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Méndez-Cruz CF, Rodríguez-Herrera J, Varela-Vega A, Mateo-Estrada V, Castillo-Ramírez S. Unsupervised learning and natural language processing highlight research trends in a superbug. Front Artif Intell 2024; 7:1336071. [PMID: 38576460 PMCID: PMC10991725 DOI: 10.3389/frai.2024.1336071] [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: 11/13/2023] [Accepted: 03/11/2024] [Indexed: 04/06/2024] Open
Abstract
Introduction Antibiotic-resistant Acinetobacter baumannii is a very important nosocomial pathogen worldwide. Thousands of studies have been conducted about this pathogen. However, there has not been any attempt to use all this information to highlight the research trends concerning this pathogen. Methods Here we use unsupervised learning and natural language processing (NLP), two areas of Artificial Intelligence, to analyse the most extensive database of articles created (5,500+ articles, from 851 different journals, published over 3 decades). Results K-means clustering found 113 theme clusters and these were defined with representative terms automatically obtained with topic modelling, summarising different research areas. The biggest clusters, all with over 100 articles, are biased toward multidrug resistance, carbapenem resistance, clinical treatment, and nosocomial infections. However, we also found that some research areas, such as ecology and non-human infections, have received very little attention. This approach allowed us to study research themes over time unveiling those of recent interest, such as the use of Cefiderocol (a recently approved antibiotic) against A. baumannii. Discussion In a broader context, our results show that unsupervised learning, NLP and topic modelling can be used to describe and analyse the research themes for important infectious diseases. This strategy should be very useful to analyse other ESKAPE pathogens or any other pathogens relevant to Public Health.
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Affiliation(s)
- Carlos-Francisco Méndez-Cruz
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Joel Rodríguez-Herrera
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Alfredo Varela-Vega
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Valeria Mateo-Estrada
- Programa de Genómica Evolutiva, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Santiago Castillo-Ramírez
- Programa de Genómica Evolutiva, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
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Ensemble of Support Vector Machine and Ontological Structures to Generate Abstractive Text Summarization. INTERNATIONAL JOURNAL OF INFORMATION RETRIEVAL RESEARCH 2022. [DOI: 10.4018/ijirr.300294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Automatic summarization systems are much needed to lessen the information overload which is being faced by people due to exponential growth of data on World Wide Web. These systems choose the most significant part of the text from a single document or multiple documents and present the compressed surrogate form of the complete information which was intended to be conveyed. In this research paper, we propose an approach to generate summary from a given text first by extracting the most relevant sentences and then making further concise by creating ontological structures of these sentences and then generating the abstractive summary from these structures. Our proposed system is evaluated with DUC 2002 data set and it is found that the performance of this system as evaluated using ROUGE-1 is 58.175 which is better than other state of the art systems. The values reported in the experimental process of the research report the significant contribution of this innovative method.
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Méndez-Cruz CF, Blanchet A, Godínez A, Arroyo-Fernández I, Gama-Castro S, Martínez-Luna SB, González-Colín C, Collado-Vides J. Knowledge extraction for assisted curation of summaries of bacterial transcription factor properties. Database (Oxford) 2020; 2020:baaa109. [PMID: 33306798 PMCID: PMC7731926 DOI: 10.1093/database/baaa109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 11/18/2020] [Accepted: 11/26/2020] [Indexed: 11/21/2022]
Abstract
Transcription factors (TFs) play a main role in transcriptional regulation of bacteria, as they regulate transcription of the genetic information encoded in DNA. Thus, the curation of the properties of these regulatory proteins is essential for a better understanding of transcriptional regulation. However, traditional manual curation of article collections to compile descriptions of TF properties takes significant time and effort due to the overwhelming amount of biomedical literature, which increases every day. The development of automatic approaches for knowledge extraction to assist curation is therefore critical. Here, we show an effective approach for knowledge extraction to assist curation of summaries describing bacterial TF properties based on an automatic text summarization strategy. We were able to recover automatically a median 77% of the knowledge contained in manual summaries describing properties of 177 TFs of Escherichia coli K-12 by processing 5961 scientific articles. For 71% of the TFs, our approach extracted new knowledge that can be used to expand manual descriptions. Furthermore, as we trained our predictive model with manual summaries of E. coli, we also generated summaries for 185 TFs of Salmonella enterica serovar Typhimurium from 3498 articles. According to the manual curation of 10 of these Salmonella typhimurium summaries, 96% of their sentences contained relevant knowledge. Our results demonstrate the feasibility to assist manual curation to expand manual summaries with new knowledge automatically extracted and to create new summaries of bacteria for which these curation efforts do not exist. Database URL: The automatic summaries of the TFs of E. coli and Salmonella and the automatic summarizer are available in GitHub (https://github.com/laigen-unam/tf-properties-summarizer.git).
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Affiliation(s)
- Carlos-Francisco Méndez-Cruz
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av. Universidad s/n, Colonia Chamilpa, Cuernavaca 62100, Morelos, Mexico
| | - Antonio Blanchet
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av. Universidad s/n, Colonia Chamilpa, Cuernavaca 62100, Morelos, Mexico
| | - Alan Godínez
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av. Universidad s/n, Colonia Chamilpa, Cuernavaca 62100, Morelos, Mexico
| | - Ignacio Arroyo-Fernández
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av. Universidad s/n, Colonia Chamilpa, Cuernavaca 62100, Morelos, Mexico
- División de Posgrado, Universidad Tecnológica de la Mixteca, Carretera a Acatlima Km. 2.5, Huajuapan de León, 69000, Oaxaca, Mexico
| | - Socorro Gama-Castro
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av. Universidad s/n, Colonia Chamilpa, Cuernavaca 62100, Morelos, Mexico
| | - Sara Berenice Martínez-Luna
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av. Universidad s/n, Colonia Chamilpa, Cuernavaca 62100, Morelos, Mexico
| | - Cristian González-Colín
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av. Universidad s/n, Colonia Chamilpa, Cuernavaca 62100, Morelos, Mexico
| | - Julio Collado-Vides
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av. Universidad s/n, Colonia Chamilpa, Cuernavaca 62100, Morelos, Mexico
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Room 403, Boston, 02215 MA, USA
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Santos-Zavaleta A, Salgado H, Gama-Castro S, Sánchez-Pérez M, Gómez-Romero L, Ledezma-Tejeida D, García-Sotelo JS, Alquicira-Hernández K, Muñiz-Rascado LJ, Peña-Loredo P, Ishida-Gutiérrez C, Velázquez-Ramírez DA, Del Moral-Chávez V, Bonavides-Martínez C, Méndez-Cruz CF, Galagan J, Collado-Vides J. RegulonDB v 10.5: tackling challenges to unify classic and high throughput knowledge of gene regulation in E. coli K-12. Nucleic Acids Res 2020; 47:D212-D220. [PMID: 30395280 PMCID: PMC6324031 DOI: 10.1093/nar/gky1077] [Citation(s) in RCA: 225] [Impact Index Per Article: 56.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 10/19/2018] [Indexed: 01/31/2023] Open
Abstract
RegulonDB, first published 20 years ago, is a comprehensive electronic resource about regulation of transcription initiation of Escherichia coli K-12 with decades of knowledge from classic molecular biology experiments, and recently also from high-throughput genomic methodologies. We curated the literature to keep RegulonDB up to date, and initiated curation of ChIP and gSELEX experiments. We estimate that current knowledge describes between 10% and 30% of the expected total number of transcription factor- gene regulatory interactions in E. coli. RegulonDB provides datasets for interactions for which there is no evidence that they affect expression, as well as expression datasets. We developed a proof of concept pipeline to merge binding and expression evidence to identify regulatory interactions. These datasets can be visualized in the RegulonDB JBrowse. We developed the Microbial Conditions Ontology with a controlled vocabulary for the minimal properties to reproduce an experiment, which contributes to integrate data from high throughput and classic literature. At a higher level of integration, we report Genetic Sensory-Response Units for 200 transcription factors, including their regulation at the metabolic level, and include summaries for 70 of them. Finally, we summarize our research with Natural language processing strategies to enhance our biocuration work.
