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Toward Cell-Specific Nanoparticle De...

Murphy, Sean,

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Toward Cell-Specific Nanoparticle Delivery Systems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Toward Cell-Specific Nanoparticle Delivery Systems // Sean Murphy.
作者:	Murphy, Sean,
面頁冊數:	1 electronic resource (47 pages)
附註:	Source: Masters Abstracts International, Volume: 86-08.
Contained By:	Masters Abstracts International86-08.
標題:	Cancer. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31851365
ISBN:	9798304954594

Toward Cell-Specific Nanoparticle Delivery Systems /
Murphy, Sean,

Toward Cell-Specific Nanoparticle Delivery Systems /Sean Murphy. - 1 electronic resource (47 pages)

Source: Masters Abstracts International, Volume: 86-08.

The targetable delivery of therapeutic nanoparticles remains a significant challenge in modern medicine, particularly due to the complexity, time, and expense involved in experimental design and optimization for cell-specific applications. To address this, NOCAP (Nanoparticle Optimization and Cell Affinity Prediction) was developed, a computational framework designed to (i) predict the affinities between nanoparticles and gene expression signatures of cancer cells and (ii) optimize nanoparticle formulations for specific targets. NOCAP successfully predicts cellular affinity for previously unseen cancer cell lines. The findings demonstrate the potential of machine learning to streamline the rational selection of target-specific nanoparticle drug delivery systems, paving the way for more efficient and precise therapeutic interventions.

English

ISBN: 9798304954594Subjects--Topical Terms:

634186
Cancer.
Subjects--Index Terms:

Therapeutic nanoparticles

Toward Cell-Specific Nanoparticle Delivery Systems /
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520 $a The targetable delivery of therapeutic nanoparticles remains a significant challenge in modern medicine, particularly due to the complexity, time, and expense involved in experimental design and optimization for cell-specific applications. To address this, NOCAP (Nanoparticle Optimization and Cell Affinity Prediction) was developed, a computational framework designed to (i) predict the affinities between nanoparticles and gene expression signatures of cancer cells and (ii) optimize nanoparticle formulations for specific targets. NOCAP successfully predicts cellular affinity for previously unseen cancer cell lines. The findings demonstrate the potential of machine learning to streamline the rational selection of target-specific nanoparticle drug delivery systems, paving the way for more efficient and precise therapeutic interventions.
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650 4 $a Cancer. $3 634186
650 4 $a Cells. $3 634126
650 4 $a Gene expression. $3 643979
650 4 $a Large intestine. $3 3821768
650 4 $a Toxicity. $3 3564836
650 4 $a Drug delivery systems. $3 657987
650 4 $a Surface chemistry. $3 551428
650 4 $a Nanoparticles. $3 605747
650 4 $a Statistical inference. $3 3700481
650 4 $a Tumors. $3 893964
650 4 $a Biochemistry. $3 518028
650 4 $a Nanotechnology. $3 526235
653 $a Therapeutic nanoparticles
653 $a Cell-specific applications
653 $a Cancer cells
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792 $a 2024
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