AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This chapter presents tensor decomposition (TD)-based unsupervised feature extraction (FE) as a robust framework for feature selection in bioinformatics and provides a versatile and effective approach for extracting meaningful biological insights from complex, high-dimensional datasets.
This paper introduces a knowledge-guided approach that brings together data and computational models by jointly analyzing real data and simulated data (generated using a computational model) using coupled tensor factorizations with linear coupling.
This study addresses the challenge of organizing and interpreting large collections of scientific literature, focusing on a rapidly growing and heterogeneous AI research domain derived from object-detection-with-small-data literature. First, based on the set of keywords using Clarivate Analytics queries, over 18,500 re...
This work proposes post-pretrained lasso selective inference (PPL-SI), a novel selective inference method designed to provide statistically valid p values for the pretrained lasso that reliably controls false discoveries and significantly improves the detection of biologically relevant features compared to traditional...
This work presents Semi-supervised Omics Factor Analysis (SOFA), a method that incorporates known covariates into the model upfront and focuses the factor discovery on novel sources of variation.
Abstract This study evaluated statistical dimensionality reduction techniques, Principal Component Analysis (PCA), Sparse PCA (SPCA), Independent PCA (IPCA), deep learning based on autoencoders (AE), and their hybrid combinations, for subpopulation identification in genomic data. High-dimensional single nucleotide poly...
The Grouping–Scoring–Modeling (G-S-M) framework is proposed, a knowledge-driven pipeline that anchors feature selection in established disease–gene associations that provides a reproducible computational framework for candidate biomarker discovery.
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