Artificial Intelligence & Machine Learning Methods
Our GitHub Spaces
Zero-Shot Prediction of Therapeutic Use with Geometric Deep Learning and Clinician Centered Design
Contextualizing Protein Representations Using Deep Learning on Interactomes and Single-Cell Experiments
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Domain Adaptation for Time Series Under Feature and Label Shifts
Deep Learning for Diagnosing Patients with Rare Genetic Diseases
Similarity Search in Heterogeneous Knowledge Graphs via Meta Paths
Phenotype Discovery in Molecular Interaction Networks
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
Therapeutics Data Commons
Machine Learning Datasets and Tasks for Drug Discovery and Development
Deep learning library for drug-target interaction prediction and applications to drug repurposing and virtual screening
Graph Query Embeddings
Method for embedding logical queries on knowledge graphs
Network-Guided Matrix Completion
Method for probabilistic prediction and imputation of interactions using prior knowledge
Deep multi-task learning for cross-type biomedical named entity recognition
May 2023: Congratulations to Ada and Michelle
Congrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow!
Apr 2023: Universal Domain Adaptation at ICML 2023
New paper introducing the first model for closed-set and universal domain adaptation on time series accepted at ICML 2023. Raincoat addresses feature and label shifts and can detect private labels. Project website.
Apr 2023: Celebrating Achievements of Our Undergrads
Undergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological & Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.
Apr 2023: Welcoming a New Postdoctoral Fellow
An enthusiastic welcome to Tianlong Chen, our newly appointed postdoctoral fellow.
Apr 2023: New Study in Nature Machine Intelligence
New paper in Nature Machine Intelligence introducing the blueprint for multimodal learning with graphs.
Mar 2023: Precision Health in Nature Machine Intelligence
New paper with NASA in Nature Machine Intelligence on biomonitoring and precision health in deep space supported by artificial intelligence.
Mar 2023: Self-Driving Labs in Nature Machine Intelligence
New paper with NASA in Nature Machine Intelligence on biological research and self-driving labs in deep space supported by artificial intelligence.
Mar 2023: TxGNN - Zero-shot prediction of therapeutic use
New study on zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design. Check out our project website and TxGNN Explorer.
Mar 2023: GraphXAI published in Scientific Data
Our approach evaluating explainability of geometric deep learning models is published in Scientific Data. Project website.
Feb 2023: Welcoming New Postdoctoral Fellows
A warm welcome to postdoctoral fellows Wanxiang Shen and Ruth Johnson. Congratulations to Ruthie for being named a Berkowitz Fellow.
Feb 2023: New Preprint on Distribution Shifts
Feb 2023: PrimeKG published in Scientific Data
Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.
Jan 2023: GNNDelete published at ICLR 2023
New paper on machine unlearning for graph neural networks accepted at ICLR 2023. Project website.
Jan 2023: New Network Principle for Molecular Phenotypes
New paper introducing mutual interactor-based GNN for molecular phenotype prediction at PSB. Project website.
Dec 2022: Can we shorten rare disease diagnostic odyssey?
New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.
Nov 2022: Can AI transform the way we discover new drugs?
Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.
Oct 2022: New Paper in Nature Biomedical Engineering
New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.
Sep 2022: New Paper in Nature Chemical Biology
Our paper on artificial intelligence foundation for therapeutic science is published in Nature Chemical Biology.
Sep 2022: Self-Supervised Pre-Training at NeurIPS 2022
New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.
Sep 2022: Best Paper Honorable Mention Award at IEEE VIS
Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.