Systems Biology of Aging external website

1. Ursu E, Minnegalieva A, Rawat P, Chernigovskaya M, Tacutu R, Sandve GK, Robert PA, Greiff VUrsu E et al . "Training data composition determines machine learning generalization and biological rule discovery", Nature Machine Intelligence 7(8): 1206-1219, (2025)
   doi: 10.1101/2024.06.17.599333
   IF: 23.90AI: 9.87
2. Lagger C, Ursu E, Equey A, Avelar RA, Pisco AO, Tacutu R, de Magalhães JPLagger C et al . "scDiffCom: a tool for differential analysis of cell-cell interactions provides a mouse atlas of aging changes in intercellular communication", Nature aging 3(11): 1446-1461, (2023)
   doi: 10.1038/s43587-023-00514-x
   IF: 8.48
3. Ghenea S, Chiritoiu M, Tacutu R, Miranda-Vizuete A, Petrescu SMGhenea S et al . "Targeting EDEM protects against ER stress and improves development and survival in C. elegans", PLoS genetics 18(2): e1010069, (2022)
   IF: 5.90AI: 2.47
4. Knyazer A, Bunu G, Toren D, Mracica TB, Segev Y, Wolfson M, Muradian KK, Tacutu R, Fraifeld VEKnyazer A et al . "Small molecules for cell reprogramming: a systems biology analysis", Aging 13(24): 25739-25762, (2021)
   IF: 5.68AI: 1.16
5. Matei IV, Samukange VNC, Bunu G, Toren D, Ghenea S, Tacutu RMatei IV et al . "Knock-down of odr-3 and ife-2 additively extends lifespan and healthspan in C. elegans", Aging 13(17): 21040-21065, (2021)
   IF: 5.60AI: 1.16
6. Constantinescu V, Chiru C, Boloni T, Florea A, Tacutu RConstantinescu V et al . "Learning flat representations with artificial neural networks", Applied Intelligence(51): 2456–2470, (2021)
   IF: 5.09AI: 0.69
7. Kulaga AY, Ursu E, Toren D, Tyshchenko V, Guinea R, Pushkova M, Fraifeld VE, Tacutu RKulaga AY et al . "Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals", International journal of molecular sciences 22(3): 1073, (2021)
   IF: 4.56AI: 1.06
8. Toren D, Yanai H, Abu Taha R, Bunu G, Ursu E, Ziesche R, Tacutu R, Fraifeld VEToren D et al . "Systems biology analysis of lung fibrosis-related genes in the bleomycin mouse model", Scientific reports 11(1): 19269, (2021)
   IF: 4.38AI: 1.21
9. Avelar RA, Ortega JG, Tacutu R, Tyler EJ, Bennett D, Binetti P, Budovsky A, Chatsirisupachai K, Johnson E, Murray A, Shields S, Tejada-Martinez D, Thornton D, Fraifeld VE, Bishop CL, de Magalhães JPAvelar RA et al . "A multidimensional systems biology analysis of cellular senescence in aging and disease", Genome biology 21(1): 91, (2020)
   IF: 10.81AI: 9.03
10. Toren D, Kulaga A, Jethva M, Rubin E, Snezhkina AV, Kudryavtseva AV, Nowicki D, Tacutu R, Moskalev AA, Fraifeld VEToren D et al . "Gray whale transcriptome reveals longevity adaptations associated with DNA repair and ubiquitination", Aging cell 19(7): e13158, (2020)
    IF: 7.24AI: 2.61
11. Bunu G, Toren D, Ion CF, Barardo D, Sârghie L, Grigore LG, de Magalhães JP, Fraifeld VE, Tacutu RBunu G et al . "SynergyAge, a curated database for synergistic and antagonistic interactions of longevity-associated genes", Scientific data 7(1): 366, (2020)
    IF: 5.54AI: 3.25
12. Bucaciuc Mracica T, Anghel A, Ion CF, Moraru CV, Tacutu R, Lazar GABucaciuc Mracica T et al . "MetaboAge DB: a repository of known ageing-related changes in the human metabolome", Biogerontology 21(6): 763-771, (2020)
    IF: 3.77AI: 1.13
13. Martin EC, Sukarta OCA, Spiridon L, Grigore LG, Constantinescu V, Tacutu R, Goverse A, Petrescu AJMartin EC et al . "LRRpredictor-A New LRR Motif Detection Method for Irregular Motifs of Plant NLR Proteins Using an Ensemble of Classifiers", Genes (Basel) 11(3): 286, (2020)
    IF: 3.33
14. Tacutu R, Thornton D, Johnson E, Budovsky A, Barardo D, Craig T, Diana E, Lehmann G, Toren D, Wang J, Fraifeld VE, de Magalhães JPTacutu R et al . "Human Ageing Genomic Resources: new and updated databases", Nucleic acids research 46(D1): D1083-D1090, (2018)
    IF: 11.15AI: 4.48
15. Yanai H, Budovsky A, Barzilay T, Tacutu R, Fraifeld VEYanai H et al . "Wide-scale comparative analysis of longevity genes and interventions", Aging cell 16(6): 1267-1275, (2017)
    IF: 7.63AI: 2.30
16. de Magalhães JP, Lagger C, Tacutu Rde Magalhães JP et al . "Integrative Genomics of Aging", pp 151-171, Handbook of the Biology of Aging, Academic Press, Elsevier, (2021).
    ISBN: 978-0-12-815962-0
17. Tacutu R, Toren D, Ursu E, Bunu G, Bucaciuc Mracica TTacutu R et al . "Healthy Biological Systems", pp 53-78, Explaining Health Across the Sciences, Springer, Cham, (2020).
    ISBN: 978-3-030-52662-7

Databases developed and maintained by our group:

1. SynergyAge DB - a database hosting high-quality, manually curated information about the synergistic and antagonistic lifespan effects of genetic interventions in model organisms, also allowing users to explore the longevity relationships between genes in a visual way.

2. Metaboage DB - a free, open-source, manually curated human aging-related metabolome database, providing intuitive tools for the visualization, interpretation, and analysis of pathway knowledge to support basic research and improve the knowledge regarding the aging process.

3. MitoAge DB - a curated, publicly available database, containing an extensive collection of calculated mtDNA data records, integrated with longevity records.

Computational projects (software) developed by members of our group: 

1. Just-DNA-Seq Project – A set of open-source libraries and pipelines designed to generate an all-encompassing, open-source report, which provides comprehensive insights into an individual's health and longevity and encompasses polygenic health risk predictions, longevity related recommendations, and critical health risk variations

Technologies: python poalrs, OakVar,  Deepvariant, WDL, bwa-mem2, DVC, pytorch, Docker, Singularity 

2. Longevity Genie Project – An open-source project devoted to utilizing the power of large language models for aging research and personalized health data.

Technologies: Large Language Models (ChatGPT API, Hugging face models),  LangChain, Unstructured, DVC