K., Dadke, D., Nicolas, E., Serebriiskii, I. G., Apostolou, S., Canutescu, A., Egleston, B. L., & Golemis, E. A. (2008) · Molecular Biology of the Cell · doi:10.1091/mbc.e07-09-0953 · Cited by 74

Basal DNA repair machinery is subject to positive selection in ionizing-radiation-resistant bacteria

Sghaier, H., Ghedira, K., Benkahla, A., & Barkallah, I. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-297 · Cited by 71

Emergence of novel cephalopod gene regulation and expression through large-scale genome reorganization

Schmidbaur, H., Kawaguchi, A., Clarence, T., Fu, X., Hoang, O. P., Zimmermann, B., Ritschard, E. A., Weissenbacher, A., Foster, J. S., Nyholm, S. V., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29694-7 · Cited by 66

Semantic Web meets Integrative Biology: a survey

Chen, H., Yu, T., & Chen, J. Y. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs014 · Cited by 65

Analysis of chromosomal aberrations and recombination by allelic bias in RNA-Seq

Weissbein, U., Schachter, M., Egli, D., & Benvenisty, N. (2016) · Nature Communications · doi:10.1038/ncomms12144 · Cited by 64

The Iceman’s Last Meal Consisted of Fat, Wild Meat, and Cereals

Maixner, F., Turaev, D., Cazenave-Gassiot, A., Janko, M., Krause-Kyora, B., Hoopmann, M. R., Kusebauch, U., Sartain, M., Guerriero, G., O’Sullivan, N., et al. (2018) · Current Biology · doi:10.1016/j.cub.2018.05.067 · Cited by 63

Thalia: semantic search engine for biomedical abstracts

Soto, A. J., Przybyła, P., & Ananiadou, S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty871 · Cited by 60

Hallstatt miners consumed blue cheese and beer during the Iron Age and retained a non-Westernized gut microbiome until the Baroque period

Maixner, F., Sarhan, M. S., Huang, K. D., Tett, A., Schoenafinger, A., Zingale, S., Blanco-Míguez, A., Manghi, P., Cemper-Kiesslich, J., Rosendahl, W., et al. (2021) · Current Biology · doi:10.1016/j.cub.2021.09.031 · Cited by 62

A Systematic Search for Endoplasmic Reticulum (ER) Membrane-associated RING Finger Proteins Identifies Nixin/ZNRF4 as a Regulator of Calnexin Stability and ER Homeostasis

Neutzner, A., Neutzner, M., Benischke, A.-S., Ryu, S.-W., Frank, S., Youle, R. J., & Karbowski, M. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.197459 · Cited by 62

Advances in Genome Editing and Application to the Generation of Genetically Modified Rat Models

Chenouard, V., Remy, S., Tesson, L., Ménoret, S., Ouisse, L.-H., Cherifi, Y., & Anegon, I. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.615491 · Cited by 57

Human metabolic network reconstruction and its impact on drug discovery and development

Ma, H., & Goryanin, I. (2008) · Drug Discovery Today · doi:10.1016/j.drudis.2008.02.002 · Cited by 59

Predicting early Alzheimer’s with blood biomarkers and clinical features

AlMansoori, M. E., Jemimah, S., Abuhantash, F., & AlShehhi, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-56489-1 · Cited by 58

Global genetic diversity of var2csa in Plasmodium falciparum with implications for malaria in pregnancy and vaccine development

Benavente, E. D., Oresegun, D. R., de Sessions, P. F., Walker, E. M., Roper, C., Dombrowski, J. G., de Souza, R. M., Marinho, C. R. F., Sutherland, C. J., Hibberd, M. L., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-33767-3 · Cited by 47

Microbial composition of tumorous and adjacent gastric tissue is associated with prognosis of gastric cancer

Lehr, K., Nikitina, D., Vilchez-Vargas, R., Steponaitiene, R., Thon, C., Skieceviciene, J., Schanze, D., Zenker, M., Malfertheiner, P., Kupcinskas, J., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-31740-3 · Cited by 54