KEGG: Kyoto Encyclopedia of Genes and Genomes

A Comprehensive Mouse Transcriptomic BodyMap across 17 Tissues by RNA-seq

Li, B., Qing, T., Zhu, J., Wen, Z., Yu, Y., Fukumura, R., Zheng, Y., Gondo, Y., & Shi, L. (2017) · Scientific Reports · doi:10.1038/s41598-017-04520-z · Cited by 184

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

Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology

Dehkordi, S. K., Walker, J., Sah, E., Bennett, E., Atrian, F., Frost, B., Woost, B., Bennett, R. E., Orr, T. C., Zhou, Y., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00142-3 · Cited by 169

Oil accumulation mechanisms of the oleaginous microalga Chlorella protothecoides revealed through its genome, transcriptomes, and proteomes

Gao, C., Wang, Y., Shen, Y., Yan, D., He, X., Dai, J., & Wu, Q. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-582 · Cited by 175

Tracing Oxygen's Imprint on Earth's Metabolic Evolution

Falkowski, P. G. (2006) · Science · doi:10.1126/science.1125937 · Cited by 175

Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

Himmelstein, D. S., & Baranzini, S. E. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004259 · Cited by 172

Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of Clostridium ljungdahlii

Nagarajan, H., Sahin, M., Nogales, J., Latif, H., Lovley, D. R., Ebrahim, A., & Zengler, K. (2013) · Microbial Cell Factories · doi:10.1186/1475-2859-12-118 · Cited by 170

Dissection of Regulatory Networks that Are Altered in Disease via Differential Co-expression

Amar, D., Safer, H., & Shamir, R. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002955 · Cited by 169

Probiotic Bifidobacterium longum alters gut luminal metabolism through modification of the gut microbial community

Sugahara, H., Odamaki, T., Fukuda, S., Kato, T., Xiao, J.-Z., Abe, F., Kikuchi, J., & Ohno, H. (2015) · Scientific Reports · doi:10.1038/srep13548 · Cited by 169

A Systems Biology Framework Identifies Molecular Underpinnings of Coronary Heart Disease

Huan, T., Zhang, B., Wang, Z., Joehanes, R., Zhu, J., Johnson, A. D., Ying, S., Munson, P. J., Raghavachari, N., Wang, R., et al. (2013) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.112.300112 · Cited by 167

Prolonged use of a proton pump inhibitor reduces microbial diversity: implications for Clostridium difficile susceptibility

Seto, C. T., Jeraldo, P., Orenstein, R., Chia, N., & DiBaise, J. K. (2014) · Microbiome · doi:10.1186/2049-2618-2-42 · Cited by 163

Mitochondrial CoQ deficiency is a common driver of mitochondrial oxidants and insulin resistance

Fazakerley, D. J., Chaudhuri, R., Yang, P., Maghzal, G. J., Thomas, K. C., Krycer, J. R., Humphrey, S. J., Parker, B. L., Fisher-Wellman, K. H., Meoli, C. C., et al. (2018) · eLife · doi:10.7554/elife.32111 · Cited by 160

Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age

Johnson, A. A., Shokhirev, M. N., Wyss-Coray, T., & Lehallier, B. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101070 · Cited by 166

Elimination of p19ARF-expressing cells enhances pulmonary function in mice

Hashimoto, M., Asai, A., Kawagishi, H., Mikawa, R., Iwashita, Y., Kanayama, K., Sugimoto, K., Sato, T., Maruyama, M., & Sugimoto, M. (2016) · JCI Insight · doi:10.1172/jci.insight.87732 · Cited by 162

Dietary heme induces gut dysbiosis, aggravates colitis, and potentiates the development of adenomas in mice

Constante, M., Fragoso, G., Calvé, A., Samba-Mondonga, M., & Santos, M. M. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01809 · Cited by 161

Meta-analytic support vector machine for integrating multiple omics data

Kim, S., Jhong, J.-H., Lee, J., & Koo, J.-Y. (2017) · BioData Mining · doi:10.1186/s13040-017-0126-8 · Cited by 159

CowPI: a rumen microbiome focussed version of the PICRUSt functional inference software

Wilkinson, T. J., Huws, S. A., Edwards, J. E., Kingston-Smith, A. H., Siu-Ting, K., Hughes, M., Rubino, F., Friedersdorff, M., & Creevey, C. J. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.01095 · Cited by 160

GWAS for executive function and processing speed suggests involvement of the CADM2 gene

Ibrahim-Verbaas, C. A., Bressler, J., Debette, S., Schuur, M., Smith, A. V., Bis, J. C., Davies, G., Trompet, S., Smith, J. A., Wolf, C., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.37 · Cited by 147

