Biology subjects

Putin, E.

Publications and source records attributed to Putin, E..

3 recordsLinked to original sources

bioRxiv2019

Testing for batch effect through age predictors

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 transcriptomic deep-learned age predictor. Deep neural networks (DNN) are trained and tested on more than 6 000 blood gene expression samples from 17 datasets. We apply methods based on output derivatives of DNN to rank input genes by their importance in age prediction and reduce the dimensional of the data. We also show that batch effect in transcriptome datasets of healthy humans is indeed significant, but the existing normalization techniques, while removing technical variation quite effectively, also remove age-associated changes. So robust methods of age prediction are needed.

bioinformatics↗

bioRxiv2018

Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects

Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/507780_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1ad6feaorg.highwire.dtl.DTLVardef@18e72a9org.highwire.dtl.DTLVardef@109f18aorg.highwire.dtl.DTLVardef@1d0c9b7_HPS_FORMAT_FIGEXP M_FIG C_FIG AbstractThe human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous analyses of gut microflora revealed associations between specific microbes and host health and disease status, genotype and diet. Here, we developed a method of predicting biological age of the host based on the microbiological profiles of gut microbiota using a curated dataset of 1,165 healthy individuals (3,663 microbiome samples). Our predictive model, a human microbiome clock, has an architecture of a deep neural network and achieves the accuracy of 3.94 years mean absolute error in cross-validation. The performance of the deep microbiome clock was also evaluated on several additional populations. We further introduce a platform for biological interpretation of individual microbial features used in age models, which relies on permutation feature importance and accumulated local effects. This approach has allowed us to define two lists of 95 intestinal biomarkers of human aging. We further show that this list can be reduced to 39 taxa that convey the most information on their hosts aging. Overall, we show that (a) microbiological profiles can be used to predict human age; and (b) microbial features selected by models are age-related.

bioinformatics↗

bioRxiv2016

Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes

Despite many recent advances in systems biology and a marked increase in the availability of high-throughput biological data, the productivity of research and development in the pharmaceutical industry is on the decline. This is primarily due to clinical trial failure rates reaching up to 95% in oncology and other disease areas. We have developed a comprehensive analytical and computational pipeline utilizing deep learning techniques and novel systems biology analytical tools to predict the outcomes of phase I/II clinical trials. The pipeline predicts the side effects of a drug using deep neural networks and estimates drug-induced pathway activation. It then uses the predicted side effect probabilities and pathway activation scores as an input to train a classifier which predicts clinical trial outcomes. This classifier was trained on 577 transcriptomic datasets and has achieved a cross-validated accuracy of 0.83. When compared to a direct gene-based classifier, our multi-stage approach dramatically improves the accuracy of the predictions. The classifier was applied to a set of compounds currently present in the pipelines of several major pharmaceutical companies to highlight potential risks in their portfolios and estimate the fraction of clinical trials that were likely to fail in phase I and II.

bioinformatics↗