Hierarchical Rank Aggregation with Applications to Nanotoxicology

Patel, Trina; Telesca, Donatello; Ralló, Robert; George, Saji; Xia, Tian; Nel, André E.

doi:10.1007/s13253-013-0129-y

Cited by 14 publications

(18 citation statements)

References 28 publications

(50 reference statements)

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“…183,184,186 However, the ability to automate the capture and analysis of the fluorescent images of thousands of cells has made fluorescence microscopy an additional tool for systematic cell biology investigations, and also applicable for investigating nanobiology. 183,184,186 However, the ability to automate the capture and analysis of the fluorescent images of thousands of cells has made fluorescence microscopy an additional tool for systematic cell biology investigations, and also applicable for investigating nanobiology.…”

Section: The Power Of the Np-corona In In Silico Modellingmentioning

confidence: 99%

“…62,82,183,184,186 Each NP can then be characterized by a reactivity profile P NP = {Rdca}, in which each feature is the normalized assay result Rdca that emerges when the NP is added at dose d to cell type c, and its effect is measured using assay a. 62,82,183,184,186 Each NP can then be characterized by a reactivity profile P NP = {Rdca}, in which each feature is the normalized assay result Rdca that emerges when the NP is added at dose d to cell type c, and its effect is measured using assay a.…”

Section: The Power Of the Np-corona In In Silico Modellingmentioning

confidence: 99%

“…62,82,183,184,[186][187][188] Importantly, recent developments have aimed to combine and mine Fig. Thus, analytical and experimental data delivered by proteomic and in vitro HTS profiling experiments, can be collected in a data repository.…”

Section: The Power Of the Np-corona In In Silico Modellingmentioning

confidence: 99%

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The nanoparticle biomolecule corona: lessons learned – challenge accepted?

et al. 2015

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Besides the wide use of engineered nanomaterials (NMs) in technical products, their applications are not only increasing in biotechnology and biomedicine, but also in the environmental field. While the physico-chemical properties and behaviour of NMs can be characterized accurately under idealized conditions, this is no longer the case in complex physiological or natural environments. Herein, proteins and other biomolecules rapidly bind to NMs, forming a protein/biomolecule corona that critically affects the NMs' (patho)biological and technical identities. As the corona impacts the in vitro and/or in vivo NM applications in humans and ecosystems, a mechanistic understanding of its relevance and of the biophysical forces regulating corona formation is mandatory. Based on recent insights, we here critically review and present an updated concept of corona formation and evolution. We comment on how corona signatures may be linked to effects at the nano-bio interface in physiological and environmental systems. In order to comprehensively analyse corona profiles and to mechanistically understand the coronas' biological/ecological impact, we present a tiered multidisciplinary approach. To stimulate progress in this field, we introduce the potential impact of the corona for NM-microbiome-(human)host interactions and the novel concept of 'nanologicals', i.e., the nanomaterial-specific targeting of molecular machines. We conclude by discussing the relevant challenges that still need to be resolved in this field.

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Section: The Power Of the Np-corona In In Silico Modellingmentioning

confidence: 99%

Section: The Power Of the Np-corona In In Silico Modellingmentioning

confidence: 99%

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The nanoparticle biomolecule corona: lessons learned – challenge accepted?

et al. 2015

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“…Cell based HCS has evolved dramatically, allowing HTS applications to measure the responses of cells to chemicals and, as recently described, also to NPs (36,85,(120)(121)(122). The concept and technological execution of such assay systems have been reviewed (120,121,123), albeit the impact of the biomolecule corona was not specifically addressed so far.…”

Section: Systematic Strategies To Dissect the Impact Of Nm-coronas Onmentioning

confidence: 99%

“…The workflows used predominantly for such HCS/HTS screening approaches mostly focused on classical plate reader-based assays using biochemical readouts (120,121,123). However, the ability to automate the capture A minimum of 500 cells were analyzed per well, and each treatment was done in triplicate.…”

Section: Systematic Strategies To Dissect the Impact Of Nm-coronas Onmentioning

confidence: 99%

The bio-corona and its impact on nanomaterial toxicity

Westmeier

Chen²,

Stauber

et al. 2015

European Journal of Nanomedicine

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Abstract:The rapidly growing application of nanosized materials and nano-scaled processes will result in increased exposure of humans and the environment. The small size of nanomaterials (NM) comparable with molecular building blocks of cells raises concerns that their toxic potential cannot be extrapolated from studies of larger particles due to their unique physico-chemical properties. These properties are also responsible that NM rapidly adsorb various (bio)molecules when introduced into complex physiological or natural environments. As the thus formed protein/biomolecule 'corona' seems to affect the NM' in situ identity, an understanding of its toxicological relevance and the biophysical forces regulating corona formation is needed but not yet achieved. This review introduces our current concept of corona formation and evolution and present analytical methods for corona profiling. We discuss toxicity mechanisms potentially affected by the biomolecule corona, including NM cellular uptake and impact on components of the blood system. Further, we comment on pending knowledge gaps and challenges, which need to be resolved by the field. We conclude by presenting a tiered systems biology-driven approach recommended to mechanistically understand the coronas' nanotoxicological relevance and predictive potential.

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A review on instance ranking problems in statistical learning

Werner

2021

Mach Learn

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Ranking problems, also known as preference learning problems, define a widely spread class of statistical learning problems with many applications, including fraud detection, document ranking, medicine, chemistry, credit risk screening, image ranking or media memorability. While there already exist reviews concentrating on specific types of ranking problems like label and object ranking problems, there does not yet seem to exist an overview concentrating on instance ranking problems that both includes developments in distinguishing between different types of instance ranking problems as well as careful discussions about their differences and the applicability of the existing ranking algorithms to them. In instance ranking, one explicitly takes the responses into account with the goal to infer a scoring function which directly maps feature vectors to real-valued ranking scores, in contrast to object ranking problems where the ranks are given as preference information with the goal to learn a permutation. In this article, we systematically review different types of instance ranking problems and the corresponding loss functions resp. goodness criteria. We discuss the difficulties when trying to optimize those criteria. As for a detailed and comprehensive overview of existing machine learning techniques to solve such ranking problems, we systematize existing techniques and recapitulate the corresponding optimization problems in a unified notation. We also discuss to which of the instance ranking problems the respective algorithms are tailored and identify their strengths and limitations. Computational aspects and open research problems are also considered.

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Hierarchical Rank Aggregation with Applications to Nanotoxicology

Cited by 14 publications

References 28 publications

The nanoparticle biomolecule corona: lessons learned – challenge accepted?

The nanoparticle biomolecule corona: lessons learned – challenge accepted?

The bio-corona and its impact on nanomaterial toxicity

A review on instance ranking problems in statistical learning

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