Publications

Every gene can (and possibly will) be associated with cancer

A PubMed analysis shows that the vast majority of human genes have been studied in the context of cancer. As such, the study of nearly any human gene can be justified based on existing literature by its potential relevance to cancer. Moreover, these results have implications for analyzing and interpreting large-scale analyses.

Cellular reprogramming and the rise of rejuvenation biotech

Cells can be rejuvenated and biological clocks reset using cellular reprogramming. A growing number of companies now aim to use cellular reprogramming to develop therapies for rejuvenating human beings. Can the 'young' science of rejuvenation, currently mostly based on in vitro studies, drive a new biotech field toward clinical applications?

Machine learning-based predictions of dietary restriction associations across ageing-related genes

This work used a Machine Learning (ML) approach to classify ageing-related genes as DR-related or NotDR-related using 9 different types of predictive features: PathDIP pathways, two types of features based on KEGG pathways, two types of Protein-Protein Interactions (PPI) features, Gene Ontology (GO) terms, Genotype Tissue Expression (GTEx) expression features, GeneFriends co-expression features and protein sequence descriptors. 

Sex-specific aging in animals: Perspective and future directions

Sex differences in aging occur in many animal species, and they include sex differences in lifespan, in the onset and progression of age-associated decline, and in physiological and molecular markers of aging. Sex differences in aging vary greatly across the animal kingdom.  Here, we take a comparative, interdisciplinary approach to explore various hypotheses about how sex differences in aging arise. We discuss genomic, morphological, and environmental differences between the sexes and how these relate to sex differences in aging. Finally, we present some suggestions for future research in this area and provide recommendations for promising experimental designs.

m5C-Atlas: a comprehensive database for decoding and annotating the 5-methylcytosine (m5C) epitranscriptome

5-Methylcytosine (m5C) is one of the most prevalent covalent modifications on RNA. It is known to regulate a broad variety of RNA functions, including nuclear export, RNA stability and translation. Here, we present m5C-Atlas, a database for comprehensive collection and annotation of RNA 5-methylcytosine. The database contains 166 540 m5C sites in 13 species identified from 5 base-resolution epitranscriptome profiling technologies. 

Targeting aging mechanisms: pharmacological perspectives

This review aims to classify potential geroprotectors based on the mechanisms by which they influence aging. Geroprotectors slow down aging and promote healthy longevity in model animals. Although hundreds of compounds have been shown to extend the life of laboratory model organisms, clinical studies on potential geroprotectors are exceedingly rare, especially in healthy elders. 

Simple Detection Methods for Senescent Cells: Opportunities and Challenges

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This article presents a review of the cellular senescence, the irreversible growth arrest of cells from conditional renewal populations combined with a radical shift in their phenotype, is a hallmark of ageing in some mammalian species. In the light of this, interest in the detection of senescent cells in different tissues and different species is increasing.

Geroprotective and senoremediative strategies to reduce the comorbidity, infection rates, severity, and lethality in gerophilic and gerolavic infections

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This article presents a review of the limited literature available on geroprotective and senoremediative interventions that may be investigated to decrease the disease burden of gerolavic infections. This article also highlights a need for rigorous clinical validation of deep aging clocks as surrogate markers of biological age. These could be used to assess the need for, and efficacy of, geroprotective and senoremediative interventions and provide better protection for elderly populations from gerolavic infections.

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Insilico Medicine have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days.

Towards natural mimetics of metformin and rapamycin

Aging is now at the forefront of major challenges faced globally, creating an immediate need for safe, widescale interventions to reduce the burden of chronic disease and extend human healthspan.

Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare

The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics.

Induced Cell Turnover: A Novel Therapeutic Modality for In Situ Tissue Regeneration

Written and published by Biogerontology Research Foundation staff in collaboration with Northwestern University Feinberg School of Medicine, the Neurobiology Department of Swammerdam Institute of Life Sciences at the University of Amsterdam and the Department of Biomedical and Molecular Sciences of Queen's University School of Medicine, Queen's University.

druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties.

