GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation

Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, including statistical and deep learning methods, often fail to effectively integrate gene interaction knowledge from databases or explicitly learn subtype-specific interactions. To address this mismatch, we propose GeSubNet, which learns a unified representation capable of predicting gene interactions while distinguishing between different disease subtypes. Graphs generated by such representations can be considered subtype-specific networks. GeSubNet is a multi-step representation learning framework with three modules: First, a deep generative model learns distinct disease subtypes from patient gene expression profiles. Second, a graph neural network captures representations of prior gene networks from knowledge databases, ensuring accurate physical gene interactions. Finally, we integrate these two representations using an inference loss that leverages graph generation capabilities, conditioned on the patient separation loss, to refine subtype-specific information in the learned representation. GeSubNet consistently outperforms traditional methods, with average improvements of 30.6%, 21.0%, 20.1%, and 56.6% across four graph evaluation metrics, averaged over four cancer datasets. Particularly, we conduct a biological simulation experiment to assess how the behavior of selected genes from over 11,000 candidates affects subtypes or patient distributions. The results show that the generated network has the potential to identify subtype-specific genes with an 83% likelihood of impacting patient distribution shifts. The GeSubNet resource is available: https://anonymous.4open.science/r/GeSubNet/

SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning

While end-to-end multi-channel electroencephalography (EEG) learning approaches have shown significant promise, their applicability is often constrained in neurological diagnostics, such as intracranial EEG resources. When provided with a single-channel EEG, how can we learn representations that are robust to multi-channels and scalable across varied tasks, such as seizure prediction? In this paper, we present SplitSEE, a structurally splittable framework designed for effective temporal-frequency representation learning in single-channel EEG. The key concept of SplitSEE is a self-supervised framework incorporating a deep clustering task. Given an EEG, we argue that the time and frequency domains are two distinct perspectives, and hence, learned representations should share the same cluster assignment. To this end, we first propose two domain-specific modules that independently learn domain-specific representation and address the temporal-frequency tradeoff issue in conventional spectrogram-based methods. Then, we introduce a novel clustering loss to measure the information similarity. This encourages representations from both domains to coherently describe the same input by assigning them a consistent cluster. SplitSEE leverages a pre-training-to-fine-tuning framework within a splittable architecture and has following properties: (a) Effectiveness: it learns representations solely from single-channel EEG but has even outperformed multi-channel baselines. (b) Robustness: it shows the capacity to adapt across different channels with low performance variance. Superior performance is also achieved with our collected clinical dataset. (c) Scalability: With just one fine-tuning epoch, SplitSEE achieves high and stable performance using partial model layers.

CMOB: Large-Scale Cancer Multi-Omics Benchmark with Open Datasets, Tasks, and Baselines

Machine learning has shown great potential in the field of cancer multi-omics studies, offering incredible opportunities for advancing precision medicine. However, the challenges associated with dataset curation and task formulation pose significant hurdles, especially for researchers lacking a biomedical background. Here, we introduce the CMOB, the first large-scale cancer multi-omics benchmark integrates the TCGA platform, making data resources accessible and usable for machine learning researchers without significant preparation and expertise.To date, CMOB includes a collection of 20 cancer multi-omics datasets covering 32 cancers, accompanied by a systematic data processing pipeline. CMOB provides well-processed dataset versions to support 20 meaningful tasks in four studies, with a collection of benchmarks. We also integrate CMOB with two complementary resources and various biological tools to explore broader research avenues.All resources are open-accessible with user-friendly and compatible integration scripts that enable non-experts to easily incorporate this complementary information for various tasks. We conduct extensive experiments on selected datasets to offer recommendations on suitable machine learning baselines for specific applications. Through CMOB, we aim to facilitate algorithmic advances and hasten the development, validation, and clinical translation of machine-learning models for personalized cancer treatments. CMOB is available on GitHub (\url{https://github.com/chenzRG/Cancer-Multi-Omics-Benchmark}).