1
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Xu L, Liu Y. Identification, Design, and Application of Noncoding Cis-Regulatory Elements. Biomolecules 2024; 14:945. [PMID: 39199333 PMCID: PMC11352686 DOI: 10.3390/biom14080945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 07/25/2024] [Accepted: 07/30/2024] [Indexed: 09/01/2024] Open
Abstract
Cis-regulatory elements (CREs) play a pivotal role in orchestrating interactions with trans-regulatory factors such as transcription factors, RNA-binding proteins, and noncoding RNAs. These interactions are fundamental to the molecular architecture underpinning complex and diverse biological functions in living organisms, facilitating a myriad of sophisticated and dynamic processes. The rapid advancement in the identification and characterization of these regulatory elements has been marked by initiatives such as the Encyclopedia of DNA Elements (ENCODE) project, which represents a significant milestone in the field. Concurrently, the development of CRE detection technologies, exemplified by massively parallel reporter assays, has progressed at an impressive pace, providing powerful tools for CRE discovery. The exponential growth of multimodal functional genomic data has necessitated the application of advanced analytical methods. Deep learning algorithms, particularly large language models, have emerged as invaluable tools for deconstructing the intricate nucleotide sequences governing CRE function. These advancements facilitate precise predictions of CRE activity and enable the de novo design of CREs. A deeper understanding of CRE operational dynamics is crucial for harnessing their versatile regulatory properties. Such insights are instrumental in refining gene therapy techniques, enhancing the efficacy of selective breeding programs, pushing the boundaries of genetic innovation, and opening new possibilities in microbial synthetic biology.
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Affiliation(s)
- Lingna Xu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China;
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Yuwen Liu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China;
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
- Kunpeng Institute of Modern Agriculture at Foshan, Chinese Academy of Agricultural Sciences, Foshan 528226, China
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2
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Chen V, Yang M, Cui W, Kim JS, Talwalkar A, Ma J. Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments. Nat Methods 2024; 21:1454-1461. [PMID: 39122941 PMCID: PMC11348280 DOI: 10.1038/s41592-024-02359-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 06/24/2024] [Indexed: 08/12/2024]
Abstract
Recent advances in machine learning have enabled the development of next-generation predictive models for complex computational biology problems, thereby spurring the use of interpretable machine learning (IML) to unveil biological insights. However, guidelines for using IML in computational biology are generally underdeveloped. We provide an overview of IML methods and evaluation techniques and discuss common pitfalls encountered when applying IML methods to computational biology problems. We also highlight open questions, especially in the era of large language models, and call for collaboration between IML and computational biology researchers.
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Affiliation(s)
- Valerie Chen
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Muyu Yang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Wenbo Cui
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Joon Sik Kim
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ameet Talwalkar
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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3
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Viet CT, Zhang M, Dharmaraj N, Li GY, Pearson AT, Manon VA, Grandhi A, Xu K, Aouizerat BE, Young S. Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia. Tissue Eng Part A 2024. [PMID: 39041628 DOI: 10.1089/ten.tea.2024.0096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024] Open
Abstract
Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes in patients. The clinical challenge lies in identifying those patients at highest risk for developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathologic, histologic or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality of OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Machine-learning based biomarkers, such as S100A7, demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry (mIHC) workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple "hidden" layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.
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Affiliation(s)
- Chi Tonglien Viet
- Loma Linda University, Department of Oral and Maxillofacial Surgery, Loma Linda, California, United States;
| | - Michael Zhang
- Loma Linda University, Department of Oral and Maxillofacial Surgery, Loma Linda, California, United States;
| | - Neeraja Dharmaraj
- The University of Texas Health Science Center at Houston School of Dentistry, Bernard & Gloria Pepper Katz Department of Oral and Maxillofacial Surgery, Houston, Texas, United States;
| | - Grace Y Li
- The University of Chicago Medical Center, Department of Medicine, Section of Hematology/Oncology,, Chicago, Illinois, United States;
| | - Alexander T Pearson
- The University of Chicago Medical Center, Department of Medicine, Section of Hematology/Oncology,, Chicago, Illinois, United States;
| | - Victoria A Manon
- The University of Texas Health Science Center at Houston School of Dentistry, Bernard & Gloria Pepper Katz Department of Oral and Maxillofacial Surgery, Houston, Texas, United States;
| | - Anupama Grandhi
- Loma Linda University, Department of Oral and Maxillofacial Surgery, Loma Linda, California, United States;
| | - Ke Xu
- Yale School of Medicine, Department of Psychiatry, New Haven, Connecticut, United States
- VA Connecticut Healthcare System - West Haven Campus, West Haven, Connecticut, United States;
| | - Bradley E Aouizerat
- New York University College of Dentistry, Translational Research Center, New York, New York, United States;
| | - Simon Young
- The University of Texas Health Science Center at Houston School of Dentistry, Bernard & Gloria Pepper Katz Department of Oral and Maxillofacial Surgery, Houston, Texas, United States;
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4
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Weston M, Hu H, Li X. PSPI: A deep learning approach for prokaryotic small protein identification. Front Genet 2024; 15:1439423. [PMID: 39050248 PMCID: PMC11266045 DOI: 10.3389/fgene.2024.1439423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 06/18/2024] [Indexed: 07/27/2024] Open
Abstract
Small Proteins (SPs) are pivotal in various cellular functions such as immunity, defense, and communication. Despite their significance, identifying them is still in its infancy. Existing computational tools are tailored to specific eukaryotic species, leaving only a few options for SP identification in prokaryotes. In addition, these existing tools still have suboptimal performance in SP identification. To fill this gap, we introduce PSPI, a deep learning-based approach designed specifically for predicting prokaryotic SPs. We showed that PSPI had a high accuracy in predicting generalized sets of prokaryotic SPs and sets specific to the human metagenome. Compared with three existing tools, PSPI was faster and showed greater precision, sensitivity, and specificity not only for prokaryotic SPs but also for eukaryotic ones. We also observed that the incorporation of (n, k)-mers greatly enhances the performance of PSPI, suggesting that many SPs may contain short linear motifs. The PSPI tool, which is freely available at https://www.cs.ucf.edu/∼xiaoman/tools/PSPI/, will be useful for studying SPs as a tool for identifying prokaryotic SPs and it can be trained to identify other types of SPs as well.
