The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research.
Motivated by the aim of providing global explanations, we adapt the well-known Automated Concept-based Explanation approach (Ghorbani et al., 2019) to GNN node and graph classification, and propose GCExplainer.
Moreover, these pipelines are deterministic in nature, making them unable to capture predictive uncertainty.
Recent research on graph neural network (GNN) models successfully applied GNNs to classical graph algorithms and combinatorial optimisation problems.
Nevertheless, these models are severely constrained by the rigid combinatorial structure of Simplicial Complexes (SCs).
Ranked #1 on Graph Regression on ZINC
In contrast, attentional classifiers are polythetic by default and able to solve these problems with a linear embedding dimension.
Neural ODE Processes approach the problem of meta-learning for dynamics using a latent variable model, which permits a flexible aggregation of contextual information.
Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models.
Training and deploying graph neural networks (GNNs) remains difficult due to their high memory consumption and inference latency.
Ranked #4 on Graph Property Prediction on ogbg-code2
Then we use Poisson Surface Reconstruction to transform the reconstructed point cloud into a 3D mesh.
Ranked #1 on 3D Reconstruction on ShapeNet
The pairwise interaction paradigm of graph machine learning has predominantly governed the modelling of relational systems.
Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused heavily on the physiological time series data, largely ignoring sparse data such as diagnoses and medications.
Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning.
Then, we propose the use of the Laplacian eigenvectors as such vector field.
Ranked #1 on Graph Classification on CIFAR10 100k
The current training and evaluation procedures for these models through the use of synthetic multi-relational datasets however are agnostic to interaction network isomorphism classes, which produce identical dynamics up to initial conditions.
We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs.
Neural Processes (NPs) are powerful and flexible models able to incorporate uncertainty when representing stochastic processes, while maintaining a linear time complexity.
Results: Network multi-omic integration has led to the discovery of interesting oscillatory signals.
Molecular Networks Computational Engineering, Finance, and Science I.5.2
In this work, we propose a new deep learning model based on the combination of temporal convolution and pointwise (1x1) convolution, to solve the length of stay prediction task on the eICU and MIMIC-IV critical care datasets.
A novel method to identify salient computational paths within randomly wired neural networks before training is proposed.
The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff.
Neural networks used for multi-interaction trajectory reconstruction lack the ability to estimate the uncertainty in their outputs, which would be useful to better analyse and understand the systems they model.
The information contained in hierarchical topology, intrinsic to many networks, is currently underutilised.
Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures.
Using spatiotemporally correlated image time series as an example, we show that the choice of which correlation structures to explicitly represent in the latent space has a significant impact on model performance in terms of reconstruction.
We investigate a flexible means of regularization for link prediction based on an approximation of the Kolmogorov complexity of graphs that is differentiable and compatible with recent advances in link prediction algorithms.
Multi-Agent Reinforcement Learning (MARL) encompasses a powerful class of methodologies that have been applied in a wide range of fields.
Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.
Ranked #2 on Graph Classification on CIFAR10 100k
The characterisation of the brain as a "connectome", in which the connections are represented by correlational values across timeseries and as summary measures derived from graph theory analyses, has been very popular in the last years.
Recent advancements in graph representation learning have led to the emergence of condensed encodings that capture the main properties of a graph.
Dynamic assessment of patient status (e. g. by an automated, continuously updated assessment of outcome) in the Intensive Care Unit (ICU) is of paramount importance for early alerting, decision support and resource allocation.
Recordings in the first few hours of a patient's stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores within just 2 hours and achieving a state of the art Area Under the Receiver Operating Characteristic (AUROC) value of 0. 80 (95% CI 0. 79-0. 80) at 12 hours vs 0. 70 and 0. 66 for SAPS II and OASIS at 24 hours respectively.
Modern medicine requires generalised approaches to the synthesis and integration of multimodal data, often at different biological scales, that can be applied to a variety of evidence structures, such as complex disease analyses and epidemiological models.
The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task.
Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions.
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects.
Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time.
ChronoMID builds on the success of cross-modal convolutional neural networks (X-CNNs), making the novel application of the technique to medical imaging data.
Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks.
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.
Ranked #39 on Node Classification on Citeseer
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements.
Our work improves on existing multimodal deep learning algorithms in two essential ways: (1) it presents a novel method for performing cross-modality (before features are learned from individual modalities) and (2) extends the previously proposed cross-connections which only transfer information between streams that process compatible data.
In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks).