Results 171 to 180 of about 282,298 (328)

Predictive Factors of Response to Immunotherapy in Lymphomas: A Multicentre Clinical Data Warehouse Study (PRONOSTIM). [PDF]

Cancers (Basel), 2023
Detroit M, Collier M, Beeker N, Willems L, Decroocq J, Deau-Fischer B, Vignon M, Birsen R, Moufle F, Leclaire C, Balladur E, Deschamps P, Chauchet A, Batista R, Limat S, Treluyer JM, Ricard L, Stocker N, Hermine O, Choquet S, Morel V, Metz C, Bouscary D, Kroemer M, Zerbit J.   +24 more
europepmc   +1 more source

Replication data for Northern Region of Ghana: Insect Population Dynamics in Maize Warehouses

, 2018
George Opit
openalex   +1 more source

Identification of Exhaled Volatile Organic Compounds Biomarkers for Lung Cancer Under Data‐Limited Conditions Using Data Augmentation and Multi‐View Feature Selection

Advanced Intelligent Discovery, EarlyView.
This work introduces a novel framework for identifying non‐small cell lung cancer biomarkers from hundreds of volatile organic compounds in breath, analyzed via gas chromatography‐mass spectrometry. This method integrates generative data augmentation and multi‐view feature selection, providing a stable and accurate solution for biomarker discovery in ...
Guancheng Ren, Bohao Liu, Zhiyuan Qu, Xue Jiang, Heng Zhao, Danyao Qu, Ruizhi Ning, Chen Su, Taoping Liu, Guangjian Zhang, Weiwei Wu   +10 more
wiley   +1 more source

Correction: Rotterdam Oncology Documentation (RONCDOC) - a high-quality data warehouse and tissue collection for head and neck cancer. [PDF]

BMC Cancer
Hoesseini A, Dronkers EAC, Dieleman E, De Herdt MJ, Wieringa MH, van Velthuysen MF, Oferman MPJ, Sewnaik A, Monserez D, Keereweer S, Hardillo JAU, Baatenburg de Jong RJ.   +11 more
europepmc   +1 more source

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

Strong association between psychiatric disorders co-occurrence and dementia: a Bayesian approach on a 14-year clinical data warehouse. [PDF]

BMJ Ment Health
Baudouin E, Duron E, Verdoux M, Gasnier M, Pelloux Y, Bottemanne H, Corruble E, Colle R.   +7 more
europepmc   +1 more source

Enhancing an enterprise data warehouse for research with data extracted using natural language processing. [PDF]

J Clin Transl Sci, 2023
Magoc T, Everson R, Harle CA.
europepmc   +1 more source

The Challenge of Handling Structured Missingness in Integrated Data Sources

Advanced Intelligent Discovery, EarlyView.
As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson, Marius Hofert, Eleni‐Rosalina Andrinopoulou, Niels Hagenbuch, Sarah McGough, Chris Harbron, Robin Mitra   +6 more
wiley   +1 more source

Advancements in Graphdiyne‐Based Multiscale Catalysts for Green Hydrogen Energy Conversion

Advanced Intelligent Discovery, EarlyView.
This review systematically explores the fundamental characteristics of graphdiyne (GDY), cutting‐edge field of GDY‐based multiscale catalysts within sustainable energy conversion systems.Special emphasis is placed on the structure‒property relationships in different reactions.
Qian Xiao, Lu Qi, Siao Chen, Yurui Xue
wiley   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source