The DrugAge database of aging-related drugs

Aging is a major worldwide medical challenge. Not surprisingly, identifying drugs and compounds that extend lifespan in model organisms is a growing research area.

Here, we present DrugAge (http://genomics.senescence.info/drugs/), a curated database of lifespan-extending drugs and compounds.

In silico analysis of pathways activation landscape in oral squamous cell carcinoma and oral leukoplakia

A subset of patients with oral squamous cell carcinoma (OSCC), the most common subtype of head and neck squamous cell carcinoma (HNSCC), harbor dysplastic lesions (often visually identified as leukoplakia) prior to cancer diagnosis.

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input.

Design of efficient computational workflows for in silico drug repurposing. Drug Discov Today.

Written and published by Biogerontology Research Foundation staff in collaboration with BioTime Inc., Insilico Medicine Inc. and Johns Hopkins University. Authors: Vanhaelen Q, Mamoshina P, Aliper AM, Artemov A, Lezhnina K, Ozerov I, Labat I, Zhavoronkov A.

In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state

Written and published by Biogerontology Research Foundation staff in collaboration with Insilico Medicine Inc., Pathway Pharmaceuticals, Ltd., Center for Biogerontology and Regenerative Medicine, Moscow Institute of Physics and Technology, University of Tennessee Health Science Center (Department of Genetics, Genomics, and Informatics), D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology, and Immunology, Life Extension, George Mason University (School of Systems Biology), and the Engelhardt Institute of Molecular Biology of Russian Academy of Sciences.

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. Mol Pharm.

Written and published by Biogerontology Research Foundation staff in collaboration with Insilico Medicine Inc., The Mind Research Network and Datalytic Solutions. Authors: Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A.

Common pathway signature in lung and liver fibrosis. Cell Cycle.

Written and published by Biogerontology Research Foundation staff in collaboration with Wake Forest Institute for Regenerative Medicine (Wake Forest School of Medicine), Atlas Regeneration Inc. and Johns Hopkins University. Authors: Makarev E, Izumchenko E, Aihara F, Wysocki PT, Zhu Q, Buzdin A, Sidransky D, Zhavoronkov A, Atala A.

Pro-fibrotic pathway activation in trabecular meshwork and lamina cribrosa is the main driving force of glaucoma. Cell Cycle.

Written and published by Biogerontology Research Foundation staff in collaboration with Biotime, Inc., Johns Hopkins University, Howard University (Epigenetics Laboratory), Retrope, Inc., Vision Genomics, Inc., and Insilico Medicine, Inc. Authors: Zhavoronkov A, Kanherkar RR, Izumchenko E, Teka M, Cantor C, Manaye K, Sidransky D, West MD, Makarev E, Csoka AB.

Developing criteria for evaluation of geroprotectors as a key stage toward translation to the clinic

Written and published by Biogerontology Research Foundation staff in collaboration with the Buck Institute for Research on Aging, Engelhardt Institute of Molecular Biology of Russian Academy of Sciences, Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences, Moscow Institute of Physics and Technology, Rogachev FRC Center for Pediatric Hematology, Oncology and Immunology and 5Institute for Systems Analysis, Russian Academy of Sciences. Authors: Moskalev A, Chernyagina E, Tsvetkov V, Fedintsev A, Shaposhnikov M, Krut'ko V, Zhavoronkov A, Kennedy BK.

Deep biomarkers of human aging: Application of deep neural networks to biomarker development

Written and published by Biogerontology Research Foundation staff in collaboration with Insilico Medicine Inc., Pathway Pharmaceuticals, Ltd., Center for Biogerontology and Regenerative Medicine, Moscow Institute of Physics and Technology, University of Tennessee Health Science Center (Department of Genetics, Genomics, and Informatics), D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology, and Immunology, Life Extension, George Mason University (School of Systems Biology), and the Engelhardt Institute of Molecular Biology of Russian Academy of Sciences.

