Results 201 to 210 of about 12,341,256 (312)

Risk of bias in machine learning and statistical models to predict height or weight: a systematic review in fetal and paediatric medicine. [PDF]

Diagn Progn Res
Lawrence NR, Bacila I, Tonge J, Tucker A, Dawson J, Lang ZQ, Krone NP, Dhiman P, Collins GS.   +8 more
europepmc   +1 more source

The impact of leukocyte‐associated RNA retention on platelet RNA‐based cancer detection

Molecular Oncology, EarlyView.
Platelets carry RNA signals that may help detect cancer, but platelet samples can also retain RNA from white blood cells. We found that this retained white blood cell signal did not significantly affect machine learning cancer classification, suggesting that the cancer‐related RNA signal in platelets is largely independent of leukocyte‐associated RNA ...
Michał Sieczczyński, Krzysztof Pastuszak, Anna Samelak‐Czajka, Zuzanna Cemka, Paulina Jackowiak, Tomasz Stefaniak, Sylwia Łapińska‐Szumczyk, Jacek Jassem, Anna J. Żaczek, Matthew T. Rondina, Myron G. Best, Sjors G. J. G. In 't Veld, Anna Supernat   +12 more
wiley   +1 more source

Gene expression and metadata based identification of key genes for lung cancer, COPD, and IPF using machine learning and statistical models. [PDF]

PLoS One
Yasmin MF, Hosen MF, Basar MA, Rahman A, Hasan M, Al Farid F, Karim HA, Miah ASM.   +7 more
europepmc   +1 more source

Overcoming platinum resistance by the disulfiram metabolite CuET in novel preclinical models of human carcinomas

Molecular Oncology, EarlyView.
Cisplatin‐resistant cancer cells survive DNA‐damaging platinum‐based treatment yet remain sensitive to CuET‐induced proteotoxic stress. The distinct mechanism of action allows CuET to overcome and eliminate cisplatin‐resistant tumour cells, unless highly expressed metallothioneins (MTs) interfere with CuET, thereby highlighting MT levels as candidate ...
Stefanos Arampatzis, Tereza Buchtová, Jirina Bartkova, Marie Lund Bay, Christoffer Lynge Halvorsen, Zuzana Machacova, Zdenek Skrott, Martin Mistrik, Ekaterina Dulina, Miguel Andujar‐Sanchez, Estrid Høgdall, Tine Henrichsen Schnack, Claus Høgdall, Berit Jul Mosgaard, Marie Boennelycke, Jacob Brink Laursen, Allan Jensen, Susanne K. Kjær, Tuula Kallunki, Robert Strauss, Jiri Bartek   +20 more
wiley   +1 more source

Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models. [PDF]

Sci Rep
Esmaeilyfard R, Bayati M.
europepmc   +1 more source

Automated FRAP microscopy for high‐throughput analysis of protein dynamics in chromatin organization and transcription

FEBS Open Bio, EarlyView.
RoboMic is an automated confocal microscopy pipeline for high‐throughput functional imaging in living cells. Demonstrated with fluorescence recovery after photobleaching (FRAP), it integrates AI‐driven nuclear segmentation, ROI selection, bleaching, and analysis.
Selçuk Yavuz, Bart Geverts, Johan A. Slotman, Andrea Sacchetti, Stefan Prekovic, Martin E. van Royen, Adriaan B. Houtsmuller   +6 more
wiley   +1 more source

Detecting LNAPL migration in real time using electrical resistivity and statistical models. [PDF]

Sci Rep
Tareq Al-Hussain O, Ramli H, Al-Haidarey MJ, Naser HY.   +3 more
europepmc   +1 more source

Raman‐based label‐free microscopic analysis of the pancreas in living zebrafish larvae

FEBS Open Bio, EarlyView.
Forward stimulated Raman scattering (F‐SRS) and epi coherent anti‐Stokes Raman scattering (E‐CARS) allow label‐free discrimination of distinct subcellular structures in the pancreas of living zebrafish larvae. Given the straightforward applicability, we anticipate broad implementation of Raman microscopy in other organs and across various biomedical ...
Noura Faraj, Eline M. F. de Lange, Klaas A. Sjollema, Ben N. G. Giepmans   +3 more
wiley   +1 more source

Comparison of oblique random survival forest, random survival forest, and statistical models for time-to-event data using simulation study. [PDF]

Sci Rep
Suliman A, Abdullahi AS, Masud MM, Serhani MA, AlZahmi A, Oulhaj A.   +5 more
europepmc   +1 more source

Optimizing photoactivation of PA‐mCherry for optical pooled CRISPR screens

FEBS Open Bio, EarlyView.
Photoactivatable PA‐mCherry finds widespread use to optically tag individual cells. However, confocal 405 nm UV laser‐scanning (normal scan) is much less efficient than widefield UV illumination, limiting the use of PA‐mCherry on confocal instruments. We remedy this limitation by reporting that rapid and repeated confocal scanning with a low‐intensity,
Sravasti Mukherjee, Giulia Zanetti, Bram van den Broek, Kees Jalink   +3 more
wiley   +1 more source