PaperPlayer biorxiv bioinformatics – Détails, épisodes et analyse

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.21.513175v1?rss=1 Authors: Li, H. Abstract: Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by such method. In this work, we carry out the performance evaluation regarding to learning results as well as time consumed, speedup, and efficiency using different types of neural network structures and distributed training pipeline implementations. Besides, the reasons lead to an unideal performance enhancement is investigated. Code availability at https://github.com/ htlee6/perf-analysis-dist-training-gnn. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.18.512676v1?rss=1 Authors: Jain, S., Chouzenoux, E., Kumar, K., Majumdar, A. Abstract: Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a suitable solution. This paper presents a novel Graph Regularized Probabilistic Matrix Factorization (GRPMF) method, which incorporates expert knowledge through a novel graph-based regularization strategy within an MF framework. An efficient and sounded optimization algorithm is proposed to solve the resulting non-convex problem in an alternating fashion. The performance of the proposed method is evaluated through the DrugBank dataset, and comparisons are provided against state-of-the-art techniques. The results demonstrate the superior performance of GRPMF when compared to its counterparts. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.18.512665v1?rss=1 Authors: Narendra, T., Visona, G., Cardona, C. d. J., Schweikert, G. Abstract: Epigenetic mechanisms coordinate packaging, accessibility and read-out of the DNA sequence within the chromatin context. They significantly contribute to the regulation of gene expression. Thus, they play fundamental roles during differentiation on the one hand and maintenance and propagation of cell identity on the other. Epigenetic malfunctioning is associated with a large range of diseases, from neurodevelopmental disorders to cancer progression. In humans, hundreds of known epigenetic factors and complexes are involved in establishing covalent modifications on the DNA sequence itself and on associated histone proteins. Within the cellular context, the resulting combinatorial epigenomic patterns are neither established nor interpreted independently of each other and therefore exhibit high correlations in a region-specific manner. Post-translational modifications of histone proteins can be analysed using Chromatin Immunoprecipitation followed by sequencing (ChIP-Seq). Often, several assays for a number of different histone modifications are performed as part of the same experimental design. These measurements are, however, confounded by shared biases including chromatin accessibility, PCR amplification and mappability. Existing computational methods analyse each histone modification separately, while often also merging biological or technical replicates. We introduce DecoDen, a new approach that leverages replicates and multi-histone ChIP-Seq experiments for a fixed cell type to learn and remove shared biases. DecoDen (Deconvolve and Denoise) consists of two major steps: We use non-negative matrix factorisation (NMF) to learn a joint cell-type specific signal. Half-sibling regression (HSR) is then used to correct for the cell-type specific biases in the histone modification signals. We demonstrate that DecoDen is a robust and interpretable method that enables the unbiased discovery of subtle peaks, which are particularly important in an individual-specific context. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.26.513573v1?rss=1 Authors: BICCN Data Ecosytem Collaboration,, Hawrylycz, M. J., Martone, M. E., Hof, P. R., Lein, E. S., Regev, A., Ascoli, G. A. A., Bjaalie, J. G., Dong, H.-W., Ghosh, S. S., Gillis, J., Hertzano, R., Haynor, D. R., Kim, Y., Liu, Y., Miller, J. A., Mitra, P. P., Mukamel, E., Osumi-Sutherland, D., Peng, H., Ray, P. L., Sanchez, R., Ropelewski, A., Scheuermann, R. H., Tan, S. Z. K., Tickle, T., Tilgner, H., Varghese, M., Wester, B., White, O., Aevermann, B., Allemang, D., Ament, S., Athey, T. L., Baker, P. M., Baker, C., Baker, K. S., Bandrowski, A., Bishwakarma, P., Carr, A., Chen, M., Choudhury, R., Abstract: Characterizing cellular diversity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required to manipulate cell types in controlled ways, and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data generating centers, data archives and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its extensive resources, including the BRAIN Cell Data Center (BCDC) which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.21.529152v1?rss=1 Authors: Lorig-Roach, R., Meredith, M., Monlong, J., Jain, M., Olsen, H., McNulty, B., Porubsky, D., Montague, T. G., Lucas, J., Condon, C., Eizenga, J., Juul, S., McKenzie, S., Simmonds, S., Park, J., Asri, M., Koren, S., Eichler, E., Axel, R., Martin, B., Carnevali, P., Miga, K., Paten, B. Abstract: As a step towards simplifying and reducing the cost of haplotype resolved de novo assembly, we describe new methods for accurately phasing nanopore data with the Shasta genome assembler and a modular tool for extending phasing to the chromosome scale called GFAse. We test using new variants of Oxford Nanopore Technologies' (ONT) PromethION sequencing, including those using proximity ligation and show that newer, higher accuracy ONT reads substantially improve assembly quality. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.22.529500v1?rss=1 Authors: Vastrad, B. M., Vastrad, C. M. Abstract: Coronary artery disease (CAD) is the most common cardiovascular disorder and the leading cause of heart related deaths in world. Increasing molecular targets have been discovered for CAD and CAD - related complications prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might help the diagnosis and treatment of CAD and CAD related complications. We searched next generation sequencing (NGS) dataset (GSE202625) and identified differentially expressed genes (DEGs) by comparing CAD and normal control samples using DESeq2. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g:Profiler online database. The protein protein interaction (PPI) network was plotted with IMEx interactome and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. MiRNA hub gene regulatory network and TF hub gene regulatory network analysis was performed to identify the hub genes, miRNAs and TFs. Receiver operating characteristic (ROC) curve analysis was used to predict the diagnostic effectiveness of the hub genes. A total of 118 DEGs (479 up regulated genes and 479 down regulated genes) were detected. The GO enrichment analysis indicated that the DEGs most significantly enriched in cellular response to stimulus and biosynthetic process. The REACTOME pathway enrichment analysis revealed that the DEGs were most significantly enriched in immune system and eukaryotic translation elongation. PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network analysis demonstrated that EGR1, SIRT1, STAT1, LRRK2, HIF1A, CSNK2B, RPS3, RPS2, RPS4X and HDAC11 were the hub genes. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of CAD and CAD-related complications, and provide potential targets for further research. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.21.529454v1?rss=1 Authors: Qian, C., Xiang, X., Yao, H., Li, P., Cheng, B., Wei, D., An, W., Lu, Y., Chu, M., Wei, L., Asakawa, T., Xu, J., Xia, F., Liu, X., Liu, B.-F. Abstract: Since Major Depressive Disorder (MDD) represents a neurological pathology caused by inter-synaptic messaging errors, membrane receptors, the source of signal cascades, constitute ap-pealing drugs targets. G protein-coupled receptors (GPCRs) and ion channel receptors chelated antidepressants (ADs) high-resolution architectures were reported to realize receptors physical mechanism and design prototype compounds with minimal side effects. Tyrosine kinase recep-tor 2 (TrkB), a receptor that directly modulates synaptic plasticity, has a finite three-dimensional chart due to its high molecular mass and intrinsically disordered regions (IDRs). Leveraging breakthroughs in deep learning, the meticulous architecture of TrkB was projected employing Alphfold 2 (AF2). Furthermore, the Alphafold Multimer algorithm (AF-M) models the coupling of intra- and extra-membrane topologies to chaperones: mBDNF, SHP2, Etc. Conjugating firmly dimeric transmembrane helix with novel compounds like 2R,6R-hydroxynorketamine (2R,6R-HNK) expands scopes of drug screening to encompass all coding sequences throughout ge-nomes. The operational implementation of TrkB kinase-SHP2, PLC{gamma}1, and SHC1 ensembles has paved the path for machine learning in which it can forecast structural transitions in the self-assembly and self-dissociation of molecules during trillions of cellular mechanisms. In silicon, the cornerstone of the alteration will be artificial intelligence (AI), empowering signal networks to operate at the atomic level and picosecond timescales. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.22.529574v1?rss=1 Authors: Nambiar, A., Forsyth, J. M., Liu, S., Maslov, S. Abstract: Despite their lack of a rigid structure, intrinsically disordered regions in proteins play important roles in cellular functions, including mediating protein-protein interactions. Therefore, it is important to computationally annotate disordered regions of proteins with high accuracy. Most popular tools use evolutionary or biophysical features to make predictions of disordered regions. In this study, we present DR-BERT, a compact protein language model that is first pretrained on a large number of unannotated proteins before being trained to predict disordered regions. Although it does not use any evolutionary or biophysical information, DR-BERT shows a statistically significant improvement when compared to several existing methods on a gold standard dataset. We show that this performance is due to the information learned during pre-training and DR-BERT's ability to use contextual information. A web application for using DR-BERT is available at https://huggingface.co/spaces/nambiar4/DR-BERT and the code to run the model can be found at https://github.com/maslov-group/DR-BERT. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.21.527754v1?rss=1 Authors: Xiong, J. Abstract: In this paper, we present a novel pre-trained network medicine model called Selective Remodeling of Protein Networks by Chemicals (SEMO). We divide the global human protein-protein interaction (PPI) network into smaller sub-networks, and quantify the potential effects of chemicals by statistically comparing their target and non-target gene sets. By combining 9607 PPI gene sets with 2658 chemicals, we created a pre-trained pool of SEMOs, which we then used to identify SEMOs related to Covid-19 severity using DNA methylation profiling data from two clinical cohorts. The nutraceutical-derived SEMO features provided an effective model for predicting Covid-19 severity, with an AUC score of 81% in the training data and 80% in the independent validation data. Our findings suggest that Vitamin D3, Lipoic Acid, Citrulline, and Niacin, along with their associated protein networks,particularly STAT1, MMP2, CD8A, and CXCL8 as hub nodes,could be used to effectively predict Covid-19 severity. Furthermore, the severity-associated SEMOs were found to be significantly correlated with CD4+ and monocyte cell proportions. These insights can be used to generate personalized nutraceutical regimes by ranking the relative severity risk associated with each SEMO. Thus, our pre-trained SEMO model can serve as a fundamental knowledge map when coupled with DNA methylation measurements, allowing us to simultaneously generate biomarkers, targets, re-purposing drugs, and nutraceutical interventions. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.22.529510v1?rss=1 Authors: Fourquet, O., Krejca, M. S., Doerr, C., Schwikowski, B. Abstract: Motivation Monotonic bivariate classifiers can describe simple patterns in high-dimensional data that may not be discernible using only elementary linear decision boundaries. Such classifiers are relatively simple, easy to interpret, and do not require large amounts of data to be effective. A challenge is that finding optimal pairs of features from a vast number of possible pairs tends to be computationally intensive, limiting the applicability of these classifiers. Results We prove a simple mathematical inequality and show how it can be exploited for the faster identification of optimal feature combinations. Our empirical results suggest speedups of 10x--20x, relative to the previous, naive, approach in applications. This result thus greatly extends the range of possible applications for bivariate monotonic classifiers. In addition, we provide the first open-source code to identify optimal monotonic bivariate classifiers. Availability: https://gitlab.pasteur.fr/ofourque/mem_python Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC