This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering tech...
In fact, commercial use of machine learning, especially deep learning methods, is relatively new. They require vast sets of properly organized and prepared data to provide accurate answers to the questions we want to ask them. A business working on a practical machine learning application needs to...
To identify surgical phenotypes, patients with similar combinations of individual-level SHAP values were grouped together based on Euclidean distance, an unsupervised distance-based clustering method (eMethods in the Supplement). Results There were a total of 2372 patients in the study cohort. The ...
b, Hierarchical clustering of TFBS pairs in the non-template (NT; purple) or template (T; yellow) orientation. The order of transcription factors is denoted with the most distant transcription factor relative to the TSS shown in the rows. In the first column, each color represents a ...
Methods In this study, we compared fecal metagenomes of 25 antiretroviral-treatment (ART)-controlled PWH to three independent control groups of 25 non-infected matched individuals by means of univariate analyses and machine learning methods. Moreover, we used two external datasets to validate predicti...
Compared to the conventional dimensionality reduction methods such as principal component analysis, AutoEncoder is capable of learning intrinsic, nonlinear relationships in the input data and therefore better suited for high-dimensional nonlinear data [25]. Step two: ensemble clustering A common problem ...
Methods In this study, we compared fecal metagenomes of 25 antiretroviral-treatment (ART)-controlled PWH to three independent control groups of 25 non-infected matched individuals by means of univariate analyses and machine learning methods. Moreover, we used two external datasets to validate predicti...
Graphs structures can be coded directly (e.g.NetworkX), or using a model (there are MANY deep learning approaches). Model-based methods also facilitate tasks such as link (edge) prediction. See also:Graph Neural Networks Collaborative Filtering: Naïve Bayes {#intro_naive_bayes_collab_filter}...
a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional...
Computer scientists and traditional engineers need to speak the same language--a language rooted in real analysis, linear algebra, probability and physics. Computer scientists ought to take physics through electromagnetism. But, to do that, they'll need take up through multivariate calculus, (and di...