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      Integration of artificial intelligence and multi-omics in kidney diseases

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          Abstract

          Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.

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          Highly accurate protein structure prediction with AlphaFold

          John Jumper, Richard Evans, Alexander Pritzel … (2021)
          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
          Article 0 comments Cited 9893 times – based on 0 reviews     Review now
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            WGCNA: an R package for weighted correlation network analysis

            Peter Langfelder, Steve Horvath (2008)
            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
            Article 0 comments Cited 6966 times – based on 0 reviews     Review now
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              High-performance medicine: the convergence of human and artificial intelligence

              Eric Topol (2019)
              The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
              Article 0 comments Cited 1293 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Xu-Jie Zhou
                Journal
                Journal ID (nlm-ta): Fundam Res
                Journal ID (iso-abbrev): Fundam Res
                Title: Fundamental Research
                Publisher: KeAi Publishing
                ISSN (Print): 2096-9457
                ISSN (Electronic): 2667-3258
                Publication date PMC-release: 16 March 2022
                Publication date Collection: January 2023
                Publication date (Electronic): 16 March 2022
                Volume: 3
                Issue: 1
                Pages: 126-148
                Affiliations
                [a ]Renal Division, Peking University First Hospital, Beijing 100034, China
                [b ]Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
                [c ]Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
                [d ]Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
                [e ]Department of Pediatrics, Peking University First Hospital, Beijing, China
                [f ]The Big Data Research Center, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
                Author notes
                [* ]Corresponding author. zhouxujie@ 123456bjmu.edu.cn
                Article
                Publisher Item ID: S2667-3258(22)00131-5
                DOI: 10.1016/j.fmre.2022.01.037
                PMC ID: 11197676
                PubMed ID: 38933564
                SO-VID: 5ae5b084-7257-4541-8b5e-22e38db581b0
                Copyright © © 2022 The Authors. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 14 September 2021
                Date revision received : 14 December 2021
                Date accepted : 24 January 2022
                Categories
                Subject: Review

                Keywords: artificial intelligence,kidney diseases,multi-omics,network,pathway,precision medicine
                Data availability:
                Keywords: artificial intelligence, kidney diseases, multi-omics, network, pathway, precision medicine

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