TxGNN
Zero-Shot Prediction of Therapeutic Use with Geometric Deep Learning and Clinician Centered Design
PINNACLE
Contextualizing Protein Representations Using Deep Learning on Interactomes and Single-Cell Experiments
TimeX
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Raincoat
Domain Adaptation for Time Series Under Feature and Label Shifts
SHEPHERD
Deep Learning for Diagnosing Patients with Rare Genetic Diseases
metapaths
Similarity Search in Heterogeneous Knowledge Graphs via Meta Paths
Mutual Interactors
Phenotype Discovery in Molecular Interaction Networks
Raindrop
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
Therapeutics Data Commons
Machine Learning Datasets and Tasks for Drug Discovery and Development
DeepPurpose
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
Multi-BioNER
Deep multi-task learning for cross-type biomedical named entity recognition
Latest News
Sep 2023: New papers accepted at NeurIPS
Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers, which are among the top 3% of all submissions, focus on explaining sequence models through self-supervised learning and the full-atom design of protein pockets.
Sep 2023: Future Directions in Network Biology
Excited to share our perspectives on current and future directions in network biology.
Aug 2023: Scientific Discovery in the Age of AI
New paper on the role of artificial intelligence in scientific discovery is published in Nature.
Jul 2023: PINNACLE - Contextual AI protein model
PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.
Jun 2023: Our Group is Joining the Kempner Institute
Excited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.
Jun 2023: Welcoming a New Postdoctoral Fellow
An enthusiastic welcome to Shanghua Gao who is joining our group as a postdoctoral research fellow.
Jun 2023: On Pretraining in Nature Machine Intelligence
Excited to share our new study on language model pretraining and general-purpose methods for biological sequences. Project website.
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.
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