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Affiliation(s)
- Matthew Weston
- Department of Computer Science, University of Central Florida, Orlando, FL, United States
| | - Haiyan Hu
- Department of Computer Science, University of Central Florida, Orlando, FL, United States
| | - Xiaoman Li
- Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, FL, United States
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5
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Wang S, Wang W. Interpretable prediction of mRNA abundance from promoter sequence using contextual regression models. NAR Genom Bioinform 2024; 6:lqae055. [PMID: 38807713 PMCID: PMC11131020 DOI: 10.1093/nargab/lqae055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 04/08/2024] [Accepted: 05/12/2024] [Indexed: 05/30/2024] Open
Abstract
While machine learning models have been successfully applied to predicting gene expression from promoter sequences, it remains a great challenge to derive intuitive interpretation of the model and reveal DNA motif grammar such as motif cooperation and distance constraint between motif sites. Previous interpretation approaches are often time-consuming or have difficulty to learn the combinatory rules. In this work, we designed interpretable neural network models to predict the mRNA expression levels from DNA sequences. By applying the Contextual Regression framework we developed, we extracted weighted features to cluster samples into different groups, which have different gene expression levels. We performed motif analysis in each cluster and found motifs with active or repressive regulation on gene expression. By comparing the co-occurrence locations of discovered motifs, we also uncovered multiple grammars of motif combination including communities of cooperative motifs and distance constraints between motif pairs. These results revealed new insights of the regulatory architecture of promoter sequences.
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Affiliation(s)
- Song Wang
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359, USA
| | - Wei Wang
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093-0359, USA
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6
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Dotan E, Jaschek G, Pupko T, Belinkov Y. Effect of tokenization on transformers for biological sequences. Bioinformatics 2024; 40:btae196. [PMID: 38608190 PMCID: PMC11055402 DOI: 10.1093/bioinformatics/btae196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 02/20/2024] [Accepted: 04/11/2024] [Indexed: 04/14/2024] Open
Abstract
MOTIVATION Deep-learning models are transforming biological research, including many bioinformatics and comparative genomics algorithms, such as sequence alignments, phylogenetic tree inference, and automatic classification of protein functions. Among these deep-learning algorithms, models for processing natural languages, developed in the natural language processing (NLP) community, were recently applied to biological sequences. However, biological sequences are different from natural languages, such as English, and French, in which segmentation of the text to separate words is relatively straightforward. Moreover, biological sequences are characterized by extremely long sentences, which hamper their processing by current machine-learning models, notably the transformer architecture. In NLP, one of the first processing steps is to transform the raw text to a list of tokens. Deep-learning applications to biological sequence data mostly segment proteins and DNA to single characters. In this work, we study the effect of alternative tokenization algorithms on eight different tasks in biology, from predicting the function of proteins and their stability, through nucleotide sequence alignment, to classifying proteins to specific families. RESULTS We demonstrate that applying alternative tokenization algorithms can increase accuracy and at the same time, substantially reduce the input length compared to the trivial tokenizer in which each character is a token. Furthermore, applying these tokenization algorithms allows interpreting trained models, taking into account dependencies among positions. Finally, we trained these tokenizers on a large dataset of protein sequences containing more than 400 billion amino acids, which resulted in over a 3-fold decrease in the number of tokens. We then tested these tokenizers trained on large-scale data on the above specific tasks and showed that for some tasks it is highly beneficial to train database-specific tokenizers. Our study suggests that tokenizers are likely to be a critical component in future deep-network analysis of biological sequence data. AVAILABILITY AND IMPLEMENTATION Code, data, and trained tokenizers are available on https://github.com/technion-cs-nlp/BiologicalTokenizers.
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Affiliation(s)
- Edo Dotan
- The Henry and Marilyn Taub Faculty of Computer Science, Technion – Israel Institute of Technology, Haifa 3200003, Israel
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Gal Jaschek
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, United States
| | - Tal Pupko
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yonatan Belinkov
- The Henry and Marilyn Taub Faculty of Computer Science, Technion – Israel Institute of Technology, Haifa 3200003, Israel
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7
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Sharma A, Lysenko A, Jia S, Boroevich KA, Tsunoda T. Advances in AI and machine learning for predictive medicine. J Hum Genet 2024:10.1038/s10038-024-01231-y. [PMID: 38424184 DOI: 10.1038/s10038-024-01231-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/04/2024] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit limitations in handling potential relationships within the data for more accurate prediction. This review explores a revolutionary shift in predictive modeling through the application of deep learning (DL), specifically convolutional neural networks (CNNs). Using transformation methods such as DeepInsight, omics data with independent variables in tabular (table-like, including vector) form can be turned into image-like representations, enabling CNNs to capture latent features effectively. This approach not only enhances predictive power but also leverages transfer learning, reducing computational time, and improving performance. However, integrating CNNs in predictive omics data analysis is not without challenges, including issues related to model interpretability, data heterogeneity, and data size. Addressing these challenges requires a multidisciplinary approach, involving collaborations between ML experts, bioinformatics researchers, biologists, and medical doctors. This review illuminates these complexities and charts a course for future research to unlock the full predictive potential of CNNs in omics data analysis and related fields.
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Affiliation(s)
- Alok Sharma
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Institute for Integrated and Intelligent Systems, Griffith University, Queensland, Australia.
| | - Artem Lysenko
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
| | - Shangru Jia
- Laboratory for Medical Science Mathematics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Keith A Boroevich
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Laboratory for Medical Science Mathematics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
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8
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Yan Z, Ge F, Liu Y, Zhang Y, Li F, Song J, Yu DJ. TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion. J Chem Inf Model 2024; 64:1407-1418. [PMID: 38334115 DOI: 10.1021/acs.jcim.3c02019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Studying the effect of single amino acid variations (SAVs) on protein structure and function is integral to advancing our understanding of molecular processes, evolutionary biology, and disease mechanisms. Screening for deleterious variants is one of the crucial issues in precision medicine. Here, we propose a novel computational approach, TransEFVP, based on large-scale protein language model embeddings and a transformer-based neural network to predict disease-associated SAVs. The model adopts a two-stage architecture: the first stage is designed to fuse different feature embeddings through a transformer encoder. In the second stage, a support vector machine model is employed to quantify the pathogenicity of SAVs after dimensionality reduction. The prediction performance of TransEFVP on blind test data achieves a Matthews correlation coefficient of 0.751, an F1-score of 0.846, and an area under the receiver operating characteristic curve of 0.871, higher than the existing state-of-the-art methods. The benchmark results demonstrate that TransEFVP can be explored as an accurate and effective SAV pathogenicity prediction method. The data and codes for TransEFVP are available at https://github.com/yzh9607/TransEFVP/tree/master for academic use.
