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Alexander Aliper
Johns Hopkins University
48Publications
20H-index
992Citations
Publications 44
Newest
#1Polina Mamoshina (University of Oxford)H-Index: 7
#2Kirill KochetovH-Index: 3
Last.Alex ZhavoronkovH-Index: 26
view all 11 authors...
There is an association between smoking and cancer, cardiovascular disease and all-cause mortality. However, currently, there are no affordable and informative tests for assessing the effects of smoking on the rate of biological aging. In this study we demonstrate for the first time that smoking status can be predicted using blood biochemistry and cell count results andthe recent advances in artificial intelligence (AI). By employing age-prediction models developed using supervised deep learning...
4 CitationsSource
#1Alexander AliperH-Index: 20
Last.Andreyan N. OsipovH-Index: 13
view all 5 authors...
Source
#1Polina Mamoshina (University of Oxford)H-Index: 7
#2Kirill KochetovH-Index: 3
Last.Alex ZhavoronkovH-Index: 26
view all 5 authors...
Transcriptome profiling has been shown really useful in the understanding of the aging process. To date, transcriptomic data is the second most abundant omics data type following genomics. To deconvolute the relationship between transcriptomic changes and aging one needs to conduct an analysis on the comprehensive dataset. At the same time, biological aging clocks constructed for clinical use needs to robustly predict new data without any further retraining. In this paper, we develop a transcrip...
Source
#1Daniela BakulaH-Index: 2
#2Alexander AliperH-Index: 20
Last.Morten Scheibye-KnudsenH-Index: 25
view all 23 authors...
DB is supported by the German Research Foundation (Forschungsstipendium; BA 6276/1-1). CYE is supported by Swiss National Science Foundation [163898]. VNG is supported by grants from National Institutes of Health, and by the Russian Federation grant 14.W03.31.0012. DWL presented the results of research supported in part by research grants and funds from the National Institutes of Health, the Wisconsin Partnership Program, the Progeria Research Foundation, the American Federation for Aging Resear...
2 CitationsSource
#1Polina Mamoshina (University of Oxford)H-Index: 7
#2Kirill Kochetov (Johns Hopkins University)H-Index: 3
Last.Alex Zhavoronkov (Johns Hopkins University)H-Index: 26
view all 12 authors...
18 CitationsSource
#1Daniil Polykovskiy (HSE: National Research University – Higher School of Economics)H-Index: 3
#2Alexander ZhebrakH-Index: 5
Last.Artur KadurinH-Index: 5
view all 10 authors...
Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular structures within hours, while conventional drug discovery pipelines require months of work. In this article, we propose a new generative architecture, entangled conditional adversarial autoencoder, that generates molecular structures based on various properties, such as activity against a specific protein, solubility, or ease of synthesis. We appl...
18 CitationsSource
Last.Yegor E. YegorovH-Index: 15
view all 8 authors...
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#1Alexander AliperH-Index: 20
#2Leslie C. JellenH-Index: 5
Last.Alexander ZhavoronkovH-Index: 3
view all 8 authors...
18 CitationsSource
#1Nicolas Borisov (Kurchatov Institute)H-Index: 8
#2Maria SuntsovaH-Index: 9
Last.Anton Buzdin (Kurchatov Institute)H-Index: 9
view all 21 authors...
ABSTRACTHigh throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bia...
17 CitationsSource
#1Georgios N. Stamatas (Johnson & Johnson)H-Index: 26
#2Jeff Wu (Johnson & Johnson)H-Index: 2
Last.Alex Zhavoronkov (Johns Hopkins University)H-Index: 26
view all 11 authors...
Androgenetic alopecia is the most common form of hair loss. Minoxidil has been approved for the treatment of hair loss, however its mechanism of action is still not fully clarified. In this study, we aimed to elucidate the effects of 5% minoxidil topical foam on gene expression and activation of signaling pathways in vertex and frontal scalp of men with androgenetic alopecia. We identified regional variations in gene expression and perturbed signaling pathways using in silico Pathway Activation ...
1 CitationsSource
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