The COVID-19 pandemic has reshaped health and medicine in ways both dramatic and subtle. Some of the less obvious shifts can only emerge from analysis of millions of pieces of data—patient records, medical notes, clinical encounter reports. Check out the story in Harvard Medicine News highlighting our research.
New preprint! We introduce Raindrop, a graph-guided network for learning representations of irregularly sampled multivariate time series.
Hot off the press in Nature Computational Science! We develop an algorithmic approach for massive analysis of drug adverse events. Our analyses of 10,443,476 adverse event reports have implications for safe medication use and public health policy, and can enable comparison of COVID-19 pandemic to other health emergencies.
Hot off the press in Nature Communications! We developed OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology.
We will be organizing a meeting on Trustworthy AI for Healthcare at AAAI 2022. Stay tuned for details and call for papers.
Our latest paper on Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development will appear at NeurIPS. We are excited to contribute novel datasets and benchmarks in the broad area of therapeutics.
We are organizing the AI for Science workshop at NeurIPS 2021 and have a stellar lineup of invited speakers.
Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at the ICML Interpretable ML in Healthcare Workshop. Stay tuned for more news on this evolving project.
We are excited to be at ICML 2021 where we will present 1 paper at Workshop on Socially Responsible Machine Learning, 1 paper at Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI, 2 papers at Workshop on Interpretable Machine Learning in Healthcare, and 1 paper at Workshop on Computational Biology. Congratulations to fantastic students!
We introduce the first axiomatic framework for theoretically analyzing, evaluating, and comparing GNN explanation methods. We formalize key properties that all methods should satisfy to generate reliable explanations: faithfulness, stability, and fairness.
New preprint on contextualized protein embeddings aims to characterize genes with disease-specific interactions and elucidate disease manifestation in specific cell types.
Our unified framework for fair and stable graph representation learning has just been accepted at UAI. We establish a theoretical connection between counterfactual fairness and stability and use it in a framework that can be used with any GNN to learn fair and stable embeddings.
Hot off the press! We deployed AI/ML and network medicine algorithms to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. We screened in human cells the top-ranked drugs, identifying six drugs that reduced viral infection, four of which could be repurposed to treat COVID-19.
In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.
We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.
Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!
Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.
We are excited to share slides from our recent lecture on Graph Neural Networks in Computational Biology, which we gave at Stanford ML for Graphs course.
We are excited to share the preprint on Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics. TDC is available at http://tdcommons.ai.
The new algorithmic approach investigates population-scale patient safety data and reveals inequalities in adverse events before and during COVID-19 pandemic.
We are excited to co-organize Workshop on AI in Health: Transferring and Integrating Knowledge for Better Health at the Web (WWW) conference. The call for papers is open! We also announce the AI in Health Data Challenge.
We are excited to see published our paper on DeepPurpose, a deep learning library for drug-target interaction prediction, and our paper on skipGNN, a graph neural network for predicting molecular interactions.
Our research won the Bayer Early Excellence in Science Award. We are honored to have received this recognition!
We are thrilled to announce Therapeutics Data Commons (TDC)! We invite you to join TDC. TDC is an open-source and community-driven effort.
On behalf of the NSF, we are organizing the National Symposium on Drug Repurposing for Future Pandemics. We have a stellar lineup of invited speakers! Register at www.drugsymposium.org.
Hot off the press! MARS, an approach for discovering novel cell types across heterogeneous single-cell experiments is just published in Nature Methods.
The MITxHarvard Women in AI initiative talked with Marinka about AI, machine learning, and the role of new technologies in biomedical research.
Tune in to the podcast with Marinka about machine learning to drug development. The discussion focuses on open research questions in the field, including how to limit the search space of high-throughput screens, design drugs entirely from scratch, and identify likely side-effects of combining drugs in novel ways.
We have a new opening for a postdoctoral research fellow in novel machine learning methods to combat COVID-19! Submit your application by September 1, 2020.
DeepPurpose is a deep learning library for drug-target interaction prediction and applications to drug repurposing and virtual screening.
Subgraph neural networks learn powerful subgraph representations that create fundamentally new opportunities for predictions beyond nodes, edges, and entire graphs.
GNNGuard can defend graph neural networks against a variety of training-time attacks. Remarkably, GNNGuard can restore state-of-the-art performance of any GNN in the face of adversarial attacks.
G-Meta is a meta-learning approach for graphs that quickly adapts to new prediction tasks using only a handful of data points. G-Meta works in most challenging, few-shot learning settings and scales to massive interactomics data as we show on our new Networks of Life dataset comprising of 1,840 networks.
A new paper introducing the Open Graph Benchmark, a diverse set of challenging and realistic benchmark datasets for graph machine learning.
Marinka is co-editing a special issue of IEEE Big Data on AI for COVID-19. In light of the urgent need for data-driven solutions to mitigate the COVID-19 pandemic, the special issue will aim for a fast-track peer review.
A new preprint describing a graph neural network approach for the prediction of molecular interactions, including drug-drug, drug-target, protein-protein, and gene-disease interactions.
The call for ECML-PKDD 2020 PhD Forum Track is now online. If you are a PhD student, submit your work on machine learning and knowledge discovery.
We are excited to share our latest results on how networks and graph machine-learning help us search for a cure for COVID-19.
We are joining AI Cures initiative at MIT! We will develop machine learning methods for finding promising antiviral molecules for COVID-19 and other emerging pathogens.