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Affiliation(s)
- Zihao Yan
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China
| | - Fang Ge
- State Key Laboratory of Organic Electronics and lnformation Displays & lnstitute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, PR China
| | - Yan Liu
- Department of Computer Science, Yangzhou University, Yangzhou 225100, PR China
| | - Yumeng Zhang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, PR China
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Fuyi Li
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria 3000, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China
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9
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Mikhailova AA, Rinke S, Harrison MC. Genomic signatures of eusocial evolution in insects. CURRENT OPINION IN INSECT SCIENCE 2024; 61:101136. [PMID: 37922983 DOI: 10.1016/j.cois.2023.101136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/27/2023] [Accepted: 10/28/2023] [Indexed: 11/07/2023]
Abstract
The genomes of eusocial insects allow the production and regulation of highly distinct phenotypes, largely independent of genotype. Although rare, eusociality has evolved convergently in at least three insect orders (Hymenoptera, Blattodea and Coleoptera). Despite such disparate origins, eusocial phenotypes show remarkable similarity, exhibiting long-lived reproductives and short-lived sterile workers and soldiers. In this article, we review current knowledge on genomic signatures of eusocial evolution. We confirm that especially an increased regulatory complexity and the adaptive evolution of chemical communication are common to several origins of eusociality. Furthermore, colony life itself can shape genomes of divergent taxa in a similar manner. Future research should be geared towards generating more high-quality genomic resources, especially in hitherto understudied clades, such as ambrosia beetles and termites. The application of more sophisticated tools such as machine learning techniques may allow the detection of more subtle convergent genomic footprints of eusociality.
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Affiliation(s)
- Alina A Mikhailova
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasße 1, 48149 Münster, Germany
| | - Sarah Rinke
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasße 1, 48149 Münster, Germany
| | - Mark C Harrison
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasße 1, 48149 Münster, Germany.
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10
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Zheng H, Wang S, Li X, Hu H. A computational modeling of pri-miRNA expression. PLoS One 2024; 19:e0290768. [PMID: 38165860 PMCID: PMC10760784 DOI: 10.1371/journal.pone.0290768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 08/15/2023] [Indexed: 01/04/2024] Open
Abstract
MicroRNAs (miRNAs) play crucial roles in gene regulation. Most studies focus on mature miRNAs, which leaves many unknowns about primary miRNAs (pri-miRNAs). To fill the gap, we attempted to model the expression of pri-miRNAs in 1829 primary cell types, cell lines, and tissues in this study. We demonstrated that the expression of pri-miRNAs can be modeled well by the expression of specific sets of mRNAs, which we termed their associated mRNAs. These associated mRNAs differ from their corresponding target mRNAs and are enriched with specific functions. Most associated mRNAs of a miRNA are shared across conditions, while on average, about one-fifth of the associated mRNAs are condition-specific. Our study shed new light on understanding miRNA biogenesis and general gene transcriptional regulation.
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Affiliation(s)
- Hansi Zheng
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
| | - Saidi Wang
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
| | - Xiaoman Li
- Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, Florida, United States of America
| | - Haiyan Hu
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
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11
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van Oosterhout C. AI-informed conservation genomics. Heredity (Edinb) 2024; 132:1-4. [PMID: 38151537 PMCID: PMC10798949 DOI: 10.1038/s41437-023-00666-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/09/2023] [Accepted: 12/11/2023] [Indexed: 12/29/2023] Open
Affiliation(s)
- Cock van Oosterhout
- School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK.
- Conservation Genetics Specialist Group, International Union for Conservation of Nature (IUCN), Gland, Switzerland.
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12
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Qiao C, Gao B, Liu Y, Hu X, Hu W, Calhoun VD, Wang YP. Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivity. Med Image Anal 2023; 90:102941. [PMID: 37683445 DOI: 10.1016/j.media.2023.102941] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 08/19/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023]
Abstract
Although many deep learning models-based medical applications are performance-driven, i.e., accuracy-oriented, their explainability is more critical. This is especially the case with neuroimaging, where we are often interested in identifying biomarkers underlying brain development or disorders. Herein we propose an explainable deep learning approach by elucidating the information transmission mechanism between two layers of a deep network with a joint feature selection strategy that considers several shallow-layer explainable machine learning models and sparse learning of the deep network. At the end, we apply and validate the proposed approach to the analysis of dynamic brain functional connectivity (FC) from fMRI in a brain development study. Our approach can identify the differences within and between functional brain networks over age during development. The results indicate that the brain network transits from undifferentiated structures to more specialized and organized ones, and the information processing ability becomes more efficient as age increases. In addition, we detect two developmental patterns in the brain network: the FCs in regions related to visual and sound processing and mental regulation become weakened, while those between regions corresponding to emotional processing and cognitive activities are enhanced.
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Affiliation(s)
- Chen Qiao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
| | - Bin Gao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
| | - Yuechen Liu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
| | - Xinyu Hu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
| | - Wenxing Hu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, USA; Emory University, Atlanta, GA 30303, USA.
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.
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13
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Toussaint PA, Leiser F, Thiebes S, Schlesner M, Brors B, Sunyaev A. Explainable artificial intelligence for omics data: a systematic mapping study. Brief Bioinform 2023; 25:bbad453. [PMID: 38113073 PMCID: PMC10729786 DOI: 10.1093/bib/bbad453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 07/28/2023] [Accepted: 11/08/2023] [Indexed: 12/21/2023] Open
Abstract
Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in their respective research community. An overview of XAI for omics data is needed to highlight promising approaches and help detect common issues. Toward this end, we conducted a systematic mapping study. To identify relevant literature, we queried Scopus, PubMed, Web of Science, BioRxiv, MedRxiv and arXiv. Based on keywording, we developed a coding scheme with 10 facets regarding the studies' AI methods, explainability methods and omics data. Our mapping study resulted in 405 included papers published between 2010 and 2023. The inspected papers analyze DNA-based (mostly genomic), transcriptomic, proteomic or metabolomic data by means of neural networks, tree-based methods, statistical methods and further AI methods. The preferred post-hoc explainability methods are feature relevance (n = 166) and visual explanation (n = 52), while papers using interpretable approaches often resort to the use of transparent models (n = 83) or architecture modifications (n = 72). With many research gaps still apparent for XAI for omics data, we deduced eight research directions and discuss their potential for the field. We also provide exemplary research questions for each direction. Many problems with the adoption of XAI for omics data in clinical practice are yet to be resolved. This systematic mapping study outlines extant research on the topic and provides research directions for researchers and practitioners.
