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7th International Conference on Artificial Intelligence and Virtual Reality, AIVR 2023

作者： Rahman, Nurazreen Afiqah A. Nasarudin, Nurul Athirah Mohamad, Mohd Saberi Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medicine and Health Science United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9789819990177

The pathway-based analysis is one method for selecting and classifying genes by incorporating pathway information. Integration of pathway knowledge into microarray data significantly advances researchers in the analysis of complex diseases. Microarray data involves thousands of genes to be selected, and therefore, a suitable method to eliminate noisy and uninformative genes is needed. Selecting significant genes for a specific disease is crucial to identifying genes highly related to disease production. Previous research shows that using both pathway information and gene expression data is more significant in disease identification. Therefore, pathway-based analysis using Support Vector Machine Recursive Feature Elimination (SVM-RFE) is introduced in this research to identify significant genes associated with analyzing the targeted phenotype. The datasets involved in this research are lung cancer and gender dataset. The results from the proposed method performed better than previous work as the significant genes are selected from the highest rank of genes in the highest rank of the pathway. The performance of the proposed method was evaluated using 10-fold cross-validation in terms of accuracy. Finally, a biological validation was conducted on selected genes in the top 5 pathways based on biological literature. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gene expression

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Single-cell transcriptomics and epigenomics point to CD58-CD2 interaction in controlling primary melanoma growth and immunity

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Cancer Communications 2025年第4期45卷 465-470页

作者： Antonia Stubenvoll Maria Schmidt Johanna Moeller Max Alexander Lingner Chango Carolyn Schultz Olga Antoniadou Henry Loeffler-Wirth Stephan Bernhart Florian Groβe Beatrice Thier Annette Paschen Ulf Anderegg Jan CSimon Mirjana Ziemer Clara TSchoeder Hans Binder Manfred Kunz Department of Dermatology Venereology and AllergologyUniversity of Leipzig Medical CenterLeipzigGermany Interdisciplinary Center for Bioinformatics University of LeipzigLeipzigGermany Institute for Drug Discovery University of Leipzig Medical CenterLeipzigGermany Bioinformatics Group Department of Computer ScienceUniversity of LeipzigLeipzigGermany Department of Diagnostics Fraunhofer Institute of Cell Therapy and Immunology(IZI)LeipzigGermany Center for Scalable Data Analytics and Artificial Intelligence(ScaDS.AI)Dresden/Leipzig LeipzigGermany Department of Dermatology University Hospital EssenUniversity Duisburg-Essen and German Cancer Consortium(DKTK)Partner Site Essen/DüsseldorfEssenGermany

Immunotherapy is currently one of the most promising treatment options for malignant melanoma[1].To uncover new immunological targets for future treatment approaches,single-cell transcriptomic and epigenomic analyses ... 详细信息

关键词： malignant melanoma single cell transcriptomics melanocytic nevus immunity immunotherapy primary melanoma epigenomic analyses epigenomics cd cd interaction

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Saccharomyces cerevisiae I-3856 in irritable bowel syndrome with predominant constipation

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World Journal of Gastroenterology 2022年第22期28卷 2509-2522页

作者： Florian Mourey Amélie Decherf Jean-François Jeanne Mathieu Clément-Ziza Marie-Lise Grisoni François Machuron Sophie Legrain-Raspaud Arnaud Bourreille Pierre Desreumaux Research and Applications Department Gnosis by LesaffreLesaffre GroupMarcq-en-Baroeul 59700France Data Science and Bioinformatics Laboratory Research and Development DepartmentLesaffre InternationalLesaffre GroupMarcq-en-Baroeul 59700France Institut des Maladies de l'Appareil Digestif Centre Hospitalier Universitaire de NantesNantes 44093France Institute for Translational Research in Inflammation Centre Hospitalier Universitaire de LilleLille 59000France

BACKGROUND Probiotics are a promising solution for managing irritable bowel syndrome(IBS).Saccharomyces cerevisiae(***)I-3856 has already demonstrated beneficial effects in IBS subjects,particularly in IBS with predominant constipation(IBS-C).AIM To confirm the efficacy of *** I-3856 in the management of gastrointestinal symptoms in *** A randomized,double-blind,placebo-controlled clinical study was performed in a total of 456 *** a run-in period,subjects were randomly assigned to the group receiving *** I-3856(8×109 CFU daily)or the placebo for 8 wk,and they performed daily self-evaluations of gastrointestinal *** primary objective was to assess the effect of the probiotic on abdominal *** secondary objectives were the evaluation of other gastrointestinal symptoms,bowel movement frequency and consistency,and quality of life(QOL).RESULTS A significantly higher proportion of abdominal pain responders was reported in the Probiotic group(45.1%vs 33.9%,P=0.017).A nonsignificant difference in the area under the curve for abdominal pain over the second month of supplementation was observed in subjects receiving probiotic vs placebo[P=0.073,95%CI:-0.59(-1.23;0.05)].No statistically significant differences were reported in the evolution of bowel movement frequency and stool consistency between the *** 8 wk of supplementation,the overall QOL score was significantly higher in the Probiotic group than in the Placebo group[P=0.047,95%CI:3.86(0.52;7.20)].Furthermore,exploratory analyses showed statistically significant and clinically relevant improvements in QOL scores in abdominal pain responders vs *** The results of this clinical study confirmed the abdominal pain alleviation properties of *** I-3856 in *** pain relief was associated with improved *** identifier:NCT03150212.

