research
∙
06/03/2023
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Interpreting time series models is uniquely challenging because it requi...
share
research
∙
02/06/2023
Domain Adaptation for Time Series Under Feature and Label Shifts
The transfer of models trained on labeled datasets in a source domain to...
research
∙
08/19/2022
Evaluating Explainability for Graph Neural Networks
As post hoc explanations are increasingly used to understand the behavio...
research
∙
06/22/2022
OpenXAI: Towards a Transparent Evaluation of Model Explanations
While several types of post hoc explanation methods (e.g., feature attri...
research
∙
06/17/2022
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
Pre-training on time series poses a unique challenge due to the potentia...
research
∙
03/16/2022
Multimodal Learning on Graphs for Disease Relation Extraction
Objective: Disease knowledge graphs are a way to connect, organize, and ...
research
∙
03/14/2022
Rethinking Stability for Attribution-based Explanations
As attribution-based explanation methods are increasingly used to establ...
research
∙
12/22/2021
Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health
Human space exploration beyond low Earth orbit will involve missions of ...
research
∙
12/22/2021
Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs
Space biology research aims to understand fundamental effects of spacefl...
research
∙
10/11/2021
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
In many domains, including healthcare, biology, and climate science, tim...
research
∙
06/16/2021
Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations
As Graph Neural Networks (GNNs) are increasingly employed in real-world ...
research
∙
06/04/2021
Deep Contextual Learners for Protein Networks
Spatial context is central to understanding health and disease. Yet refe...
research
∙
04/11/2021
Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities
With the remarkable success of representation learning in providing powe...
research
∙
03/18/2021
Rethinking Relational Encoding in Language Model: Pre-Training for General Sequences
Language model pre-training (LMPT) has achieved remarkable results in na...
research
∙
02/25/2021
Towards a Unified Framework for Fair and Stable Graph Representation Learning
As the representations output by Graph Neural Networks (GNNs) are increa...
research
∙
01/11/2021
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
Machine Learning (ML) models typically require large-scale, balanced tra...
research
∙
10/05/2020
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
The efficacy of a drug depends on its binding affinity to the therapeuti...
research
∙
06/18/2020
Subgraph Neural Networks
Deep learning methods for graphs achieve remarkable performance on many ...
research
∙
06/15/2020
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
Deep learning methods for graphs achieve remarkable performance on many ...
research
∙
06/14/2020
Graph Meta Learning via Local Subgraphs
Prevailing methods for graphs require abundant label and edge informatio...
research
∙
05/02/2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs
We present the Open Graph Benchmark (OGB), a diverse set of challenging ...
research
∙
04/30/2020
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks
Molecular interaction networks are powerful resources for the discovery....
research
∙
04/15/2020
Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
The COVID-19 pandemic demands the rapid identification of drug-repurpusi...
research
∙
02/20/2020
Interpretability of machine learning based prediction models in healthcare
There is a need of ensuring machine learning models that are interpretab...
research
∙
05/29/2019
Pre-training Graph Neural Networks
Many applications of machine learning in science and medicine, including...
research
∙
03/10/2019
GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks
Graph Neural Networks (GNNs) are a powerful tool for machine learning on...
research
∙
08/06/2018
NIMFA: A Python Library for Nonnegative Matrix Factorization
NIMFA is an open-source Python library that provides a unified interface...
research
∙
06/30/2018
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
New technologies have enabled the investigation of biology and human hea...
research
∙
06/05/2018
Querying Complex Networks in Vector Space
Learning vector embeddings of complex networks is a powerful approach us...
research
∙
05/09/2018
Network Enhancement: a general method to denoise weighted biological networks
Networks are ubiquitous in biology where they encode connectivity patter...
research
∙
05/07/2018
Prioritizing network communities
Uncovering modular structure in networks is fundamental for advancing th...
research
∙
02/02/2018
Modeling polypharmacy side effects with graph convolutional networks
The use of multiple drugs, termed polypharmacy, is common to treat patie...
research
∙
01/30/2018
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Motivation: Biomedical named entity recognition (BioNER) is the most fun...
research
∙
10/27/2017
Spectral Graph Wavelets for Structural Role Similarity in Networks
Nodes residing in different parts of a graph can have similar structural...
research
∙
08/10/2017
Jumping across biomedical contexts using compressive data fusion
Motivation: The rapid growth of diverse biological data allows us to con...
research
∙
07/14/2017
Predicting multicellular function through multi-layer tissue networks
Motivation: Understanding functions of proteins in specific human tissue...
research
∙
07/02/2013