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Affiliation(s)
- Philipp A Toussaint
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
- HIDSS4Health – Helmholtz Information and Data Science School for Health, Karlsruhe, Heidelberg, Germany
| | - Florian Leiser
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Scott Thiebes
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Matthias Schlesner
- Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Translational Oncology, National Center for Tumor Diseases, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ali Sunyaev
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
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14
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Klie A, Laub D, Talwar JV, Stites H, Jores T, Solvason JJ, Farley EK, Carter H. Predictive analyses of regulatory sequences with EUGENe. NATURE COMPUTATIONAL SCIENCE 2023; 3:946-956. [PMID: 38177592 PMCID: PMC10768637 DOI: 10.1038/s43588-023-00544-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 09/27/2023] [Indexed: 01/06/2024]
Abstract
Deep learning has become a popular tool to study cis-regulatory function. Yet efforts to design software for deep-learning analyses in regulatory genomics that are findable, accessible, interoperable and reusable (FAIR) have fallen short of fully meeting these criteria. Here we present elucidating the utility of genomic elements with neural nets (EUGENe), a FAIR toolkit for the analysis of genomic sequences with deep learning. EUGENe consists of a set of modules and subpackages for executing the key functionality of a genomics deep learning workflow: (1) extracting, transforming and loading sequence data from many common file formats; (2) instantiating, initializing and training diverse model architectures; and (3) evaluating and interpreting model behavior. We designed EUGENe as a simple, flexible and extensible interface for streamlining and customizing end-to-end deep-learning sequence analyses, and illustrate these principles through application of the toolkit to three predictive modeling tasks. We hope that EUGENe represents a springboard towards a collaborative ecosystem for deep-learning applications in genomics research.
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Affiliation(s)
- Adam Klie
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - David Laub
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - James V Talwar
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | | | - Tobias Jores
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Joe J Solvason
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Molecular Biology, University of California San Diego, La Jolla, CA, USA
| | - Emma K Farley
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Molecular Biology, University of California San Diego, La Jolla, CA, USA
| | - Hannah Carter
- Department of Medicine, University of California San Diego, La Jolla, CA, USA.
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
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15
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Tognon M, Giugno R, Pinello L. A survey on algorithms to characterize transcription factor binding sites. Brief Bioinform 2023; 24:bbad156. [PMID: 37099664 PMCID: PMC10422928 DOI: 10.1093/bib/bbad156] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/27/2023] [Accepted: 04/01/2023] [Indexed: 04/28/2023] Open
Abstract
Transcription factors (TFs) are key regulatory proteins that control the transcriptional rate of cells by binding short DNA sequences called transcription factor binding sites (TFBS) or motifs. Identifying and characterizing TFBS is fundamental to understanding the regulatory mechanisms governing the transcriptional state of cells. During the last decades, several experimental methods have been developed to recover DNA sequences containing TFBS. In parallel, computational methods have been proposed to discover and identify TFBS motifs based on these DNA sequences. This is one of the most widely investigated problems in bioinformatics and is referred to as the motif discovery problem. In this manuscript, we review classical and novel experimental and computational methods developed to discover and characterize TFBS motifs in DNA sequences, highlighting their advantages and drawbacks. We also discuss open challenges and future perspectives that could fill the remaining gaps in the field.
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Affiliation(s)
- Manuel Tognon
- Computer Science Department, University of Verona, Verona, Italy
- Molecular Pathology Unit, Center for Computational and Integrative Biology and Center for Cancer Research, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Rosalba Giugno
- Computer Science Department, University of Verona, Verona, Italy
| | - Luca Pinello
- Molecular Pathology Unit, Center for Computational and Integrative Biology and Center for Cancer Research, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Department of Pathology, Harvard Medical School, Boston, Massachusetts, United States of America
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16
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Wysocka M, Wysocki O, Zufferey M, Landers D, Freitas A. A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data. BMC Bioinformatics 2023; 24:198. [PMID: 37189058 DOI: 10.1186/s12859-023-05262-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 03/30/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings. METHODS This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. For this, we retrieved and analyzed 42 studies focusing on emerging architectural and methodological advances, the encoding of biological domain knowledge and the integration of explainability methods. RESULTS We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We introduce a concept of bio-centric interpretability and according to its taxonomy, we discuss representational methodologies for the integration of domain prior knowledge in such models. CONCLUSIONS The paper provides a critical outlook into contemporary methods for explainability and interpretability used in DL for cancer. The analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability. We introduce bio-centric interpretability which is an important step towards formalisation of biological interpretability of DL models and developing methods that are less problem- or application-specific.
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Affiliation(s)
- Magdalena Wysocka
- Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK.
- Department of Computer Science, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK.
| | - Oskar Wysocki
- Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK.
- Department of Computer Science, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK.
- Idiap Research Institute, National University of Sciences, Rue Marconi 19, CH - 1920, Martigny, Switzerland.
| | - Marie Zufferey
- Idiap Research Institute, National University of Sciences, Rue Marconi 19, CH - 1920, Martigny, Switzerland
| | - Dónal Landers
- DeLondra Oncology Ltd, 38 Carlton Avenue, Wilmslow, SK9 4EP, UK
| | - André Freitas
- Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK
- Department of Computer Science, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK
- Idiap Research Institute, National University of Sciences, Rue Marconi 19, CH - 1920, Martigny, Switzerland
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17
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Smith GD, Ching WH, Cornejo-Páramo P, Wong ES. Decoding enhancer complexity with machine learning and high-throughput discovery. Genome Biol 2023; 24:116. [PMID: 37173718 PMCID: PMC10176946 DOI: 10.1186/s13059-023-02955-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
Enhancers are genomic DNA elements controlling spatiotemporal gene expression. Their flexible organization and functional redundancies make deciphering their sequence-function relationships challenging. This article provides an overview of the current understanding of enhancer organization and evolution, with an emphasis on factors that influence these relationships. Technological advancements, particularly in machine learning and synthetic biology, are discussed in light of how they provide new ways to understand this complexity. Exciting opportunities lie ahead as we continue to unravel the intricacies of enhancer function.
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Affiliation(s)
- Gabrielle D Smith
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, Australia
- School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Kensington, NSW, Australia
| | - Wan Hern Ching
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, Australia
| | - Paola Cornejo-Páramo
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, Australia
- School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Kensington, NSW, Australia
| | - Emily S Wong
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, Australia.
- School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Kensington, NSW, Australia.
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18
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Xu F, Qiao C, Zhou H, Calhoun VD, Stephen JM, Wilson TW, Wang Y. An explainable autoencoder with multi-paradigm fMRI fusion for identifying differences in dynamic functional connectivity during brain development. Neural Netw 2023; 159:185-197. [PMID: 36580711 DOI: 10.1016/j.neunet.2022.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/19/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
Multi-paradigm deep learning models show great potential for dynamic functional connectivity (dFC) analysis by integrating complementary information. However, many of them cannot use information from different paradigms effectively and have poor explainability, that is, the ability to identify significant features that contribute to decision making. In this paper, we propose a multi-paradigm fusion-based explainable deep sparse autoencoder (MF-EDSAE) to address these issues. Considering explainability, the MF-EDSAE is constructed based on a deep sparse autoencoder (DSAE). For integrating information effectively, the MF-EDASE contains the nonlinear fusion layer and multi-paradigm hypergraph regularization. We apply the model to the Philadelphia Neurodevelopmental Cohort and demonstrate it achieves better performance in detecting dynamic FC (dFC) that differ significantly during brain development than the single-paradigm DSAE. The experimental results show that children have more dispersive dFC patterns than adults. The function of the brain transits from undifferentiated systems to specialized networks during brain development. Meanwhile, adults have stronger connectivities between task-related functional networks for a given task than children. As the brain develops, the patterns of the global dFC change more quickly when stimulated by a task.