关键词： Probiotic Yeast Saccharomyces cerevisiae Irritable bowel syndrome Abdominal pain Quality of life

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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MassSpecGym: a benchmark for the discovery and identification of molecules 24

MassSpecGym: a benchmark for the discovery and identificatio...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

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A Short Note on Relevant Cuts

arXiv

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arXiv 2024年

作者： Domschke, Nico Gatter, Thomas Golnik, Richard Stadler, Peter F. Bioinformatics Group Department of Computer Science Leipzig University Härtelstraße 16–18 LeipzigD-04107 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig School of Embedded Composite Artificial Intelligence Leipzig University Härtelstraße 16–18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Department of Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

The set of relevant cuts in a graph is the union of all minimum weight bases of the cut space. We show that a cut is relevant if and only if it is the a minimum weight cut between two distinct vertices. Moreover, we give a characterization in terms of Picard-Queyranne DAGs that can be used to accelerate the enumeration of the relevant cuts. © 2024, CC BY-NC-ND.

关键词： Trees (mathematics)

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Genetic effects of Red Lettuce Leaf genes on red coloration in leaf lettuce under artificial lighting conditions

Plant-Environment Interactions

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Plant-Environment Interactions 2022年第4期3卷 179-192页

作者： Wada, Kaede C. Inagaki, Noritoshi Sakai, Hiroaki Yamashita, Hiroto Nakai, Yusuke Fujimoto, Zui Yonemaru, Jun-ichi Itoh, Hironori Breeding Big Data Management and Utilization Group Division of Smart Breeding Research Institute of Crop Science National Agriculture and Food Research Organization Tsukuba Japan Biomacromolecules Research Unit Research Center for Advanced Analysis Core Technology Research Headquarters National Agriculture and Food Research Organization Tsukuba Japan Bioinformatics Unit Research Center for Advanced Analysis Core Technology Research Headquarters National Agriculture and Food Research Organization Tsukuba Japan Greenhouse Vegetable Production Group Division of Field Crop and Vegetable Research Kyushu-Okinawa Agricultural Research Center National Agriculture and Food Research Organization Kurume Japan

Some cultivars of lettuce accumulate anthocyanins, which act as functional food ingredients. Leaf lettuce has been known to be erratic in exhibiting red color when grown under artificial light, and there is a need for cultivars that more stably exhibit red color in artificial light cultivation. In this study, we aimed to dissect the genetic architecture for red coloring in various leaf lettuce cultivars grown under artificial light. We investigated the genotype of Red Lettuce Leaf (RLL) genes in 133 leaf lettuce strains, some of which were obtained from publicly available resequencing data. By studying the allelic combination of RLL genes, we further analyzed the contribution of these genes to producing red coloring in leaf lettuce. From the quantification of phenolic compounds and corresponding transcriptome data, we revealed that gene expression level-dependent regulation of RLL1 (bHLH) and RLL2 (MYB) is the underlying mechanism conferring high anthocyanin accumulation in red leaf lettuce under artificial light cultivation. Our data suggest that different combinations of RLL genotypes cause quantitative differences in anthocyanin accumulation among cultivars, and some genotype combinations are more effective at producing red coloration even under artificial lighting. © 2022 The Authors. Plant-Environment Interactions published by New Phytologist Foundation and John Wiley & Sons Ltd.

关键词： anthocyanin biosynthesis artificial light type plant factories genotyping leaf lettuce (Lactuca sativa) MBW complex RNA-seq transcription factor transcriptome

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The Occurrence and Genetic Characteristics of Carbapenemase-Producing Enterobacteriaceae Isolated from Shared Water Bodies for Livestock and Human Utilization in Central Thailand

SSRN

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SSRN 2024年

作者： Songsaeng, Wipawee Kurilung, Alongkorn Prapasarakul, Nuvee Wongsurawat, Thidathip Am-In, Nutthee Lugsomya, Kittitat Sirichokchatchawan, Wandee College of Public Health Science Chulalongkorn University Bangkok Thailand Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR United States Department of Veterinary Microbiology Faculty of Veterinary Science Chulalongkorn University Bangkok Thailand Bangkok Thailand Division of Bioinformatics and Data Management for Research Research Group and Research Network Division Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Department of Obstetrics Gynecology and Reproduction Faculty of Veterinary Science Chulalongkorn University Bangkok Thailand Veterinary and Life Science Murdoch University MurdochWA6150 Australia

The environmental dissemination of antimicrobial resistant Enterobacteriaceae poses a remarkable threat to public health. This study investigates the environmental presence and dissemination of carbapenemase-producing Enterobacteriaceae (CPE) in 30 important water bodies selected according to the interconnection and utilization by livestock and community people in central Thailand. From all selected samples, 6 isolates (20%) were identified as multi-drug resistant CPE including one E. coli, one K. pneumoniae and four E. roggemkampii carrying blaNDM-5, blaNDM-1 and blaIMI-1 genes respectively. The blaNDM-5 and blaNDM-1 genes were located on plasmids, p0111 and IncC, while blaIMI-1 was located on chromosomes. The plasmids also consisted of components that closely resembled those found in resistance plasmids obtained from clinical and environmental isolates worldwide. Additionally, through plasmid conjugation experiment, carbapenemase genes were transferable with high rate among bacterial species. These findings indicated that water bodies are polluted and there is an urgent need for integrated strategies to monitor and mitigate the spread of antibiotic resistance across human, animal, and environmental health domains in aquatic environments. © 2024, The Authors. All rights reserved.

关键词： Genes

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