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Mathematical Understanding and Implementation of Principal Component Analysis (PCA) via Covariance Matrices [Python]

In data analysis and machine learning practice, "dimensionality reduction" is an essential technique for visualizing high-dimensional data and as a preprocessing step for clustering. Representative ...
GitHub

RALEIGH: RAL EIGensolver for real symmetric and Hermitian problems

RALEIGH is a Python implementation of the block Jacobi-conjugated gradients algorithm for computing several eigenpairs (eigenvalues and corresponding eigenvectors) of large scale real symmetric and ...
GitHub

BEER: Batch EffEct Remover for single-cell data

Mar. 2021 ( V0.1.9 ): First version for Seurat 4.0.0 Feb. 2021 ( V0.1.8 ): Last version for Seurat 3.0.0 Nov. 2019 ( v0.1.7 ): In ".simple_combine(D1, D2, FILL=TRUE)", "FILL" can help users to keep ...
Nature

Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data

Mass spectrometry-based lipidomics and metabolomics generate extensive data sets that, along with metadata such as clinical parameters, require specific data exploration skills to identify and ...
Scientific Research Publishing

Bankruptcy Prediction in the Polish Banking Industry Using Principal Component Analysis and BP Neural Network ()

Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, College of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, China. With the ...
C&EN

Processing Next-Generation Mass Spectrometry Imaging Data: Principal Component Analysis at Scale

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Mass spectrometry imaging (MSI) is constantly improving in spatial resolving power, ...
Nature

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional ...
InfoWorld

The engines of AI: Machine learning algorithms explained

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...
InfoWorld

14 popular AI algorithms and their uses

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...
Scientific Research Publishing

Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis ()

The process of automated prediction of disease is key for better treatment and lifesaving. As such, many machine learning (ML) based methods have been developed for various diseases. The growing ...