Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of COX7A1 in embryonic and cancer cells

Abstract

Here we present the application of deep neural network (DNN) ensembles trained on transcriptomic data to identify the novel markers associated with the mammalian embryonic-fetal transition (EFT). Molecular markers of this process could provide important insights into regulatory mechanisms of normal development, epimorphic tissue regeneration and cancer. Subsequent analysis of the most significant genes behind the DNNs classifier on an independent dataset of adult-derived and human embryonic stem cell (hESC)-derived progenitor cell lines led to the identification of COX7A1 gene as a potential EFT marker. COX7A1, encoding a cytochrome C oxidase subunit, was up-regulated in post-EFT murine and human cells including adult stem cells, but was not expressed in pre-EFT pluripotent embryonic stem cells or their in vitro-derived progeny. COX7A1 expression level was observed to be undetectable or low in multiple sarcoma and carcinoma cell lines as compared to normal controls. The knockout of the gene in mice led to a marked glycolytic shift reminiscent of the Warburg effect that occurs in cancer cells. The DNN approach facilitated the elucidation of a potentially new biomarker of cancer and pre-EFT cells, the embryo-onco phenotype, which may potentially be used as a target for controlling the embryonic-fetal transition.

Vive la radiorésistance!: converging research in radiobiology and biogerontology to enhance human radioresistance for deep space exploration and colonization

“…In recent years much progress has been made in the applications of artificial intelligence and specifically deep learning to biomarker development and drug discovery [ 284 ]. Deep neural networks (DNNs) were applied to profiling of the biological samples [ 285 ] predict the age of a patient using the basic clinical blood tests and identify the most important features [ 286 ]. Similar concepts can be applied to other data types including transcriptomic, proteomic, imaging, photographic, activity and physiological data to evaluate the minute changes transpiring during aging or due to the irradiation in space.…”

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

“…The renaissance of deep learning that started in 2015 resulted in unprecedented machine learning performance in image, voice, and text recognition, as well as a range of biomedical applications 29 such as drug repurposing 30 and target identification 31 . One of the most impactful applications of DL in biomedicine was in the applications of generative models to de novo molecular design [32][33][34][35][36] .…”

Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders

“…Machine learning has numerous applications in biomedicine and drug discovery (Gawehn et al, 2016;Mamoshina et al, 2016;Ching et al, 2018). Deep neural networks demonstrated positive results in various tasks, such as prediction of biological age (Putin et al, 2016;Mamoshina et al, 2018a;Mamoshina et al, 2019), prediction of targets and side effects Aliper et al, 2017;Mamoshina et al, 2018b;West et al, 2018), and applications in medicinal chemistry (Lusci et al, 2013;Ma et al, 2015).…”