HopPER: an adaptive model for probability estimation of influenza reassortment through host prediction

Yin, Rui; Zhou, Xinrui; Rashid, Shamima; Kwoh, Chee Keong

doi:10.1186/s12920-019-0656-7

Cited by 16 publications

(9 citation statements)

References 52 publications

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“…integrated a machine learning model using least general generalization algorithm combined with yield stress, viscoelasticity, and shape fidelity from using various type I collagen-based bio-inks[ 190 ]. By separating the class variables into shape fidelity and extrusion, the machine learning algorithm effectively optimized the composite bio-ink material fraction and subsequent printing performance[ 191 , 192 ]. Current applications of 3D bioprinting based machine learning algorithms are currently geared towards using regressive models such as LASSO; however, a potential avenue of integrating advanced learning systems using generative ensembles or Bayesian approaches in producing highest performing inks of spheroidal assembly remains completely untapped.…”

Section: Outlooks and Challengesmentioning

confidence: 99%

Using Spheroids as Building Blocks Towards 3D Bioprinting of Tumor Microenvironment

Zhuang¹,

Chiang²,

Fernanda³

et al. 2024

IJB

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Cancer still ranks as a leading cause of mortality worldwide. Although considerable efforts have been dedicated to anticancer therapeutics, progress is still slow, partially due to the absence of robust prediction models. Multicellular tumor spheroids, as a major three-dimensional (3D) culture model exhibiting features of avascular tumors, gained great popularity in pathophysiological studies and high throughput drug screening. However, limited control over cellular and structural organization is still the key challenge in achieving in vivo like tissue microenvironment. 3D bioprinting has made great strides toward tissue/organ mimicry, due to its outstanding spatial control through combining both cells and materials, scalability, and reproducibility. Prospectively, harnessing the power from both 3D bioprinting and multicellular spheroids would likely generate more faithful tumor models and advance our understanding on the mechanism of tumor progression. In this review, the emerging concept on using spheroids as a building block in 3D bioprinting for tumor modeling is illustrated. We begin by describing the context of the tumor microenvironment, followed by an introduction of various methodologies for tumor spheroid formation, with their specific merits and drawbacks. Thereafter, we present an overview of existing 3D printed tumor models using spheroids as a focus. We provide a compilation of the contemporary literature sources and summarize the overall advancements in technology and possibilities of using spheroids as building blocks in 3D printed tissue modeling, with a particular emphasis on tumor models. Future outlooks about the wonderous advancements of integrated 3D spheroidal printing conclude this review.

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Section: Outlooks and Challengesmentioning

confidence: 99%

Using Spheroids as Building Blocks Towards 3D Bioprinting of Tumor Microenvironment

Zhuang¹,

Chiang²,

Fernanda³

et al. 2024

IJB

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“…A bioinformatic approach named host-prediction-based probability estimation of reassortment (HopPER) estimates the reassortment probabilities of influenza viruses through host tropism prediction using 147 new features generated from seven physicochemical properties of amino acids [123].…”

Section: Monitoring and Bioinformatic Prediction Of Aiv Strains With Zoonotic Potentialmentioning

confidence: 99%

Avian Influenza in Wild Birds and Poultry: Dissemination Pathways, Monitoring Methods, and Virus Ecology

et al. 2021

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Avian influenza is one of the largest known threats to domestic poultry. Influenza outbreaks on poultry farms typically lead to the complete slaughter of the entire domestic bird population, causing severe economic losses worldwide. Moreover, there are highly pathogenic avian influenza (HPAI) strains that are able to infect the swine or human population in addition to their primary avian host and, as such, have the potential of being a global zoonotic and pandemic threat. Migratory birds, especially waterfowl, are a natural reservoir of the avian influenza virus; they carry and exchange different virus strains along their migration routes, leading to antigenic drift and antigenic shift, which results in the emergence of novel HPAI viruses. This requires monitoring over time and in different locations to allow for the upkeep of relevant knowledge on avian influenza virus evolution and the prevention of novel epizootic and epidemic outbreaks. In this review, we assess the role of migratory birds in the spread and introduction of influenza strains on a global level, based on recent data. Our analysis sheds light on the details of viral dissemination linked to avian migration, the viral exchange between migratory waterfowl and domestic poultry, virus ecology in general, and viral evolution as a process tightly linked to bird migration. We also provide insight into methods used to detect and quantify avian influenza in the wild. This review may be beneficial for the influenza research community and may pave the way to novel strategies of avian influenza and HPAI zoonosis outbreak monitoring and prevention.

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“…Machine learning has been utilized in many aspects of viral genomic analysis, e.g., antigenicity prediction of viruses [21], genome classification of novel pathogens [43], reassortment detection [44], receptor binding analysis [45] [48], VGG [49] and ResNet [50].…”

Section: Model Constructionmentioning

confidence: 99%

Alignment-free machine learning approaches for the lethality prediction of potential novel human-adapted coronavirus using genomic nucleotide

Yin

Luo

Kwoh

2020

Preprint

Self Cite

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A newly emerging novel coronavirus appeared and rapidly spread worldwide and World Health Organization declared a pandemic on March 11, 2020. The roles and characteristics of coronavirus have captured much attention due to its power of causing a wide variety of infectious diseases, from mild to severe on humans. The detection of the lethality of human coronavirus is key to estimate the viral toxicity and provide perspective for treatment. We developed alignment-free machine learning approaches for an ultra-fast and highly accurate prediction of the lethality of potential human-adapted coronavirus using genomic nucleotide. We performed extensive experiments through six different feature transformation and machine learning algorithms in combination with digital signal processing to infer the lethality of possible future novel coronaviruses using previous existing strains. The results tested on SARS-CoV, MERS-Cov and SARS-CoV-2 datasets show an average 96.7% prediction accuracy. We also provide preliminary analysis validating the effectiveness of our models through other human coronaviruses. Our study achieves high levels of prediction performance based on raw RNA sequences alone without genome annotations and specialized biological knowledge. The results demonstrate that, for any novel human coronavirus strains, this alignment-free machine learning-based approach can offer a reliable real-time estimation for its viral lethality.

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HopPER: an adaptive model for probability estimation of influenza reassortment through host prediction

Cited by 16 publications

References 52 publications

Using Spheroids as Building Blocks Towards 3D Bioprinting of Tumor Microenvironment

Using Spheroids as Building Blocks Towards 3D Bioprinting of Tumor Microenvironment

Avian Influenza in Wild Birds and Poultry: Dissemination Pathways, Monitoring Methods, and Virus Ecology

Alignment-free machine learning approaches for the lethality prediction of potential novel human-adapted coronavirus using genomic nucleotide

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