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sefA Python Library for Similarity-based Dimensionality Reduction
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partitionA fast and flexible framework for data reduction in R
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NebulosaR package to visualize gene expression data based on weighted kernel density estimation
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ezancestryEasy genetic ancestry predictions in Python
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snATAC<<------ Use SnapATAC!!
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dyngenSimulating single-cell data using gene regulatory networks 📠
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twpca🕝 Time-warped principal components analysis (twPCA)
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Machine Learning With PythonPractice and tutorial-style notebooks covering wide variety of machine learning techniques
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Awesome Single CellCommunity-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
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UmapUniform Manifold Approximation and Projection
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dbMAPA fast, accurate, and modularized dimensionality reduction approach based on diffusion harmonics and graph layouts. Escalates to millions of samples on a personal laptop. Adds high-dimensional big data intrinsic structure to your clustering and data visualization workflow.
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NIDS-Intrusion-DetectionSimple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN
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dmlR package for Distance Metric Learning
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UMAP.jlUniform Manifold Approximation and Projection (UMAP) implementation in Julia
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bhtsneParallel Barnes-Hut t-SNE implementation written in Rust.
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timecorrEstimate dynamic high-order correlations in multivariate timeseries data
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adenineADENINE: A Data ExploratioN PipelINE
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pymdeMinimum-distortion embedding with PyTorch
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walkletsA lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).
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scHPFSingle-cell Hierarchical Poisson Factorization
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tldrTLDR is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses
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lfdaLocal Fisher Discriminant Analysis in R
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mosesStreaming, Memory-Limited, r-truncated SVD Revisited!
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uapcaUncertainty-aware principal component analysis.
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CeleScopeSingle Cell Analysis Pipelines
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