Results 111 to 120 of about 940,524 (345)
An optimized initialization center K-means clustering algorithm based on density
, 2015 Traditional K-means algorithm's clustering effect is affected by the initial cluster center points. To solve this problem, a method is proposed to optimize the K-means initial center points.
Xiaofeng Zhou +5 more
core +1 more source
Structural insights and therapeutic targets in Acinetobacter baumannii capsule biosynthesis
FEBS Letters, EarlyView.Hypervirulent KL49 A. baumannii's capsular polysaccharide contains the nonulosonic acid 8‐epi‐Leg5,7Ac2, synthesized by epimerization via ElaA, ElaB, and ElaC. Crystal structures of ElaA, ElaB, and ElaC reveal their role in CMP‐Leg5,7Ac2 synthesis and regioselective C8 epimerization.
Woo Cheol Lee +7 more
wiley +1 more source
RACK: RApid Clustering using K-means algorithm
, 2009 The k-means algorithm is an extremely popular technique for clustering data. One of the major limitations of the k-means is that the time to cluster a given dataset D is linear in the number of clusters, k.
Murty, MN, Garg, Vikas K
core
Jurnal Teknologi dan Sistem InformasiData tidak bisa dipisahkan dari bidang industri yang semakin maju. Analisis ini menyoroti kekuatan big data dalam industri food and beverages. Dengan menggunakan profil pelanggan dan analisis transaksi, bisnis dapat memperoleh wawasan pelanggan yang ...
Anggita Nurlathifa, Diarany Sucahyati
doaj +1 more source
Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
Document clustering using sample weighting [PDF]
, 2007 Clustering algorithm based on Sample weighting has been noticed recently. In this paper, a novel sample weighting clustering algorithm is presented based on K-Means and fuzzy C-Means algorithm.
Su, Xinning +2 more
core
Clustering analysis of railway driving missions with niching [PDF]
, 2012 A wide number of applications requires classifying or grouping data into a set of categories or clusters. Most popular clustering techniques to achieve this objective are K-means clustering and hierarchical clustering.
Jaafar, Amine +2 more
core +1 more source
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
FEBS Letters, EarlyView.Protein aggregates threaten proteostasis and cell health. In human cells, Hsp70–J‐domain protein‐based disaggregases remove aggregates, but how they assemble remains unclear. Our biochemical findings show that DNAJA2‐ and DNAJB1‐containing disaggregase scaffolds enhance luciferase aggregate targeting, and that Hsp70 recruitment by both J‐domain ...
Anna Szlachcic, Nadinath B. Nillegoda
wiley +1 more source
The role of miR‐335‐5p in the redifferentiation of BRAF p.V600E thyroid cancers
Molecular Oncology, EarlyView.The BRAF p.V600E mutation promotes thyroid cancer dedifferentiation and radioiodine resistance. Using a network approach, we identified miR‐335‐5p as a key regulator of BRAF‐mutated thyroid tumors. Restoring miR‐335‐5p increased thyroid‐specific gene expression and iodine uptake in cells and organoids.
Valeria Pecce +11 more
wiley +1 more source