Results 21 to 30 of about 6,396 (182)
Austrian Journal of Statistics, 2020 Biclustering, in general, is a process of clustering genes and conditions simultaneously rather than clustering them separately. The purpose of biclustering is to discover a subset from experimental data.
Alhadi Bustamam +3 more
doaj +1 more source
Unsupervised learning for medical data: A review of probabilistic factorization methods
Statistics in Medicine, Volume 42, Issue 30, Page 5541-5554, 30 December 2023., 2023 We review popular unsupervised learning methods for the analysis of high‐dimensional data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks. We show that four commonly used methods, principal component analysis, K‐means clustering, nonnegative matrix factorization, and latent Dirichlet allocation, can be written as ...
Dorien Neijzen, Gerton Lunter
wiley +1 more source
Biometrics, Volume 79, Issue 4, Page 3846-3858, December 2023., 2023 Abstract Clustering has long been a popular unsupervised learning approach to identify groups of similar objects and discover patterns from unlabeled data in many applications. Yet, coming up with meaningful interpretations of the estimated clusters has often been challenging precisely due to their unsupervised nature.
Minjie Wang +2 more
wiley +1 more source
Multi-species integrative biclustering [PDF]
Genome Biology, 2010 AbstractWe describe an algorithm, multi-species cMonkey, for the simultaneous biclustering of heterogeneous multiple-species data collections and apply the algorithm to a group of bacteria containing Bacillus subtilis, Bacillus anthracis, and Listeria monocytogenes.
Waltman, Peter +6 more
openaire +2 more sources
IET Control Theory &Applications, Volume 17, Issue 17, Page 2304-2312, November 2023., 2023 Two algorithms—trajectory feature extraction and task‐to‐crowd matching are proposed here for extracting features from trajectory data and solving the job‐to‐crowd matching problem. The real‐world dataset is used in the simulation to verify the truth and justify the achievements.
Pei‐Wei Tsai, Xingsi Xue, Jing Zhang
wiley +1 more source
Analysis of regulatory network involved in mechanical induction of embryonic stem cell differentiation [PDF]
, 2012 Embryonic stem cells are conventionally differentiated by modulating specific growth factors in the cell culture media. Recently the effect of cellular mechanical microenvironment in inducing phenotype specific differentiation has attracted considerable ...
Banerjee, I +4 more
core +7 more sources
Mass Spectrometry Reviews, Volume 42, Issue 5, Page 1927-1964, September/October 2023., 2023 Abstract Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS ...
Gerard Baquer +5 more
wiley +1 more source
A Characterization of Interactive Visual Data Stories With a Spatio‐Temporal Context
Computer Graphics Forum, Volume 42, Issue 6, September 2023., 2023 We combined and adapted three existing design spaces for visual data stories to classify 130 spatio‐temporal stories collected between 2018 and 2022. An analyzis of the collected data revealed various patterns, for example how large‐scale struggles shape the development of storytelling techniques. Abstract Large‐scale issues with a spatial and temporal
Benedikt Mayer +3 more
wiley +1 more source
BicAT: a biclustering analysis toolbox [PDF]
Bioinformatics, 2006 Abstract Summary: Besides classical clustering methods such as hierarchical clustering, in recent years biclustering has become a popular approach to analyze biological data sets, e.g. gene expression data. The Biclustering Analysis Toolbox (BicAT) is a software platform for clustering-based data analysis that integrates various ...
Barkow, Simon +4 more
openaire +4 more sources
A visual analytics approach for understanding biclustering results from microarray data
BMC Bioinformatics, 2008 Background Microarray analysis is an important area of bioinformatics. In the last few years, biclustering has become one of the most popular methods for classifying data from microarrays.
Quintales Luis +2 more
doaj +1 more source