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Affiliation(s)
- Faming Xu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
| | - Chen Qiao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
| | - Huiyu Zhou
- School of Computing and Mathematical Sciences, University of Leicester, LE1 7RH, UK.
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30030, USA.
| | | | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA.
| | - Yuping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA.
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19
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Deep learning in regulatory genomics: from identification to design. Curr Opin Biotechnol 2023; 79:102887. [PMID: 36640453 DOI: 10.1016/j.copbio.2022.102887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/12/2022] [Accepted: 12/14/2022] [Indexed: 01/14/2023]
Abstract
Genomics and deep learning are a natural match since both are data-driven fields. Regulatory genomics refers to functional noncoding DNA regulating gene expression. In recent years, deep learning applications on regulatory genomics have achieved remarkable advances so-much-so that it has revolutionized the rules of the game of the computational methods in this field. Here, we review two emerging trends: (i) the modeling of very long input sequence (up to 200 kb), which requires self-matched modularization of model architecture; (ii) on the balance of model predictability and model interpretability because the latter is more able to meet biological demands. Finally, we discuss how to employ these two routes to design synthetic regulatory DNA, as a promising strategy for optimizing crop agronomic properties.
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20
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Hu Q, Li K, Yang C, Wang Y, Huang R, Gu M, Xiao Y, Huang Y, Chen L. The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges. Front Oncol 2023; 13:1133164. [PMID: 36959810 PMCID: PMC10028142 DOI: 10.3389/fonc.2023.1133164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 02/20/2023] [Indexed: 03/09/2023] Open
Abstract
Objectives Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC). Materials and methods A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis. Results Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability. Conclusion AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.
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Affiliation(s)
- Qiuyuan Hu
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Ke Li
- Department of Cancer Biotherapy Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Conghui Yang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yue Wang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Rong Huang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Mingqiu Gu
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yuqiang Xiao
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yunchao Huang
- Department of Thoracic Surgery I, Key Laboratory of Lung Cancer of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
- *Correspondence: Long Chen, ; Yunchao Huang,
| | - Long Chen
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
- *Correspondence: Long Chen, ; Yunchao Huang,
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21
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Ott J, Park T. Overview of frequent pattern mining. Genomics Inform 2022; 20:e39. [PMID: 36617647 PMCID: PMC9847378 DOI: 10.5808/gi.22074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 12/22/2022] [Indexed: 12/31/2022] Open
Abstract
Various methods of frequent pattern mining have been applied to genetic problems, specifically, to the combined association of two genotypes (a genotype pattern, or diplotype) at different DNA variants with disease. These methods have the ability to come up with a selection of genotype patterns that are more common in affected than unaffected individuals, and the assessment of statistical significance for these selected patterns poses some unique problems, which are briefly outlined here.
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Affiliation(s)
- Jurg Ott
- Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10065, USA,Corresponding author E-mail:
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul 08826, Korea
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22
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Galindez G, Sadegh S, Baumbach J, Kacprowski T, List M. Network-based approaches for modeling disease regulation and progression. Comput Struct Biotechnol J 2022; 21:780-795. [PMID: 36698974 PMCID: PMC9841310 DOI: 10.1016/j.csbj.2022.12.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
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Affiliation(s)
- Gihanna Galindez
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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23
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Lee M, Kim PJ, Joe H, Kim HG. Gene-centric multi-omics integration with convolutional encoders for cancer drug response prediction. Comput Biol Med 2022; 151:106192. [PMID: 36327883 DOI: 10.1016/j.compbiomed.2022.106192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/26/2022] [Accepted: 10/08/2022] [Indexed: 12/27/2022]
Abstract
MOTIVATION Tumor heterogeneity, including genetic and transcriptomic characteristics, can reduce the efficacy of anticancer pharmacological therapy, resulting in clinical variability in patient response to therapeutic medications. Multi-omics integration can allow in silico models to provide an additional perspective on a biological system. METHODS In this study, we propose a gene-centric multi-channel (GCMC) architecture to integrate multi-omics for predicting cancer drug response. GCMC transformed multi-omics profiles into a three-dimensional tensor with an additional dimension for omics types. GCMC's convolutional encoders captures multi-omics profiles for each gene and yields gene-centric features to predict drug responses. RESULTS We evaluated GCMC on various datasets, including The Cancer Genome Atlas (TCGA) patients, patient-derived xenografts (PDX) mice models, and the Genomics of Drug Sensitivity in Cancer (GDSC) cell line datasets. GCMC achieved better performance than baseline models, including single-omics models, in more than 75% of 265 drugs from GDSC cell line datasets. Furthermore, as for the clinical applicability of GCMC, it achieved the best performance on TCGA and PDX datasets in terms of both AUPR and AUC. We also analyzed models' capability of integrating multi-omics profiles by measuring the contribution ratio of omics types. GCMC can incorporate multi-omics profiles in various manners to enhance performance for each drug type. These results suggested that GCMC can improve performance and feature extraction capability by integrating multi-omics profiles in a gene-centric manner.
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Affiliation(s)
- Munhwan Lee
- Biomedical Knowledge Engineering Lab., Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
| | - Pil-Jong Kim
- Biomedical Knowledge Engineering Lab., Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
| | - Hyunwhan Joe
- Biomedical Knowledge Engineering Lab., Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
| | - Hong-Gee Kim
- Biomedical Knowledge Engineering Lab., Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
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24
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Lan AY, Corces MR. Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases. Front Aging Neurosci 2022; 14:1027224. [PMID: 36466610 PMCID: PMC9716280 DOI: 10.3389/fnagi.2022.1027224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/24/2022] [Indexed: 11/19/2022] Open
Abstract
Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer's-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.
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Affiliation(s)
- Alexander Y. Lan
- Gladstone Institute of Neurological Disease, San Francisco, CA, United States
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States
- Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - M. Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, United States
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States
- Department of Neurology, University of California San Francisco, San Francisco, CA, United States
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25
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Linder J, Koplik SE, Kundaje A, Seelig G. Deciphering the impact of genetic variation on human polyadenylation using APARENT2. Genome Biol 2022; 23:232. [PMID: 36335397 PMCID: PMC9636789 DOI: 10.1186/s13059-022-02799-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 10/19/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND 3'-end processing by cleavage and polyadenylation is an important and finely tuned regulatory process during mRNA maturation. Numerous genetic variants are known to cause or contribute to human disorders by disrupting the cis-regulatory code of polyadenylation signals. Yet, due to the complexity of this code, variant interpretation remains challenging. RESULTS We introduce a residual neural network model, APARENT2, that can infer 3'-cleavage and polyadenylation from DNA sequence more accurately than any previous model. This model generalizes to the case of alternative polyadenylation (APA) for a variable number of polyadenylation signals. We demonstrate APARENT2's performance on several variant datasets, including functional reporter data and human 3' aQTLs from GTEx. We apply neural network interpretation methods to gain insights into disrupted or protective higher-order features of polyadenylation. We fine-tune APARENT2 on human tissue-resolved transcriptomic data to elucidate tissue-specific variant effects. By combining APARENT2 with models of mRNA stability, we extend aQTL effect size predictions to the entire 3' untranslated region. Finally, we perform in silico saturation mutagenesis of all human polyadenylation signals and compare the predicted effects of [Formula: see text] million variants against gnomAD. While loss-of-function variants were generally selected against, we also find specific clinical conditions linked to gain-of-function mutations. For example, we detect an association between gain-of-function mutations in the 3'-end and autism spectrum disorder. To experimentally validate APARENT2's predictions, we assayed clinically relevant variants in multiple cell lines, including microglia-derived cells. CONCLUSIONS A sequence-to-function model based on deep residual learning enables accurate functional interpretation of genetic variants in polyadenylation signals and, when coupled with large human variation databases, elucidates the link between functional 3'-end mutations and human health.
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Affiliation(s)
| | | | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, USA
- Department of Computer Science, Stanford University, Stanford, USA
| | - Georg Seelig
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, USA
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26
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Wang Y, Huang X, Xian B, Jiang H, Zhou T, Chen S, Wen F, Pei J. Machine learning and bioinformatics-based insights into the potential targets of saponins in Paris polyphylla smith against non-small cell lung cancer. Front Genet 2022; 13:1005896. [PMID: 36386821 PMCID: PMC9649596 DOI: 10.3389/fgene.2022.1005896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/17/2022] [Indexed: 12/24/2022] Open
Abstract
Background: Lung cancer has the highest mortality rate among cancers worldwide, and non-small cell lung cancer (NSCLC) is the major lethal factor. Saponins in Paris polyphylla smith exhibit antitumor activity against non-small cell lung cancer, but their targets are not fully understood. Methods: In this study, we used differential gene analysis, lasso regression analysis and support vector machine recursive feature elimination (SVM-RFE) to screen potential key genes for NSCLC by using relevant datasets from the GEO database. The accuracy of the signature genes was verified by using ROC curves and gene expression values. Screening of potential active ingredients for the treatment of NSCLC by molecular docking of the reported active ingredients of saponins in Paris polyphylla Smith with the screened signature genes. The activity of the screened components and their effects on key genes expression were further validated by CCK-8, flow cytometry (apoptosis and cycling) and qPCR. Results: 204 differential genes and two key genes (RHEBL1, RNPC3) stood out in the bioinformatics analysis. Overall survival (OS), First-progression survival (FP) and post-progression survival (PPS) analysis revealed that low expression of RHEBL1 and high expression of RNPC3 indicated good prognosis. In addition, Polyphyllin VI(PPVI) and Protodioscin (Prot) effectively inhibited the proliferation of non-small cell lung cancer cell line with IC50 of 4.46 μM ± 0.69 μM and 8.09 μM ± 0.67μM, respectively. The number of apoptotic cells increased significantly with increasing concentrations of PPVI and Prot. Prot induces G1/G0 phase cell cycle arrest and PPVI induces G2/M phase cell cycle arrest. After PPVI and Prot acted on this cell line for 48 h, the expression of RHEBL1 and RNPC3 was found to be consistent with the results of bioinformatics analysis. Conclusion: This study identified two potential key genes (RHEBL1 and RNPC3) in NSCLC. Additionally, PPVI and Prot may act on RHEBL1 and RNPC3 to affect NSCLC. Our findings provide a reference for clinical treatment of NSCLC.
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Affiliation(s)
| | | | | | | | | | | | | | - Jin Pei
- *Correspondence: Feiyan Wen, ; Jin Pei,
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27
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Towards a better understanding of TF-DNA binding prediction from genomic features. Comput Biol Med 2022; 149:105993. [DOI: 10.1016/j.compbiomed.2022.105993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/12/2022] [Accepted: 08/14/2022] [Indexed: 11/17/2022]
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Abstract
High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state-of-the-art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines.
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Affiliation(s)
- David S Watson
- Department of Statistical Science, University College London, London, UK.
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29
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Alharbi WS, Rashid M. A review of deep learning applications in human genomics using next-generation sequencing data. Hum Genomics 2022; 16:26. [PMID: 35879805 PMCID: PMC9317091 DOI: 10.1186/s40246-022-00396-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.
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Affiliation(s)
- Wardah S Alharbi
- Department of AI and Bioinformatics, King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh, 11426, Saudi Arabia
| | - Mamoon Rashid
- Department of AI and Bioinformatics, King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh, 11426, Saudi Arabia.
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30
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Talukder A, Zhang W, Li X, Hu H. A deep learning method for miRNA/isomiR target detection. Sci Rep 2022; 12:10618. [PMID: 35739186 PMCID: PMC9226005 DOI: 10.1038/s41598-022-14890-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
Accurate identification of microRNA (miRNA) targets at base-pair resolution has been an open problem for over a decade. The recent discovery of miRNA isoforms (isomiRs) adds more complexity to this problem. Despite the existence of many methods, none considers isomiRs, and their performance is still suboptimal. We hypothesize that by taking the isomiR-mRNA interactions into account and applying a deep learning model to study miRNA-mRNA interaction features, we may improve the accuracy of miRNA target predictions. We developed a deep learning tool called DMISO to capture the intricate features of miRNA/isomiR-mRNA interactions. Based on tenfold cross-validation, DMISO showed high precision (95%) and recall (90%). Evaluated on three independent datasets, DMISO had superior performance to five tools, including three popular conventional tools and two recently developed deep learning-based tools. By applying two popular feature interpretation strategies, we demonstrated the importance of the miRNA regions other than their seeds and the potential contribution of the RNA-binding motifs within miRNAs/isomiRs and mRNAs to the miRNA/isomiR-mRNA interactions.
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Affiliation(s)
- Amlan Talukder
- Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA
| | - Wencai Zhang
- Burnett School of Biomedical Science, University of Central Florida, Orlando, FL, 32816, USA
| | - Xiaoman Li
- Burnett School of Biomedical Science, University of Central Florida, Orlando, FL, 32816, USA.
| | - Haiyan Hu
- Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA.
- Genomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL, 32816, USA.
