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machine learning

  • Open Access
    Machine Learning Reveals Time-Varying Microbial Predictors with Complex Effects on Glucose Regulation
    Research Article | Host-Microbe Biology
    Machine Learning Reveals Time-Varying Microbial Predictors with Complex Effects on Glucose Regulation

    Recent studies have shown a clear link between gut microbiota and type 2 diabetes. However, current results are based on cross-sectional studies that aim to determine the microbial dysbiosis when the disease is already prevalent.

    Oliver Aasmets, Kreete Lüll, Jennifer M. Lang, Calvin Pan, Johanna Kuusisto, Krista Fischer, Markku Laakso, Aldons J. Lusis, Elin Org
  • Open Access
    Assessing Biodegradability of Chemical Compounds from Microbial Community Growth Using Flow Cytometry
    Research Article | Applied and Environmental Science
    Assessing Biodegradability of Chemical Compounds from Microbial Community Growth Using Flow Cytometry

    The manifold effects of potentially toxic compounds on microbial communities are often difficult to discern. Some compounds may be transformed or completely degraded by few or multiple strains in the community, whereas others may present inhibitory effects.

    B. D. Özel Duygan, S. Rey, S. Leocata, L. Baroux, M. Seyfried, J. R. van der Meer
  • Open Access
    Harnessing Machine Learning To Unravel Protein Degradation in <span class="named-content genus-species" id="named-content-1">Escherichia coli</span>
    Research Article | Molecular Biology and Physiology
    Harnessing Machine Learning To Unravel Protein Degradation in Escherichia coli

    Bacteria use protein degradation to control proliferation, dispose of misfolded proteins, and adapt to physiological and environmental shifts, but the factors that dictate which proteins are prone to degradation are mostly unknown. In this study, we have used a combined computational-experimental approach to explore protein degradation in E. coli.

    Natan Nagar, Noa Ecker, Gil Loewenthal, Oren Avram, Daniella Ben-Meir, Dvora Biran, Eliora Ron, Tal Pupko
  • Open Access
    Human Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors
    Research Article | Host-Microbe Biology
    Human Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors

    Identification of HIV-1 HDFs remains a crucial step to understand the complicated relationships between human and HIV-1. To complement the experimental identification of HDFs, we have implemented an existing network-based gene discovery strategy to predict HDFs from the human genome. The core idea of the proposed method is that the rich information deposited in host gene functional networks can be effectively utilized to infer the...

    Chen Fu, Shiping Yang, Xiaodi Yang, Xianyi Lian, Yan Huang, Xiaobao Dong, Ziding Zhang
  • Open Access
    The Gut Microbiome and Individual-Specific Responses to Diet
    Sponsored Content Minireview | Clinical Science and Epidemiology
    The Gut Microbiome and Individual-Specific Responses to Diet

    Nutritional content and timing are increasingly appreciated to constitute important human variables collectively impacting all aspects of human physiology and disease. However, person-specific mechanisms driving nutritional impacts on the human host remain incompletely understood, while current dietary recommendations remain empirical and nonpersonalized. Precision nutrition aims to harness individualized bodies of data, including the...

    Avner Leshem, Eran Segal, Eran Elinav
  • Open Access
    Genome-Wide Analysis of RNA Decay in the Cyanobacterium <em>Synechococcus</em> sp. Strain PCC 7002
    Research Article | Molecular Biology and Physiology
    Genome-Wide Analysis of RNA Decay in the Cyanobacterium Synechococcus sp. Strain PCC 7002

    RNA degradation is an important process that affects the final concentration of individual mRNAs, affecting protein expression and cellular physiology. Studies of how RNA is degraded increase our knowledge of this fundamental process as well as enable the creation of genetic tools to manipulate RNA stability. By studying global transcript turnover, we searched for sequence elements that correlated with transcript (in)stability and used...

    Gina C. Gordon, Jeffrey C. Cameron, Sanjan T. P. Gupta, Michael D. Engstrom, Jennifer L. Reed, Brian F. Pfleger
  • Open Access
    T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors
    Research Article | Host-Microbe Biology
    T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors

    Type III secreted effector (T3SE) prediction remains a big computational challenge. In practical applications, current software tools often suffer problems of high false-positive rates. One of the causal factors could be the relatively unitary type of biological features used for the design and training of the models. In this research, we made a comprehensive survey on the sequence-based features of T3SEs, including signal sequences,...

    Xinjie Hui, Zewei Chen, Mingxiong Lin, Junya Zhang, Yueming Hu, Yingying Zeng, Xi Cheng, Le Ou-Yang, Ming-an Sun, Aaron P. White, Yejun Wang
  • Open Access
    Predicting Phenotypic Polymyxin Resistance in <span class="named-content genus-species" id="named-content-1">Klebsiella pneumoniae</span> through Machine Learning Analysis of Genomic Data
    Research Article | Therapeutics and Prevention
    Predicting Phenotypic Polymyxin Resistance in Klebsiella pneumoniae through Machine Learning Analysis of Genomic Data

    Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection...

    Nenad Macesic, Oliver J. Bear Don’t Walk, Itsik Pe’er, Nicholas P. Tatonetti, Anton Y. Peleg, Anne-Catrin Uhlemann
  • Open Access
    Interpretable Log Contrasts for the Classification of Health Biomarkers: a New Approach to Balance Selection
    Research Article | Host-Microbe Biology
    Interpretable Log Contrasts for the Classification of Health Biomarkers: a New Approach to Balance Selection

    High-throughput sequencing provides an easy and cost-effective way to measure the relative abundance of bacteria in any environmental or biological sample. When these samples come from humans, the microbiome signatures can act as biomarkers for disease prediction. However, because bacterial abundance is measured as a composition, the data have unique properties that make conventional analyses inappropriate. To overcome this, analysts...

    Thomas P. Quinn, Ionas Erb
  • Open Access
    Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks
    Research Article | Clinical Science and Epidemiology
    Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks

    Machine learning is a proven method to predict AMR; however, the performance of any machine learning model depends on the quality of the input data. Therefore, we evaluated different methods of representing information about mutations as well as mobilizable genes, so that the information can serve as input for a robust model. We combined data from multiple bacterial species in order to develop species-independent machine learning models...

    D. Aytan-Aktug, P. T. L. C. Clausen, V. Bortolaia, F. M. Aarestrup, O. Lund

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