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Affiliation(s)
- Alberto Santos-Zavaleta
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | - Heladia Salgado
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | - Socorro Gama-Castro
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | - Mishael Sánchez-Pérez
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | - Laura Gómez-Romero
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | - Daniela Ledezma-Tejeida
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | | | - Kevin Alquicira-Hernández
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | - Luis José Muñiz-Rascado
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | - Pablo Peña-Loredo
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | - Cecilia Ishida-Gutiérrez
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | - David A Velázquez-Ramírez
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | - Víctor Del Moral-Chávez
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | - César Bonavides-Martínez
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México
| | | | - James Galagan
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Julio Collado-Vides
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México.,Department of Biomedical Engineering, Boston University, Boston, MA, USA
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Wang Y, Cho DY, Lee H, Fear J, Oliver B, Przytycka TM. Reprogramming of regulatory network using expression uncovers sex-specific gene regulation in Drosophila. Nat Commun 2018; 9:4061. [PMID: 30283019 PMCID: PMC6170494 DOI: 10.1038/s41467-018-06382-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 08/13/2018] [Indexed: 02/07/2023] Open
Abstract
Gene regulatory networks (GRNs) describe regulatory relationships between transcription factors (TFs) and their target genes. Computational methods to infer GRNs typically combine evidence across different conditions to infer context-agnostic networks. We develop a method, Network Reprogramming using EXpression (NetREX), that constructs a context-specific GRN given context-specific expression data and a context-agnostic prior network. NetREX remodels the prior network to obtain the topology that provides the best explanation for expression data. Because NetREX utilizes prior network topology, we also develop PriorBoost, a method that evaluates a prior network in terms of its consistency with the expression data. We validate NetREX and PriorBoost using the "gold standard" E. coli GRN from the DREAM5 network inference challenge and apply them to construct sex-specific Drosophila GRNs. NetREX constructed sex-specific Drosophila GRNs that, on all applied measures, outperform networks obtained from other methods indicating that NetREX is an important milestone toward building more accurate GRNs.
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Affiliation(s)
- Yijie Wang
- National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, 20894, USA
| | - Dong-Yeon Cho
- National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, 20894, USA
| | - Hangnoh Lee
- Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, 50 South Drive, Bethesda, MD, 20892, USA
| | - Justin Fear
- Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, 50 South Drive, Bethesda, MD, 20892, USA
| | - Brian Oliver
- Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, 50 South Drive, Bethesda, MD, 20892, USA.
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, 20894, USA.
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Yang CD, Huang HY, Shrestha S, Chen YH, Huang HD, Tseng CP. Large-Scale Functional Analysis of CRP-Mediated Feed-Forward Loops. Int J Mol Sci 2018; 19:ijms19082335. [PMID: 30096859 PMCID: PMC6121374 DOI: 10.3390/ijms19082335] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 07/23/2018] [Accepted: 08/02/2018] [Indexed: 11/16/2022] Open
Abstract
Feed-forward loops (FFLs) represent an important and basic network motif to understand specific biological functions. Cyclic-AMP (cAMP) receptor protein (CRP), a transcription factor (TF), mediates catabolite repression and regulates more than 400 genes in response to changes in intracellular concentrations of cAMP in Escherichia coli. CRP participates in some FFLs, such as araBAD and araFGH operons and adapts to fluctuating environmental nutrients, thereby enhancing the survivability of E. coli. Although computational simulations have been conducted to explore the potential functionality of FFLs, a comprehensive study on the functions of all structural types on the basis of in vivo data is lacking. Moreover, the regulatory role of CRP-mediated FFLs (CRP-FFLs) remains obscure. We identified 393 CRP-FFLs in E. coli using EcoCyc and RegulonDB. Dose⁻response genomic microarray of E. coli revealed dynamic gene expression of each target gene of CRP-FFLs in response to a range of cAMP dosages. All eight types of FFLs were present in CRP regulon with various expression patterns of each CRP-FFL, which were further divided into five functional groups. The microarray and reported regulatory relationships identified 202 CRP-FFLs that were directly regulated by CRP in these eight types of FFLs. Interestingly, 34% (147/432) of genes were directly regulated by CRP and CRP-regulated TFs, which indicates that these CRP-regulated genes were also regulated by other CRP-regulated TFs responding to environmental signals through CRP-FFLs. Furthermore, we applied gene ontology annotation to reveal the biological functions of CRP-FFLs.
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Affiliation(s)
- Chi-Dung Yang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli 350, Taiwan.
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan.
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China.
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China.
| | - Hsi-Yuan Huang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China.
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China.
- Department of Laboratory Medicine, China Medical University Hospital, Taichung 420, Taiwan.
| | - Sirjana Shrestha
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan.
| | - Yen-Hua Chen
- Department of Microbiology and Immunology, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA.
| | - Hsien-Da Huang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan.
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China.
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China.
| | - Ching-Ping Tseng
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan.
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