A clinician's guide to microbiome analysis

Claesson, M. J., Clooney, A. G., & O'Toole, P. W. (2017) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2017.97 · Cited by 153

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

P atika: an integrated visual environment for collaborative construction and analysis of cellular pathways

Demir, E., Babur, O., Dogrusoz, U., Gursoy, A., Nisanci, G., Cetin-Atalay, R., & Ozturk, M. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.7.996 · Cited by 156

Overcoming Genetically Based Resistance Mechanisms to PD-1 Blockade

Torrejon, D. Y., Abril-Rodriguez, G., Champhekar, A. S., Tsoi, J., Campbell, K. M., Kalbasi, A., Parisi, G., Zaretsky, J. M., Garcia-Diaz, A., Puig-Saus, C., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-1409 · Cited by 146

AD-linked R47H- TREM2 mutation induces disease-enhancing microglial states via AKT hyperactivation

Sayed, F. A., Kodama, L., Fan, L., Carling, G. K., Udeochu, J. C., Le, D., Li, Q., Zhou, L., Wong, M. Y., Horowitz, R., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abe3947 · Cited by 155

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

Development of an in vitro human liver system for interrogating nonalcoholic steatohepatitis

Feaver, R. E., Cole, B. K., Lawson, M. J., Hoang, S. A., Marukian, S., Blackman, B. R., Figler, R. A., Sanyal, A. J., Wamhoff, B. R., & Dash, A. (2016) · JCI Insight · doi:10.1172/jci.insight.90954 · Cited by 153

Web-based resources for mass-spectrometry-based metabolomics: a user’s guide

Tohge, T., & Fernie, A. R. (2009) · Phytochemistry · doi:10.1016/j.phytochem.2009.02.004 · Cited by 152

Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation

Baek, S., & Lee, I. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.012 · Cited by 151

A human multi-lineage hepatic organoid model for liver fibrosis

Guan, Y., Enejder, A., Wang, M., Fang, Z., Cui, L., Chen, S.-Y., Wang, J., Tan, Y., Wu, M., Chen, X., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26410-9 · Cited by 149

Altered gut microbiota associated with symptom severity in schizophrenia

Li, S., Zhuo, M., Huang, X., Huang, Y., Zhou, J., Xiong, D., Li, J., Liu, Y., Pan, Z., Li, H., et al. (2020) · PeerJ · doi:10.7717/peerj.9574 · Cited by 142

Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models

Aite, M., Chevallier, M., Frioux, C., Trottier, C., Got, J., Cortés, M. P., Mendoza, S. N., Carrier, G., Dameron, O., Guillaudeux, N., et al. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006146 · Cited by 137

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

Transcriptional consequences of 16p11.2 deletion and duplication in mouse cortex and multiplex autism families

Blumenthal, I., Ragavendran, A., Erdin, S., Klei, L., Sugathan, A., Guide, J. R., Manavalan, P., Zhou, J. Q., Wheeler, V. C., Levin, J. Z., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.05.004 · Cited by 145

Red fox genome assembly identifies genomic regions associated with tame and aggressive behaviours

Kukekova, A. V., Johnson, J. L., Xiang, X., Feng, S., Liu, S., Rando, H. M., Kharlamova, A. V., Herbeck, Y., Serdyukova, N. A., Xiong, Z., et al. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0611-6 · Cited by 145

Transcriptional and functional insights into the host immune response against the emerging fungal pathogen Candida auris

Bruno, M., Kersten, S., Bain, J. M., Jaeger, M., Rosati, D., Kruppa, M. D., Lowman, D. W., Rice, P. J., Graves, B., Ma, Z., et al. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0780-3 · Cited by 145

Discovery of phosphatidylcholines and sphingomyelins as biomarkers for ovarian endometriosis

Vouk, K., Hevir, N., Ribič-Pucelj, M., Haarpaintner, G., Scherb, H., Osredkar, J., Möller, G., Prehn, C., Rižner, T. L., & Adamski, J. (2012) · Human Reproduction · doi:10.1093/humrep/des152 · Cited by 143

A comprehensive modular map of molecular interactions in RB/E2F pathway

Calzone, L., Gelay, A., Zinovyev, A., Radvanyi, F., & Barillot, E. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.7 · Cited by 132

Exploration of databases and methods supporting drug repurposing: a comprehensive survey

Tanoli, Z., Seemab, U., Scherer, A., Wennerberg, K., Tang, J., & Vähä-Koskela, M. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa003 · Cited by 131

