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Found 53 papers, 34 papers with code
Empowering Biomedical Discovery with AI Agents
We envision 'AI scientists' as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate machine learning tools with experimental platforms.
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
UniTS: Building a Unified Time Series Model
However, current foundation models apply to sequence data but not to time series, which present unique challenges due to the inherent diverse and multidomain time series datasets, diverging task specifications across forecasting, classification and other types of tasks, and the apparent need for task-specialized models.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation
To this end, we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image, text, or both, and thus is suitable for LoT study.
Graph AI in Medicine
With diverse data -- from patient records to imaging -- GNNs process data holistically by viewing modalities as nodes interconnected by their relationships.
Full-Atom Protein Pocket Design via Iterative Refinement
In the initial stage, the residue types and backbone coordinates are refined using a hierarchical context encoder, complemented by two structure refinement modules that capture both inter-residue and pocket-ligand interactions.
Current and future directions in network biology
As such, it is expected to help shape short- and long-term vision for future computational and algorithmic research in network biology.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design
Recent developments in geometric deep learning, focusing on the integration and processing of 3D geometric data, coupled with the availability of accurate protein 3D structure predictions from tools like AlphaFold, have greatly advanced the field of structure-based drug design.
GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
Deleted Edge Consistency ensures that the influence of deleted elements is removed from both model weights and neighboring representations, while Neighborhood Influence guarantees that the remaining model knowledge is preserved after deletion.
Domain Adaptation for Time Series Under Feature and Label Shifts
Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain.
Multimodal learning with graphs
Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics.
Evaluating Explainability for Graph Neural Networks
As post hoc explanations are increasingly used to understand the behavior of graph neural networks (GNNs), it becomes crucial to evaluate the quality and reliability of GNN explanations.
OpenXAI: Towards a Transparent Evaluation of Model Explanations
OpenXAI comprises of the following key components: (i) a flexible synthetic data generator and a collection of diverse real-world datasets, pre-trained models, and state-of-the-art feature attribution methods, and (ii) open-source implementations of eleven quantitative metrics for evaluating faithfulness, stability (robustness), and fairness of explanation methods, in turn providing comparisons of several explanation methods across a wide variety of metrics, models, and datasets.
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
Experiments against eight state-of-the-art methods show that TF-C outperforms baselines by 15. 4% (F1 score) on average in one-to-one settings (e. g., fine-tuning an EEG-pretrained model on EMG data) and by 8. 4% (precision) in challenging one-to-many settings (e. g., fine-tuning an EEG-pretrained model for either hand-gesture recognition or mechanical fault prediction), reflecting the breadth of scenarios that arise in real-world applications.
Multimodal Learning on Graphs for Disease Relation Extraction
On a dataset annotated by human experts, REMAP improves text-based disease relation extraction by 10. 0% (accuracy) and 17. 2% (F1-score) by fusing disease knowledge graphs with text information.
Rethinking Stability for Attribution-based Explanations
As attribution-based explanation methods are increasingly used to establish model trustworthiness in high-stakes situations, it is critical to ensure that these explanations are stable, e. g., robust to infinitesimal perturbations to an input.
Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs
Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life.
Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health
Human space exploration beyond low Earth orbit will involve missions of significant distance and duration.
Sparse dictionary learning recovers pleiotropy from human cell fitness screens
In high-throughput functional genomic screens, each gene product is commonly assumed to exhibit a singular biological function within a defined protein complex or pathway.
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
Here, we introduce RAINDROP, a graph neural network that embeds irregularly sampled and multivariate time series while also learning the dynamics of sensors purely from observational data.
Leveraging the Cell Ontology to classify unseen cell types
Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution.
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods
As Graph Neural Networks (GNNs) are increasingly being employed in critical real-world applications, several methods have been proposed in recent literature to explain the predictions of these models.
Deep Contextual Learners for Protein Networks
We construct a multi-scale network of the Human Cell Atlas and apply AWARE to learn protein, cell type, and tissue embeddings that uphold cell type and tissue hierarchies.
Graph Representation Learning in Biomedicine
Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge.
Structure Inducing Pre-Training
Based on this review, we introduce a descriptive framework for pre-training that allows for a granular, comprehensive understanding of how relational structure can be induced.
Towards a Unified Framework for Fair and Stable Graph Representation Learning
In this work, we establish a key connection between counterfactual fairness and stability and leverage it to propose a novel framework, NIFTY (uNIfying Fairness and stabiliTY), which can be used with any GNN to learn fair and stable representations.
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research.
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics.
Subgraph Neural Networks
Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks.
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
Here, we develop GNNGuard, a general algorithm to defend against a variety of training-time attacks that perturb the discrete graph structure.
Graph Meta Learning via Local Subgraphs
G-Meta learns how to quickly adapt to a new task using only a handful of nodes or edges in the new task and does so by learning from data points in other graphs or related, albeit disjoint label sets.
Open Graph Benchmark: Datasets for Machine Learning on Graphs
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks
Here, we present SkipGNN, a graph neural network approach for the prediction of molecular interactions.
DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery.
Ranked #2 on
Drug Discovery
on KIBA
Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections.
Interpretability of machine learning based prediction models in healthcare
There is a need of ensuring machine learning models that are interpretable.
Strategies for Pre-training Graph Neural Networks
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
Ranked #3 on
Molecular Property Prediction
on ToxCast
GNNExplainer: Generating Explanations for Graph Neural Networks
We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.
BIG-bench Machine Learning
Explainable artificial intelligence
+2
NIMFA: A Python Library for Nonnegative Matrix Factorization
NIMFA is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms.
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
In this Review, we describe the principles of data integration and discuss current methods and available implementations.
Embedding Logical Queries on Knowledge Graphs
Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities.
Ranked #6 on
Complex Query Answering
on FB15k-237
Network Enhancement: a general method to denoise weighted biological networks
Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome.
Prioritizing network communities
Community detection identifies candidate modules as hypotheses, which then need to be validated through experiments, such as mutagenesis in a biological laboratory.
Modeling polypharmacy side effects with graph convolutional networks
The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type.
Ranked #1 on
Link Prediction
on Decagon
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases.
Spectral Graph Wavelets for Structural Role Similarity in Networks
Nodes residing in different parts of a graph can have similar structural roles within their local network topology.
Large-scale analysis of disease pathways in the human interactome
However, we show that higher-order network structures, such as small subgraphs of the pathway, provide a promising direction for the development of new methods.
Learning Structural Node Embeddings Via Diffusion Wavelets
Nodes residing in different parts of a graph can have similar structural roles within their local network topology.
Jumping across biomedical contexts using compressive data fusion
Motivation: The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures.
Predicting multicellular function through multi-layer tissue networks
We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues.
