Results 101 to 110 of about 15,644,966 (296)
Tumour‐proximal deep serum proteomics identifies biomarkers associated with ovarian cancer
Molecular Oncology, EarlyView.New biomarkers are needed for early ovarian cancer detection. Ovarian (tumour‐proximal) and peripheral vein serum from healthy women and patients with benign or malignant tumours underwent nanoparticle‐enriched deep proteomics. Ovarian serum yielded more protein identifications and revealed tumour‐reflective signatures.
Darren Lau +9 more
wiley +1 more source
Improvement of Differential Privacy K-means Clustering Algorithm
Journal of Harbin University of Science and TechnologyIn order to address the issues of arbitrary center selection and unreasonable privacy budget allocation leading to poor clustering performance in differential privacy K-means clustering algorithm, a new center selection scheme is designed based on two ...
GUO Rumin, CHEN Xuebin, SHAN Liyang
doaj +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 +12 more
wiley +1 more source
A New Clustering Algorithm Partition K-Means
, 2011 K-means is a classic algorithm of partition clustering, its speed is very fast, well, the clustering result is very sensitive to the initial cores. As a result, algorithm K-means does not always get the Global Optimization.
Chan Juan Yin, Yu Ping Xi, Min Chen
core +1 more source
An efficient k-modes algorithm for clustering categorical datasets [PDF]
, 2022 Mining clusters from data is an important endeavor in many applications. The k-means method is a popular, efficient, and distribution-free approach for clustering numerical-valued data, but does not apply for categorical-valued observations.
Maitra, Ranjan, Dorman, Karin S.
core
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 +6 more
wiley +1 more source
Identifying optimistic stocks with K-means clustering algorithm
International Review of Economics & FinanceSelecting stocks from a large number of active stocks is a critical and challenging investment decision due to high volatility and biased decision-making.
Bilal Aslam
doaj +1 more source
Fast K-Means Algorithm Clustering
International journal of Computer Networks & Communications, 2011 16 pages, Wimo2011; International Journal of Computer Networks & Communications (IJCNC) Vol.3, No.4, July ...
Raied Salman +4 more
openaire +3 more sources
The Improved K-Means Algorithm in Intrusion Detection System Research
, 2011 To improve the efficiency of Internet intrusion detection, data mining is adopted in intrusion detection. The paper introduces the concept of intrusion detection and k-means algorithm.
Hong Bo Zhang, Yi Jiang
core +1 more source
Single‐molecule DNA flow‐stretch assays for high‐throughput DNA–protein interaction studies
FEBS Open Bio, EarlyView.We describe an optimised single‐molecule DNA flow‐stretch assay that visualises DNA–protein interactions in real time. Linear DNA fragments are tethered to a surface and stretched by buffer flow for fluorescence imaging. Using λ and φX174 DNA, this protocol enhances reproducibility and accessibility, providing a versatile approach for studying diverse ...
Ayush Kumar Ganguli +8 more
wiley +1 more source