The goose genome sequence leads to insights into the evolution of waterfowl and susceptibility to fatty liver

Lu, L., Chen, Y., Wang, Z., Li, X., Chen, W., Tao, Z., Shen, J., Tian, Y., Wang, D., Li, G., et al. (2015) · Genome biology · doi:10.1186/s13059-015-0652-y · Cited by 137

Metabolic Trade-offs in Yeast are Caused by F1F0-ATP synthase

Nilsson, A., & Nielsen, J. (2016) · Scientific Reports · doi:10.1038/srep22264 · Cited by 137

DNA methylation across the genome in aged human skeletal muscle tissue and muscle-derived cells: The role of HOX genes and physical activity

Turner, D. C., Gorski, P. P., Maasar, M. F., Seaborne, R. A., Baumert, P., Brown, A. D., Kitchen, M. O., Erskine, R. M., Dos-Remedios, I., Voisin, S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-72730-z · Cited by 139

Acute sleep loss results in tissue-specific alterations in genome-wide DNA methylation state and metabolic fuel utilization in humans

Cedernaes, J., Schönke, M., Westholm, J. O., Mi, J., Chibalin, A., Voisin, S., Osler, M., Vogel, H., Hörnaeus, K., Dickson, S. L., et al. (2018) · Science Advances · doi:10.1126/sciadv.aar8590 · Cited by 137

Serum Biomarkers of Habitual Coffee Consumption May Provide Insight into the Mechanism Underlying the Association between Coffee Consumption and Colorectal Cancer

Guertin, K. A., Loftfield, E., Boca, S. M., Sampson, J. N., Moore, S. C., Xiao, Q., Huang, W.-Y., Xiong, X., Freedman, N. D., Cross, A. J., et al. (2015) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.114.096099 · Cited by 134

Osteocyte transcriptome mapping identifies a molecular landscape controlling skeletal homeostasis and susceptibility to skeletal disease

Youlten, S. E., Kemp, J. P., Logan, J. G., Ghirardello, E. J., Sergio, C. M., Dack, M. R. G., Guilfoyle, S. E., Leitch, V. D., Butterfield, N. C., Komla-Ebri, D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22517-1 · Cited by 133

Weaning age influences the severity of gastrointestinal microbiome shifts in dairy calves

Meale, S. J., Li, S. C., Azevedo, P., Derakhshani, H., DeVries, T. J., Plaizier, J. C., Steele, M. A., & Khafipour, E. (2017) · Scientific Reports · doi:10.1038/s41598-017-00223-7 · Cited by 133

The Role of Energy Metabolism Dysfunction and Oxidative Stress in Schizophrenia Revealed by Proteomics

Martins-de-Souza, D., Harris, L. W., Guest, P. C., & Bahn, S. (2011) · Antioxidants & Redox Signaling · doi:10.1089/ars.2010.3459 · Cited by 134

Deep learning allows genome-scale prediction of Michaelis constants from structural features

Kroll, A., Engqvist, M. K. M., Heckmann, D., & Lercher, M. J. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001402 · Cited by 132

Short-term high fat-feeding results in morphological and metabolic adaptations in the skeletal muscle of C57BL/6J mice

de Wilde, J., Mohren, R., van den Berg, S., Boekschoten, M., Dijk, K. W.-V., de Groot, P., Müller, M., Mariman, E., & Smit, E. (2008) · Physiological Genomics · doi:10.1152/physiolgenomics.00219.2007 · Cited by 132

Multi‐omic rejuvenation of naturally aged tissues by a single cycle of transient reprogramming

Chondronasiou, D., Gill, D., Mosteiro, L., Urdinguio, R. G., Berenguer‐Llergo, A., Aguilera, M., Durand, S., Aprahamian, F., Nirmalathasan, N., Abad, M., et al. (2022) · Aging Cell · doi:10.1111/acel.13578 · Cited by 133

Drug databases and their contributions to drug repurposing

Masoudi-Sobhanzadeh, Y., Omidi, Y., Amanlou, M., & Masoudi-Nejad, A. (2020) · Genomics · doi:10.1016/j.ygeno.2019.06.021 · Cited by 131

Bottom-up Metabolic Reconstruction of Arabidopsis and Its Application to Determining the Metabolic Costs of Enzyme Production

Arnold, A., & Nikoloski, Z. (2014) · Plant Physiology · doi:10.1104/pp.114.235358 · Cited by 131

Machine Learning in Pharmacometrics: Opportunities and Challenges

McComb, M., Bies, R., & Ramanathan, M. (2022) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14801 · Cited by 128

Genolevures: protein families and synteny among complete hemiascomycetous yeast proteomes and genomes.

