Then software systems were created that are used in medicine as decision-making systems [73, 130], and very soon after that, AI-tools for education [10, 146], and in the form of more complex games [4, 53, 126] and AI simulations [32]. Traditional algorithms have been limiting for so...
Alzheimer’s disease (AD) is the most common form of dementia. The accumulation of amyloid-beta (Aβ) peptide in the form of plaques and the formation of intracellular Tau neurofibrillary tangles in the brain are the main pathological hallmarks of this neurodegenerative disease [1]. Mutations in...
A key methodological aim in single-cell genomics is to learn structure from single-cell sequencing data in a systematic, data-driven way [1,2,3]. Clustering [4,5,6,7] and dimensionality reduction techniques such as such as PCA [8,9,10], t-SNE [11], or UMAP [12] are commonly used...
which is a subset of artificial intelligence (AI) and machine learning (ML). Cancer makes up a significant percentage of the illnesses that cause early human mortality across the globe, and this situation is likely to rise in the
machine learning (ML) methods able to efficiently handle high dimensional data are becoming widely used in GP. This is especially so because, compared to many other methods used in GP, ML methods possess the significant advantage of being able to model nonlinear relationships between the response ...
a tuned cascade forest is used to mine the features and output prediction scores deeply. The results of the 5-fold cross-validation using the HMDD v2.0 database indicate that the PCACFMDA algorithm achieved an AUC of 98.56%. Additionally, we perform case studies on breast, esophageal and lun...
We use the Bayesian PCA-based missing value estimation method by Oba et al. [29] to impute missing values for several features in preparing the next steps, and any outliers that may arise from extensive imputation are carefully removed from our analysis. ...
is not dedicated to a specific type of analysis (e.g., differential analysis) but can handle exploratory analysis (PCA, clustering, etc), differential analysis as well as different types of data edition (including correction of missing values, log-transformation, scaling or sample selection); all...
resulted constrained penalized optimization problem has a closed-form solution. As a consequence, GraphPCA can process ST data efficiently at vastly different scales. The low-dimensional spatial embeddings inferred by GraphPCA can be readily utilized for various downstream analysis tasks including spatial...
in human health. During the long process of evolution, microbes form an interdependent and mutually restrictive relationship with the host through individual adaptation and natural selection, while their microenvironment and immune system are in a dynamic equilibrium state [2]. When this dynamic balance...