Editorial: Should We Treat Aging as a Disease? Academic, Pharmaceutical, Healthcare Policy, and Pension Fund Perspectives

Written and published by Biogerontology Research Foundation staff in collaboration with Insilico Medicine Inc. Authors: Zhavoronkov A, Moskalev A.

Aging Chart: a community resource for rapid exploratory pathway analysis of age-related processes

Written and published by Biogerontology Research Foundation staff in collaboration with Moscow Institute of Physics and Technology (Laboratory of Genetics of Aging and Longevity), Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences (Laboratory of Molecular Radiobiology and Gerontology), Estonia Infinity Sciences, Inc., University of Tennessee Health Science Center (Department of Genetics, Genomics, and Informatics), and D.Rogachev FRC Center for Pediatric Hematology, Oncology and Immunology. Authors: Moskalev A, Zhikrivetskaya S, Shaposhnikov M, Dobrovolskaya E, Gurinovich R, Kuryan O, Pashuk A, Jellen LC, Aliper A, Peregudov A, Zhavoronkov A.

Interactome analysis of myeloid-derived suppressor cells in murine models of colon and breast cancer

Written and published by Biogerontology Research Foundation staff in collaboration with HiBiotechnology LLC and Federal Clinical Research Center of Pediatric Hematology, Oncology and Immunology. Authors: Aliper AM, Frieden-Korovkina VP, Buzdin A, Roumiantsev SA, Zhavoronkov A.

Classifying aging as a disease in the context of ICD-11. Front Genet.

Written and published by Biogerontology Research Foundation staff in collaboration with Novartis Pharma AG, Department of Developmental and Molecular Pathways, Novartis Institute for Biomedical Research. Authors: Zhavoronkov A, Bhullar B.

Activation of homologous recombination DNA repair in human skin fibroblasts continuously exposed to X-ray radiation. Oncotarget.

Written and published by Biogerontology Research Foundation staff in collaboration with Canadian Nuclear Laboratories, Insilico Medicine Inc., Emanuel Institute for Biochemical Physics (Russian Academy of Sciences), Moscow Institute of Physics and Technology, Dmitry Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Semenov Institute of Chemical Physics (Russian Academy of Sciences) and State Research Center - Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency (SRC-FMBC). Authors: Osipov AN, Grekhova A, Pustovalova M, Ozerov IV, Eremin P, Vorobyeva N, Lazareva N, Pulin A, Zhavoronkov A, Roumiantsev S, Klokov D, Eremin I.

Quantifying signaling pathway activation to monitor the quality of induced pluripotent stem cells. Oncotarget.

Written and published by Biogerontology Research Foundation staff in collaboration with Stanford University Medical Center (Department of Developmental Biology), Ludwig Maximilian University of Munich (Department of Computational Genomics), Ruhr University Bochum (Department of Neurophysiology), Atlas Regeneration, Inc,., and Insilico Medicine, Inc. Authors: Makarev E, Fortney K, Litovchenko M, Braunewell KH, Zhavoronkov A, Atala A.

Geroprotectors.org: a new, structured and curated database of current therapeutic interventions in aging and age-related disease. Aging (Albany NY).

Written and published by Biogerontology Research Foundation staff in collaboration with the Buck Institute for Research on Aging, George Mason University (School of Systems Biology), Moscow Institute of Physics and Technology, Engelhardt Institute of Molecular Biology of Russian Academy of Sciences (Laboratory of Postgenomic Studies), Institute of Integrative Biology (Integrative Genomics of Ageing Group). D. Rogachev Center for Pediatric Hematology, Oncology and Immunology, The Judea Regional R&D Center, Pirogov Russian National Research Medical University (The Research Institute for Translational Medicine), Insilico Medicine, Inc., Xspansa, University of Tennessee Health Science Center (Institute of Physiologically Active Compounds of Russian Academy of Sciences, Genetics, Genomics, and Informatics) and Infinity Sciences, Inc. Authors: Moskalev A, Chernyagina E, de Magalhães JP, Barardo D, Thoppil H, Shaposhnikov M, Budovsky A, Fraifeld VE, Garazha A, Tsvetkov V, Bronovitsky E, Bogomolov V, Scerbacov A, Kuryan O, Gurinovich R, Jellen LC, Kennedy B, Mamoshina P, Dobrovolskaya E, Aliper A, Kaminsky D, Zhavoronkov A.

