Causal feature selection has attracted much attention in recent years, as the causal features selected imply the causal mechanism related to the class attribute, leading to more reliable prediction models built using them. Currently there is a need of developing multi-source feature selection methods,...
Multi-source Causal Feature Selection (MCFS) can search for an invariant set in multiple interventional datasets. However, fixed feature spaces are restrained, and exactly these same features are required among multi-source data. To overcome these limitations, we propose a novel method known as ...
Using a dynamical systems framework called multifactorial causal modeling (MCM), Iturria-Medina et al. fit whole-brain population models of structural, functional, metabolic, vascular, and amyloid alterations as functions of their local pairwise interactions and inter-region propagation along anatomical,...
To identify causal candidate variants and target genes of GWAS loci within a cell type-specific context, we fine-mapped GWAS variants associated with three eye diseases, glaucoma [36], age-related macular degeneration [40], and refraction error/myopia [42]. We incorporated functional annotation (...
Source data Full size image Given the changes in body composition, we examined scWAT adipocyte size following ExT (Fig. 1d). Analysis of nearly 56,000 total adipocytes revealed differences in size distribution with training in both sexes (Fig. 1e and Supplementary Table 1a,b). Notably, the ...
Optimization and model selection for domain generalization Leveraging SAM for Single-Source Domain Generalization in Medical Image Segmentation [arxiv] SAM for single-source domain generalization Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion ...
feature linkage candidates with different throughput and different levels of fidelity to the biological, causal interactions. However, when restricting the data source to transcriptome and chromatin accessibility profiling, the feature linkages we infer are candidates that are enriched for causa...
2024-03-28 Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models Samuel Marks et.al. 2403.19647 link 2024-03-28 Change-Agent: Towards Interactive Comprehensive Change Interpretation and Analysis from Change Detection and Change Captioning Chenyang Liu et.al. 240...
Feature selection plays a critical role in the machine learning pipeline for several reasons. Firstly, it helps to mitigate the risk of overfitting by reducing the dimensionality of the data used to train the model. Additionally, it contributes to optimizing computational resources by reducing the ti...
Time-series data will affect each other, that is, there is a causal relationship between the data at the current time point and the data at the previous time point and after the time point. The recurrent neural network can learn the association between the time-series data through the ...