Network pharmacology (NP) provides a new methodological perspective for understanding
traditional medicine from a holistic perspective, giving rise to frontiers such as traditional …

This paper proposes a novel contrastive learning framework, called FOCAL, for extracting
comprehensive features from multimodal time-series sensing signals through self …

While the primary sequences of human proteins have been cataloged for over a decade,
determining how these are organized into a dynamic collection of multiprotein assemblies …

Single-cell genomics technologies enable multimodal profiling of millions of cells across
temporal and spatial dimensions. Experimental limitations prevent the measurement of all …

Single-cell multimodal datasets have measured various characteristics of individual cells,
enabling a deep understanding of cellular and molecular mechanisms. However …

Cancer is a complex disease composing systemic alterations in multiple scales. In this study,
we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi …

Recent advancements for simultaneously profiling multi-omics modalities within individual
cells have enabled the interrogation of cellular heterogeneity and molecular hierarchy …

The availability of high-throughput sequencing data creates opportunities to
comprehensively understand human diseases as well as challenges to train machine …

Single-cell multi-omics have transformed biomedical research and present exciting machine
learning opportunities. We present scLinear, a linear regression-based approach that …

Motivation: Advances in single-cell measurement techniques provide rich multimodal data,
which helps us to explore the life state of cells more deeply. However, multimodal …