Quadratic Kernel Learning for Interpolation Kernel Machine Based Graph Classification

Abstract

Interpolating classifiers interpolate all the training data and thus have zero training error. Recent research shows their fundamental importance for high-performance ensemble techniques. Interpolation kernel machines belong to the class of interpolating classifiers and do generalize well. They have been demonstrated to be a good alternative to support vector machine for graph classification. In this work we further improve their performance by considering multiple kernel learning. We establish a general scheme for achieving this goal. The current experimental work is done using quadratic kernel combination. Our experimental results demonstrate the performance boosting potential of our approach against the use of individual graph kernels.

Interpretable modeling of time-resolved single-cell gene-protein expression using CrossmodalNet

“…Single-cell RNA sequencing (scRNA-seq), which allows transcriptomic data collection from thousands of cells in parallel (Quake, 2021), enables examination of cellular states at individual cell level, leading to insights into diverse cell type identification, gene regulation, and cellular communication (Jindal et al, 2018;Osorio et al, 2020;2022;Yang et al, 2023). Compared to traditional single-cell techniques that measure only one aspect of cellular activity, the ability of multimodal (Ling et al, 2023;Han et al, 2022a; approaches has the potential to significantly improve our understanding of cellular behavior and function, thereby shedding light on a vast array of biological questions. Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) (Stoeckius et al, 2017) is a cutting-edge sequencing method that allows simultaneous measurement of gene and surface protein expression at the single-cell level.…”