Sherman, D. J., Martin, T., Nikolski, M., Cayla, C., Souciet, J.-L., & Durrens, P. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn859 · Cited by 129

Skeletal muscle gene expression in response to resistance exercise: sex specific regulation

Liu, D., Sartor, M. A., Nader, G. A., Gutmann, L., Treutelaar, M. K., Pistilli, E. E., IglayReger, H. B., Burant, C. F., Hoffman, E. P., & Gordon, P. M. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-659 · Cited by 122

A missing link between complex I and group 4 membrane-bound [NiFe] hydrogenases

Marreiros, B. C., Batista, A. P., Duarte, A. M. S., & Pereira, M. M. (2013) · Biochimica et Biophysica Acta (BBA) - Bioenergetics · doi:10.1016/j.bbabio.2012.09.012 · Cited by 120

Mining microbes for mental health: determining the role of microbial metabolic pathways in human brain health and disease

Spichak, S., Bastiaanssen, T. F. S., Berding, K., Vlckova, K., Clarke, G., Dinan, T. G., & Cryan, J. F. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.02.044 · Cited by 127

Deep proteome analysis identifies age-related processes in C. elegans

Narayan, V., Ly, T., Pourkarimi, E., Murillo, A. B., Gartner, A., Lamond, A. I., & Kenyon, C. (2016) · Cell Systems · doi:10.1016/j.cels.2016.06.011 · Cited by 127

The alteration of gut microbiome and metabolism in amyotrophic lateral sclerosis patients

Zeng, Q., Shen, J., Chen, K., Zhou, J., Liao, Q., Lu, K., Yuan, J., & Bi, F. (2020) · Scientific Reports · doi:10.1038/s41598-020-69845-8 · Cited by 127

Colonic Transit Time Is a Driven Force of the Gut Microbiota Composition and Metabolism: In Vitro Evidence

Tottey, W., Feria-Gervasio, D., Gaci, N., Laillet, B., Pujos, E., Martin, J.-F., Sebedio, J.-L., Sion, B., Jarrige, J.-F., Alric, M., et al. (2017) · Journal of Neurogastroenterology and Motility · doi:10.5056/jnm16042 · Cited by 125

cPath: open source software for collecting, storing, and querying biological pathways

Cerami, E. G., Bader, G. D., Gross, B. E., & Sander, C. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-497 · Cited by 125

Orally administered Odoribacter laneus improves glucose control and inflammatory profile in obese mice by depleting circulating succinate

Huber-Ruano, I., Calvo, E., Mayneris-Perxachs, J., Rodríguez-Peña, M.-M., Ceperuelo-Mallafré, V., Cedó, L., Núñez-Roa, C., Miro-Blanch, J., Arnoriaga-Rodríguez, M., Balvay, A., et al. (2022) · Microbiome · doi:10.1186/s40168-022-01306-y · Cited by 122

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

Genomic factors shape carbon and nitrogen metabolic niche breadth across Saccharomycotina yeasts

Opulente, D. A., LaBella, A. L., Harrison, M.-C., Wolters, J. F., Liu, C., Li, Y., Kominek, J., Steenwyk, J. L., Stoneman, H. R., VanDenAvond, J., et al. (2024) · Science · doi:10.1126/science.adj4503 · Cited by 121

Genome-wide association study of severity in multiple sclerosis

International Multiple Sclerosis Genetics Consortium, Briggs, F. B. S., Shao, X., Goldstein, B. A., Oksenberg, J. R., Barcellos, L. F., & De Jager, P. L. (2011) · Genes & Immunity · doi:10.1038/gene.2011.34 · Cited by 120

A central metabolic circuit controlled by QseC in pathogenic Escherichia coli

Hadjifrangiskou, M., Kostakioti, M., Chen, S. L., Henderson, J. P., Greene, S. E., & Hultgren, S. J. (2011) · Molecular Microbiology · doi:10.1111/j.1365-2958.2011.07660.x · Cited by 118

Gene expression changes reflect clinical response in a placebo-controlled randomized trial of abatacept in patients with diffuse cutaneous systemic sclerosis

Chakravarty, E. F., Martyanov, V., Fiorentino, D., Wood, T. A., Haddon, D. J., Jarrell, J. A., Utz, P. J., Genovese, M. C., Whitfield, M. L., & Chung, L. (2015) · Arthritis Research & Therapy · doi:10.1186/s13075-015-0669-3 · Cited by 117

Dyslipidemia, inflammation, calcification, and adiposity in aortic stenosis: a genome-wide study