It is time to classify biological aging as a disease. Front Genet.

Written and published by Biogerontology Research Foundation staff in collaboration with Oxford University (Biochemistry Department), Ghent University and Heales vzw. Authors: Bulterijs S, Hull RS, Björk VC, Roy AG.

Screening and personalizing nootropic drugs and cognitive modulator regimens in silico. Front Syst Neurosci.

Written and published by Biogerontology Research Foundation staff in collaboration with University of Tennessee Health Science Center (Department of Genetics, Genomics, and Informatics), Insilico Medicine, Inc. and Pathway Pharmaceuticals Ltd. Authors: Jellen LC, Aliper A, Buzdin A, Zhavoronkov A.

Longevity expectations in the pension fund, insurance, and employee benefits industries. Psychol Res Behav Manag.

Written and published by Biogerontology Research Foundation staff. Authors: Zhavoronkov A.

Signaling pathway activation drift during aging: Hutchinson-Gilford Progeria Syndrome fibroblasts are comparable to normal middle-age and old-age cells

Written and published by Biogerontology Research Foundation staff in collaboration with the Polish Academy of Sciences (Institute of Fundamental Technological Research), Johns Hopkins University, Insilico Medicine, Inc., Howard University (Epigenetics Laboratory), Pirogov Russian National Research Medical University, Pathway Pharmaceuticals Ltd., Moscow Institute of Physics and Technology, and George Mason University. Authors: Aliper AM, Csoka AB, Buzdin A, Jetka T, Roumiantsev S, Moskalev A, Zhavoronkov A.

Pathway activation profiling reveals new insights into age-related macular degeneration and provides avenues for therapeutic interventions

Written and published by Biogerontology Research Foundation staff in collaboration with Insilico Medicine, Inc., Boston University and Vision Genomics LLC. Authors: Makarev E, Cantor C, Zhavoronkov A, Buzdin A, Aliper A, Csoka AB.

Genetics and epigenetics of aging and longevity. Cell Cycle.

Written and published by Biogerontology Research Foundation staff in collaboration with University of California at Davis (Department of Ophthalmology and Vision Science; School of Medicine), Moscow Institute of Physics and Technology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Komi Science Center of Russian Academy of Sciences (Institute of Biology) and Federal Clinical Research Center of Pediatric Hematology, Oncology, and Immunology. Authors: Moskalev AA, Aliper AM, Smit-McBride Z, Buzdin A, Zhavoronkov A.

Signaling pathway cloud regulation for in silico screening and ranking of the potential geroprotective drugs. Front Genet.

Written and published by Biogerontology Research Foundation staff in collaboration with Moscow Institute of Physics and Technology (Department of Biological and Medical Physics). Authors: Zhavoronkov A, Buzdin AA, Garazha AV, Borisov NM, Moskalev AA.

On multilabel classification methods of incompletely labeled biomedical text data. Comput Math Methods Med.

Written and published by Biogerontology Research Foundation staff in collaboration with Moscow Institute of Physics and Technology (Center for Pediatric Hematology, Oncology, and Immunology) and Moscow State University (Chemistry Department). Authors: Kolesov A, Kamyshenkov D, Litovchenko M, Smekalova E, Golovizin A, Zhavoronkov A.

Biomedical progress rates as new parameters for models of economic growth in developed countries. Int J Environ Res Public Health.

Written and published by Biogerontology Research Foundation staff. Authors: Zhavoronkov A, Litovchenko M.

From personalized medicine to personalized science: uniting science and medicine for patient-driven, goal-oriented research.

Written and published by Biogerontology Research Foundation staff. Authors: Zhavoronkov A, Cantor CR.