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Biobankers rely on their experience, supplemented with a variety of tools, to help establish and sustain their operations. These tools support operations, cost determination, quality management, and governance. Costing tools have often been used to determine the economic value of a single specimen or an entire collection, with the purpose of allowing researchers to recover costs when providing access to those resources. Until recently, biobank managers have focused on deriving sample value based solely on cost-model analyses. We propose an alternative way to value collections using a web-based, automated tool for biobankers to determine the noneconomic value of biospecimen collections. The tool supports fit-for-purpose determinations for collections using common attributes and defined criteria to facilitate broader sample utility, sharing, and overall sustainability in operations.
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http://dx.doi.org/10.1177/19475535251374854 | DOI Listing |
Biopreserv Biobank
September 2025
National Cancer Institute, NIH, Bethesda, Maryland, USA.
Biobankers rely on their experience, supplemented with a variety of tools, to help establish and sustain their operations. These tools support operations, cost determination, quality management, and governance. Costing tools have often been used to determine the economic value of a single specimen or an entire collection, with the purpose of allowing researchers to recover costs when providing access to those resources.
View Article and Find Full Text PDFAJR Am J Roentgenol
September 2025
Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
The morphological patterns of lung adenocarcinoma (LUAD) are recognized for their prognostic significance, with ongoing debate regarding the optimal grading strategy. This study aimed to develop a clinical-grade, fully quantitative, and automated tool for pattern classification/quantification (PATQUANT), to evaluate existing grading strategies, and determine the optimal grading system. PATQUANT was trained on a high-quality dataset, manually annotated by expert pathologists.
View Article and Find Full Text PDFChem Sci
September 2025
Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca Gothenburg Sweden
Incorporating non-natural amino acids (NNAAs) into peptides enhances therapeutic properties, including binding affinity, metabolic stability, and half-life time. The pursuit of novel NNAAs for improved peptide designs faces the challenge of effective synthesis of these building blocks as well as the entire peptide itself. Solid-Phase Peptide Synthesis (SPPS) is an essential technology for the automated assembly of peptides with NNAAs, necessitating careful protection for effective coupling of amino acids in the peptide chain.
View Article and Find Full Text PDFPatterns (N Y)
July 2025
Cedars-Sinai Medical Center, Los Angeles, CA, USA.
The tree-based pipeline optimization tool (TPOT) is one of the earliest automated machine learning (ML) frameworks developed for optimizing ML pipelines, with an emphasis on addressing the complexities of biomedical research. TPOT uses genetic programming to explore a diverse space of pipeline structures and hyperparameter configurations in search of optimal pipelines. Here, we provide a comparative overview of the conceptual similarities and implementation differences between the previous and latest versions of TPOT, focusing on two key aspects: (1) the representation of ML pipelines and (2) the underlying algorithm driving pipeline optimization.
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