RNMFLP: predicting circRNA–disease associations based on robust nonnegative matrix factorization and label propagation
Circular RNAs (circRNAs) are a class of structurally stable endogenous noncoding RNA
molecules. Increasing studies indicate that circRNAs play vital roles in human diseases …
molecules. Increasing studies indicate that circRNAs play vital roles in human diseases …
Latent factor-based recommenders relying on extended stochastic gradient descent algorithms
High-dimensional and sparse (HiDS) matrices generated by recommender systems contain
rich knowledge regarding various desired patterns like users' potential preferences and …
rich knowledge regarding various desired patterns like users' potential preferences and …
Convergence analysis of single latent factor-dependent, nonnegative, and multiplicative update-based nonnegative latent factor models
A single latent factor (LF)-dependent, nonnegative, and multiplicative update (SLF-NMU)
learning algorithm is highly efficient in building a nonnegative LF (NLF) model defined on a …
learning algorithm is highly efficient in building a nonnegative LF (NLF) model defined on a …
Predicting student performance and its influential factors using hybrid regression and multi-label classification
A Alshanqiti, A Namoun - Ieee Access, 2020 - ieeexplore.ieee.org
Understanding, modeling, and predicting student performance in higher education poses
significant challenges concerning the design of accurate and robust diagnostic models …
significant challenges concerning the design of accurate and robust diagnostic models …
Human guided cooperative robotic agents in smart home using beetle antennae search
In this paper, we propose a control framework for cooperative robotic agents, which
constitutes an essential component in the construction of futuristic smart-homes. Such …
constitutes an essential component in the construction of futuristic smart-homes. Such …
An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data
High-dimensional and sparse (HiDS) data with non-negativity constraints are commonly
seen in industrial applications, such as recommender systems. They can be modeled into an …
seen in industrial applications, such as recommender systems. They can be modeled into an …
iCircDA-MF: identification of circRNA-disease associations based on matrix factorization
H Wei, B Liu - Briefings in bioinformatics, 2020 - academic.oup.com
Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-
loop structure, which play critical roles in various biological processes. Identifying …
loop structure, which play critical roles in various biological processes. Identifying …
Large-scale and scalable latent factor analysis via distributed alternative stochastic gradient descent for recommender systems
Latent factor analysis (LFA) via stochastic gradient descent (SGD) is highly efficient in
discovering user and item patterns from high-dimensional and sparse (HiDS) matrices from …
discovering user and item patterns from high-dimensional and sparse (HiDS) matrices from …
Robust bi-stochastic graph regularized matrix factorization for data clustering
Data clustering, which is to partition the given data into different groups, has attracted much
attention. Recently various effective algorithms have been developed to tackle the task …
attention. Recently various effective algorithms have been developed to tackle the task …
Entity2rec: Learning user-item relatedness from knowledge graphs for top-n item recommendation
Knowledge Graphs have proven to be extremely valuable to recommender systems, as they
enable hybrid graph-based recommendation models encompassing both collaborative and …
enable hybrid graph-based recommendation models encompassing both collaborative and …