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31
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Zhang Y, Hua S, Jiang Q, Xie Z, Wu L, Wang X, Shi F, Dong S, Jiang J. Identification of Feature Genes of a Novel Neural Network Model for Bladder Cancer. Front Genet 2022; 13:912171. [PMID: 35719407 PMCID: PMC9198295 DOI: 10.3389/fgene.2022.912171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background: The combination of deep learning methods and oncogenomics can provide an effective diagnostic method for malignant tumors; thus, we attempted to construct a reliable artificial neural network model as a novel diagnostic tool for Bladder cancer (BLCA). Methods: Three expression profiling datasets (GSE61615, GSE65635, and GSE100926) were downloaded from the Gene Expression Omnibus (GEO) database. GSE61615 and GSE65635 were taken as the train group, while GSE100926 was set as the test group. Differentially expressed genes (DEGs) were filtered out based on the logFC and FDR values. We also performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to explore the biological functions of the DEGs. Consequently, we utilized a random forest algorithm to identify feature genes and further constructed a neural network model. The test group was given the same procedures to validate the reliability of the model. We also explored immune cells' infiltration degree and correlation coefficients through the CiberSort algorithm and corrplot R package. The qRT-PCR assay was implemented to examine the expression level of the feature genes in vitro. Results: A total of 265 DEGs were filtered out and significantly enriched in muscle system processes, collagen-containing and focal adhesion signaling pathways. Based on the random forest algorithm, we selected 14 feature genes to construct the neural network model. The area under the curve (AUC) of the training group was 0.950 (95% CI: 0.850-1.000), and the AUC of the test group was 0.667 (95% CI: 0.333-1.000). Besides, we observed significant differences in the content of immune infiltrating cells and the expression levels of the feature genes. Conclusion: After repeated verification, our neural network model had clinical feasibility to identify bladder cancer patients and provided a potential target to improve the management of BLCA.
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Affiliation(s)
- Yongqing Zhang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shan Hua
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiheng Jiang
- Department of Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiwen Xie
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lei Wu
- Department of Urology, Shanghai General Hospital, Nanjing Medical University School of Medicine, Shanghai, China
| | - Xinjie Wang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fei Shi
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shengli Dong
- Nursing Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juntao Jiang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Department of Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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32
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Mo H, Breitling R, Francavilla C, Schwartz JM. Data integration and mechanistic modelling for breast cancer biology: Current state and future directions. CURRENT OPINION IN ENDOCRINE AND METABOLIC RESEARCH 2022; 24:None. [PMID: 36034741 PMCID: PMC9402443 DOI: 10.1016/j.coemr.2022.100350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence, and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for methods to investigate novel treatment options, while taking into account the vast molecular heterogeneity of breast cancer. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics and metabolomics data, enable approaching breast cancer biology at multiple levels of omics interaction networks. Systems biology approaches, including computational inference of ‘big data’ and mechanistic modelling of specific pathways, are emerging to identify potential novel combinations of breast cancer subtype signatures and more diverse targeted therapies.
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33
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Prybutok AN, Cain JY, Leonard JN, Bagheri N. Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems. Curr Opin Biotechnol 2022; 75:102704. [DOI: 10.1016/j.copbio.2022.102704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 01/27/2022] [Accepted: 02/06/2022] [Indexed: 11/03/2022]
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34
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Chalupová E, Vaculík O, Poláček J, Jozefov F, Majtner T, Alexiou P. ENNGene: an Easy Neural Network model building tool for Genomics. BMC Genomics 2022; 23:248. [PMID: 35361122 PMCID: PMC8973509 DOI: 10.1186/s12864-022-08414-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 02/23/2022] [Indexed: 11/17/2022] Open
Abstract
Background The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field. Results Here we present ENNGene—Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein. Conclusions As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08414-x.
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Affiliation(s)
- Eliška Chalupová
- Faculty of Science, National Centre for Biomolecular Research, Masaryk University, Brno, Czechia.,Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
| | - Ondřej Vaculík
- Faculty of Science, National Centre for Biomolecular Research, Masaryk University, Brno, Czechia.,Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
| | - Jakub Poláček
- Faculty of Informatics, Masaryk University, Brno, Czechia
| | - Filip Jozefov
- Faculty of Informatics, Masaryk University, Brno, Czechia
| | - Tomáš Majtner
- Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
| | - Panagiotis Alexiou
- Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia.
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35
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Andrades R, Recamonde-Mendoza M. Machine learning methods for prediction of cancer driver genes: a survey paper. Brief Bioinform 2022; 23:6551145. [PMID: 35323900 DOI: 10.1093/bib/bbac062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/06/2022] [Accepted: 02/08/2022] [Indexed: 12/21/2022] Open
Abstract
Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.
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Affiliation(s)
- Renan Andrades
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
| | - Mariana Recamonde-Mendoza
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
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36
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Ventolero MF, Wang S, Hu H, Li X. Computational analyses of bacterial strains from shotgun reads. Brief Bioinform 2022; 23:6524011. [PMID: 35136954 DOI: 10.1093/bib/bbac013] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 12/21/2022] Open
Abstract
Shotgun sequencing is routinely employed to study bacteria in microbial communities. With the vast amount of shotgun sequencing reads generated in a metagenomic project, it is crucial to determine the microbial composition at the strain level. This study investigated 20 computational tools that attempt to infer bacterial strain genomes from shotgun reads. For the first time, we discussed the methodology behind these tools. We also systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools. Because the performance of the best tools is still suboptimal, we discussed future directions that may address the limitations.
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Affiliation(s)
| | - Saidi Wang
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Haiyan Hu
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA.,Genomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL 32816, USA
| | - Xiaoman Li
- Burnett School of Biomedical Science, University of Central Florida, Orlando, FL 32816, USA
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37
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Interpreting neural networks for biological sequences by learning stochastic masks. NAT MACH INTELL 2022; 4:41-54. [DOI: 10.1038/s42256-021-00428-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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38
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AIM in Medical Informatics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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39
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Huminiecki Ł. Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science. ENTROPY (BASEL, SWITZERLAND) 2021; 24:17. [PMID: 35052043 PMCID: PMC8774939 DOI: 10.3390/e24010017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/02/2021] [Accepted: 12/14/2021] [Indexed: 06/14/2023]
Abstract
Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel's concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.
