Results 81 to 90 of about 1,986,530 (277)
Pengelompokan Komentar Dataset Sentipol dengan Modified K-Means Clustering
JuTISI (Jurnal Teknik Informatika dan Sistem Informasi), 2020 Clustering is a technique in data mining that groups data sets into similar data clusters. One of the algorithms that is commonly used for clustering is K-Means.
Ruddy Cahyanto +2 more
doaj +1 more source
Faster K-Means Cluster Estimation
, 2017 There has been considerable work on improving popular clustering algorithm `K-means' in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants tend to compute distance of each data point to each cluster centroid for ...
A Likas, DT Pham, SP Lloyd, T Kanungo
core +1 more source
Screening for lung cancer: A systematic review of overdiagnosis and its implications
Molecular Oncology, EarlyView.Low‐dose computed tomography (CT) screening for lung cancer may increase overdiagnosis compared to no screening, though the risk is likely low versus chest X‐ray. Our review of 8 trials (84 660 participants) shows added costs. Further research with strict adherence to modern nodule management strategies may help determine the extent to which ...
Fiorella Karina Fernández‐Sáenz +12 more
wiley +1 more source
An Improved NSGA-III Algorithm Using Genetic K-Means Clustering Algorithm
IEEE Access, 2019 The non-dominated sorting genetic algorithm III (NSGA-III) has recently been proposed to solve many-objective optimization problems (MaOPs). While this algorithm achieves good diversity, its convergence is unsatisfactory.
Qingguo Liu +3 more
doaj +1 more source
Towards explaining the speed of $k$-means [PDF]
, 2011 The $k$-means method is a popular algorithm for clustering, known for its speed in practice. This stands in contrast to its exponential worst-case running-time. To explain the speed of the $k$-means method, a smoothed analysis has been conducted.
Manthey, Bodo
core +2 more sources
Liquid biopsy epigenetics: establishing a molecular profile based on cell‐free DNA
Molecular Oncology, EarlyView.Cell‐free DNA (cfDNA) fragments in plasma from cancer patients carry epigenetic signatures reflecting their cells of origin. These epigenetic features include DNA methylation, nucleosome modifications, and variations in fragmentation. This review describes the biological properties of each feature and explores optimal strategies for harnessing cfDNA ...
Christoffer Trier Maansson +2 more
wiley +1 more source
Molecular Oncology, EarlyView.Clinical trials on PARP inhibitors in urothelial carcinoma (UC) showed limited efficacy and a lack of predictive biomarkers. We propose SLFN5, SLFN11, and OAS1 as UC‐specific response predictors. We suggest Talazoparib as the better PARP inhibitor for UC than Olaparib.
Jutta Schmitz +15 more
wiley +1 more source
Self-Adaptive K-Means Based on a Covering Algorithm
Complexity, 2018 The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time.
Yiwen Zhang +6 more
doaj +1 more source
A robust and sparse K-means clustering algorithm [PDF]
, 2012 In many situations where the interest lies in identifying clusters one might expect that not all available variables carry information about these groups. Furthermore, data quality (e.g.
Kondo, Yumi +2 more
core
Molecular Oncology, EarlyView.HDAC4 is degraded by the E3 ligase FBXW7. In colorectal cancer, FBXW7 mutations prevent HDAC4 degradation, leading to oxaliplatin resistance. Forced degradation of HDAC4 using a PROTAC compound restores drug sensitivity by resetting the super‐enhancer landscape, reprogramming the epigenetic state of FBXW7‐mutated cells to resemble oxaliplatin ...
Vanessa Tolotto +13 more
wiley +1 more source