Results 161 to 170 of about 3,867 (213)

A bibliometric analysis of acupuncture applied to primary osteoporosis. [PDF]

Front Med (Lausanne)
Yu G, Song H, Gao Y, Chen G, Xu Y.
europepmc   +1 more source

TSProm: deep learning framework to predict tissue-specific regulatory logic. [PDF]

NAR Genom Bioinform
Surana P, Dutta P, Papineni N, Sathian R, Zhou Z, Liu H, Davuluri RV.   +6 more
europepmc   +1 more source

Integrating brain imaging features and genomic profiles for the subtyping of major depression. [PDF]

Psychol Med
Yin L, Lin Y, Qiu J, Xiang Y, Li M, Xiao X, Lui SS, So HC.   +7 more
europepmc   +1 more source

Biclustering With Dominant Sets

Pattern Recognition, 2020
Biclustering can be defined as the simultaneous clustering of rows and columns in a data matrix and it has been recently applied to many scientific scenarios such as bioinformatics, text analysis and computer vision to name a few.
Alessandro Farinelli, Sebastiano Vascon
exaly   +3 more sources

Biclustering in Data Mining

Computers and Operations Research, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Panos M Pardalos, Oleg A. Prokopyev, Stanislav Busygin   +2 more
exaly   +4 more sources

Some of the next articles are maybe not open access.

Related searches:

Topological biclustering ARTMAP for identifying within bicluster relationships

Neural Networks, 2023
Biclustering is a powerful tool for exploratory data analysis in domains such as social networking, data reduction, and differential gene expression studies. Topological learning identifies connected regions that are difficult to find using other traditional clustering methods and produces a graphical representation.
Raghu Yelugam, Leonardo Enzo Brito da Silva, Donald C. Wunsch II   +2 more
openaire   +3 more sources

Biclustering With Missing Data

Information Sciences, 2020
Abstract Biclustering is a statistical learning methodology that simultaneously partitions rows and columns of a rectangular data array into homogeneous subsets. Biclustering is known to be an NP-hard problem, and therefore various heuristic approaches have been proposed.
Sigurdur Olafsson
exaly   +3 more sources

Impact of Biclustering on the Performance of Biclustering Based Collaborative Filtering

Expert Systems With Applications, 2018
Abstract At present online marketplaces are flooded with numerous users having diversified choices across profusely present products. Recommender systems were developed to suggest items, friends or movies etc. to online users based on their profiles.
Monika Singh, Mónica Mehrotra
exaly   +2 more sources

Biclustering by Resampling

2011
The search for similarities in large data sets has a very important role in many scientific fields. It permits to classify several types of data without an explicit information about it. In many cases researchers use analysis methodologies such as clustering to classify data with respect to the patterns and conditions together.
Ekaterina Nosova, Roberto Tagliaferri, Francesco Masulli, Stefano Rovetta   +3 more
openaire   +5 more sources