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Affiliation(s)
- Łukasz Huminiecki
- Evolutionary, Computational, and Statistical Genetics, Department of Molecula Biology, Institute of Genetics and Animal Biotechnology, Polish Academy of Sciences, Postępu 36A, Jastrzębiec, 05-552 Warsaw, Poland
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40
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Instance Segmentation for Governmental Inspection of Small Touristic Infrastructure in Beach Zones Using Multispectral High-Resolution WorldView-3 Imagery. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2021. [DOI: 10.3390/ijgi10120813] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Misappropriation of public lands is an ongoing government concern. In Brazil, the beach zone is public property, but many private establishments use it for economic purposes, requiring constant inspection. Among the undue targets, the individual mapping of straw beach umbrellas (SBUs) attached to the sand is a great challenge due to their small size, high presence, and agglutinated appearance. This study aims to automatically detect and count SBUs on public beaches using high-resolution images and instance segmentation, obtaining pixel-wise semantic information and individual object detection. This study is the first instance segmentation application on coastal areas and the first using WorldView-3 (WV-3) images. We used the Mask-RCNN with some modifications: (a) multispectral input for the WorldView3 imagery (eight channels), (b) improved the sliding window algorithm for large image classification, and (c) comparison of different image resizing ratios to improve small object detection since the SBUs are small objects (<322 pixels) even using high-resolution images (31 cm). The accuracy analysis used standard COCO metrics considering the original image and three scale ratios (2×, 4×, and 8× resolution increase). The average precision (AP) results increased proportionally to the image resolution: 30.49% (original image), 48.24% (2×), 53.45% (4×), and 58.11% (8×). The 8× model presented 94% AP50, classifying nearly all SBUs correctly. Moreover, the improved sliding window approach enables the classification of large areas providing automatic counting and estimating the size of the objects, proving to be effective for inspecting large coastal areas and providing insightful information for public managers. This remote sensing application impacts the inspection cost, tribute, and environmental conditions.
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41
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Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data. Biomedicines 2021; 9:biomedicines9111733. [PMID: 34829962 PMCID: PMC8615388 DOI: 10.3390/biomedicines9111733] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/26/2021] [Accepted: 11/17/2021] [Indexed: 12/25/2022] Open
Abstract
Deep learning (DL) is a distinct class of machine learning that has achieved first-class performance in many fields of study. For epigenomics, the application of DL to assist physicians and scientists in human disease-relevant prediction tasks has been relatively unexplored until very recently. In this article, we critically review published studies that employed DL models to predict disease detection, subtype classification, and treatment responses, using epigenomic data. A comprehensive search on PubMed, Scopus, Web of Science, Google Scholar, and arXiv.org was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Among 1140 initially identified publications, we included 22 articles in our review. DNA methylation and RNA-sequencing data are most frequently used to train the predictive models. The reviewed models achieved a high accuracy ranged from 88.3% to 100.0% for disease detection tasks, from 69.5% to 97.8% for subtype classification tasks, and from 80.0% to 93.0% for treatment response prediction tasks. We generated a workflow to develop a predictive model that encompasses all steps from first defining human disease-related tasks to finally evaluating model performance. DL holds promise for transforming epigenomic big data into valuable knowledge that will enhance the development of translational epigenomics.
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42
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Westerlund AM, Hawe JS, Heinig M, Schunkert H. Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence. Int J Mol Sci 2021; 22:10291. [PMID: 34638627 PMCID: PMC8508897 DOI: 10.3390/ijms221910291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/17/2021] [Accepted: 09/18/2021] [Indexed: 12/11/2022] Open
Abstract
Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.
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Affiliation(s)
- Annie M. Westerlund
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
- Institute of Computational Biology, HelmholtzZentrum München, Ingolstädter Landstrasse 1, 85764 Munich, Germany
| | - Johann S. Hawe
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
| | - Matthias Heinig
- Institute of Computational Biology, HelmholtzZentrum München, Ingolstädter Landstrasse 1, 85764 Munich, Germany
- Department of Informatics, Technical University Munich, Boltzmannstrasse 3, 85748 Garching, Germany
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technical University Munich, Lazarettstrasse 36, 80636 Munich, Germany; (A.M.W.); (J.S.H.)
- Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Biedersteiner Strasse 29, 80802 Munich, Germany
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43
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Picard M, Scott-Boyer MP, Bodein A, Périn O, Droit A. Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J 2021; 19:3735-3746. [PMID: 34285775 PMCID: PMC8258788 DOI: 10.1016/j.csbj.2021.06.030] [Citation(s) in RCA: 166] [Impact Index Per Article: 55.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/17/2021] [Accepted: 06/21/2021] [Indexed: 12/25/2022] Open
Abstract
Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems. Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.
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Affiliation(s)
- Milan Picard
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- Corresponding author.
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Cha M, Zheng H, Talukder A, Barham C, Li X, Hu H. A two-stream convolutional neural network for microRNA transcription start site feature integration and identification. Sci Rep 2021; 11:5625. [PMID: 33707582 PMCID: PMC7952457 DOI: 10.1038/s41598-021-85173-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/24/2021] [Indexed: 12/31/2022] Open
Abstract
MicroRNAs (miRNAs) play important roles in post-transcriptional gene regulation and phenotype development. Understanding the regulation of miRNA genes is critical to understand gene regulation. One of the challenges to study miRNA gene regulation is the lack of condition-specific annotation of miRNA transcription start sites (TSSs). Unlike protein-coding genes, miRNA TSSs can be tens of thousands of nucleotides away from the precursor miRNAs and they are hard to be detected by conventional RNA-Seq experiments. A number of studies have been attempted to computationally predict miRNA TSSs. However, high-resolution condition-specific miRNA TSS prediction remains a challenging problem. Recently, deep learning models have been successfully applied to various bioinformatics problems but have not been effectively created for condition-specific miRNA TSS prediction. Here we created a two-stream deep learning model called D-miRT for computational prediction of condition-specific miRNA TSSs ( http://hulab.ucf.edu/research/projects/DmiRT/ ). D-miRT is a natural fit for the integration of low-resolution epigenetic features (DNase-Seq and histone modification data) and high-resolution sequence features. Compared with alternative computational models on different sets of training data, D-miRT outperformed all baseline models and demonstrated high accuracy for condition-specific miRNA TSS prediction tasks. Comparing with the most recent approaches on cell-specific miRNA TSS identification using cell lines that were unseen to the model training processes, D-miRT also showed superior performance.
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Affiliation(s)
- Mingyu Cha
- Department of Computer Science, University of Central Florida, Orlando, FL, USA
| | - Hansi Zheng
- Department of Computer Science, University of Central Florida, Orlando, FL, USA
| | - Amlan Talukder
- Department of Computer Science, University of Central Florida, Orlando, FL, USA
| | - Clayton Barham
- Department of Computer Science, University of Central Florida, Orlando, FL, USA
| | - Xiaoman Li
- Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, FL, USA.
| | - Haiyan Hu
- Department of Computer Science, University of Central Florida, Orlando, FL, USA.
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Oh VKS, Li RW. Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data. Genes (Basel) 2021; 12:352. [PMID: 33673721 PMCID: PMC7997275 DOI: 10.3390/genes12030352] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 02/06/2023] Open
Abstract
Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.
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Affiliation(s)
- Vera-Khlara S. Oh
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
- Department of Computer Science and Statistics, College of Natural Sciences, Jeju National University, Jeju City 63243, Korea
| | - Robert W. Li
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
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Bruno P, Calimeri F, Greco G. AIM in Medical Informatics. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_32-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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