Yu Chen, H., Dina, C., Small, A. M., Shaffer, C. M., Levinson, R. T., Helgadóttir, A., Capoulade, R., Munter, H. M., Martinsson, A., Cairns, B. J., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad142 · Cited by 116

Analysis of 1321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations

Karcher, N., Pasolli, E., Asnicar, F., Huang, K. D., Tett, A., Manara, S., Armanini, F., Bain, D., Duncan, S. H., Louis, P., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-02042-y · Cited by 116

Reference genome of wild goat (Capra aegagrus) and sequencing of goat breeds provide insight into genic basis of goat domestication

Dong, Y., Zhang, X., Xie, M., Arefnezhad, B., Wang, Z., Wang, W., Feng, S., Huang, G., Guan, R., Shen, W., et al. (2015) · BMC Genomics · doi:10.1186/s12864-015-1606-1 · Cited by 116

Temporal analyses of postnatal liver development and maturation by single-cell transcriptomics

Liang, Y., Kaneko, K., Xin, B., Lee, J., Sun, X., Zhang, K., & Feng, G.-S. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.01.004 · Cited by 116

A spatial vascular transcriptomic, proteomic, and phosphoproteomic atlas unveils an angiocrine Tie-Wnt signaling axis in the liver

Inverso, D., Shi, J., Lee, K. H., Jakab, M., Ben-Moshe, S., Kulkarni, S. R., Schneider, M., Wang, G., Komeili, M., Vélez, P. A., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.05.001 · Cited by 114

Toolbox model of evolution of prokaryotic metabolic networks and their regulation

Maslov, S., Krishna, S., Pang, T. Y., & Sneppen, K. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903206106 · Cited by 110

Large Scale Foundation Model on Single-cell Transcriptomics

Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Song, L., & Zhang, X. (2023) · doi:10.1101/2023.05.29.542705 · Cited by 639

Role of the Astroglial Glutamate Exchanger xCT in Ventral Hippocampus in Resilience to Stress

Nasca, C., Bigio, B., Zelli, D., de Angelis, P., Lau, T., Okamoto, M., Soya, H., Ni, J., Brichta, L., Greengard, P., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.09.020 · Cited by 113

Genetic variability in a frozen batch of MCF-7 cells invisible in routine authentication affecting cell function

Kleensang, A., Vantangoli, M. M., Odwin-DaCosta, S., Andersen, M. E., Boekelheide, K., Bouhifd, M., Fornace, A. J., Li, H.-H., Livi, C. B., Madnick, S., et al. (2016) · Scientific Reports · doi:10.1038/srep28994 · Cited by 114

Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis

Blaser, M. C., Kraler, S., Lüscher, T. F., & Aikawa, E. (2021) · Circulation Research · doi:10.1161/circresaha.120.317979 · Cited by 109

Autoreactive CD8+ T cells are restrained by an exhaustion-like program that is maintained by LAG3

Grebinoski, S., Zhang, Q., Cillo, A. R., Manne, S., Xiao, H., Brunazzi, E. A., Tabib, T., Cardello, C., Lian, C. G., Murphy, G. F., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01210-5 · Cited by 107

Metabolic independence drives gut microbial colonization and resilience in health and disease

Watson, A. R., Füssel, J., Veseli, I., DeLongchamp, J. Z., Silva, M., Trigodet, F., Lolans, K., Shaiber, A., Fogarty, E., Runde, J. M., et al. (2023) · Genome Biology · doi:10.1186/s13059-023-02924-x · Cited by 92

Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging

Lehallier, B., Shokhirev, M. N., Wyss‐Coray, T., & Johnson, A. A. (2020) · Aging Cell · doi:10.1111/acel.13256 · Cited by 106

Imaging transcriptomics of brain disorders

Arnatkeviciute, A., Fulcher, B. D., Bellgrove, M. A., & Fornito, A. (2022) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2021.10.002 · Cited by 113

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

Chhabra, D., Sharma, S., Kho, A. T., Gaedigk, R., Vyhlidal, C. A., Leeder, J. S., Morrow, J., Carey, V. J., Weiss, S. T., Tantisira, K. G., et al. (2014) · Epigenetics · doi:10.4161/15592294.2014.971593 · Cited by 102

Multiomics of Tissue Extracellular Vesicles Identifies Unique Modulators of Atherosclerosis and Calcific Aortic Valve Stenosis

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Neuronal Apolipoprotein E4 Expression Results in Proteome-Wide Alterations and Compromises Bioenergetic Capacity by Disrupting Mitochondrial Function

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