Domain Invariant Learning for Gaussian Processes and Bayesian Exploration

Out-of-distribution (OOD) generalization has long been a challenging problem that remains largely unsolved. Gaussian processes (GP), as popular probabilistic model classes, especially in the small data regime, presume strong OOD generalization abilities. Surprisingly, their OOD generalization abilities have been under-explored before compared with other lines of GP research. In this paper, we identify that GP is not free from the problem and propose a domain invariant learning algorithm for Gaussian processes (DIL-GP) with a min-max optimization on the likelihood. DIL-GP discovers the heterogeneity in the data and forces invariance across partitioned subsets of data. We further extend the DIL-GP to improve Bayesian optimization's adaptability on changing environments. Numerical experiments demonstrate the superiority of DIL-GP for predictions on several synthetic and real-world datasets. We further demonstrate the effectiveness of the DIL-GP Bayesian optimization method on a PID parameters tuning experiment for a quadrotor. The full version and source code are available at: https://github.com/Billzxl/DIL-GP.

DiffDTM: A conditional structure-free framework for bioactive molecules generation targeted for dual proteins

Advances in deep generative models shed light on de novo molecule generation with desired properties. However, molecule generation targeted for dual protein targets still faces formidable challenges including protein 3D structure data requisition for model training, auto-regressive sampling, and model generalization for unseen targets. Here, we proposed DiffDTM, a novel conditional structure-free deep generative model based on a diffusion model for dual targets based molecule generation to address the above issues. Specifically, DiffDTM receives protein sequences and molecular graphs as inputs instead of protein and molecular conformations and incorporates an information fusion module to achieve conditional generation in a one-shot manner. We have conducted comprehensive multi-view experiments to demonstrate that DiffDTM can generate drug-like, synthesis-accessible, novel, and high-binding affinity molecules targeting specific dual proteins, outperforming the state-of-the-art (SOTA) models in terms of multiple evaluation metrics. Furthermore, we utilized DiffDTM to generate molecules towards dopamine receptor D2 and 5-hydroxytryptamine receptor 1A as new antipsychotics. The experimental results indicate that DiffDTM can be easily plugged into unseen dual targets to generate bioactive molecules, addressing the issues of requiring insufficient active molecule data for training as well as the need to retrain when encountering new targets.

Revisiting Automatic Question Summarization Evaluation in the Biomedical Domain

Automatic evaluation metrics have been facilitating the rapid development of automatic summarization methods by providing instant and fair assessments of the quality of summaries. Most metrics have been developed for the general domain, especially news and meeting notes, or other language-generation tasks. However, these metrics are applied to evaluate summarization systems in different domains, such as biomedical question summarization. To better understand whether commonly used evaluation metrics are capable of evaluating automatic summarization in the biomedical domain, we conduct human evaluations of summarization quality from four different aspects of a biomedical question summarization task. Based on human judgments, we identify different noteworthy features for current automatic metrics and summarization systems as well. We also release a dataset of our human annotations to aid the research of summarization evaluation metrics in the biomedical domain.

Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling

Molecular dynamic simulations are important in computational physics, chemistry, material, and biology. Machine learning-based methods have shown strong abilities in predicting molecular energy and properties and are much faster than DFT calculations. Molecular energy is at least related to atoms, bonds, bond angles, torsion angles, and nonbonding atom pairs. Previous Transformer models only use atoms as inputs which lack explicit modeling of the aforementioned factors. To alleviate this limitation, we propose Moleformer, a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them using rotational and translational invariant geometry-aware spatial encoding. Proposed spatial encoding calculates relative position information including distances and angles among nodes and edges. We benchmark Moleformer on OC20 and QM9 datasets, and our model achieves state-of-the-art on the initial state to relaxed energy prediction of OC20 and is very competitive in QM9 on predicting quantum chemical properties compared to other Transformer and Graph Neural Network (GNN) methods which proves the effectiveness of the proposed geometry-aware spatial encoding in Moleformer.

COVID-19 SignSym: A fast adaptation of general clinical NLP tools to identify and normalize COVID-19 signs and symptoms to OMOP common data model

The COVID-19 pandemic swept across the world rapidly infecting millions of people. An efficient tool that can accurately recognize important clinical concepts of COVID-19 from free text in electronic health records will be significantly valuable to accelerate various applications of COVID-19 research. To this end, the existing clinical NLP tool CLAMP was quickly adapted to COVID-19 information and generated an automated tool called COVID-19 SignSym, which can extract and signs/symptoms and their eight attributes such as temporal information and negations from clinical text. The extracted information is also mapped to standard clinical concepts in the common data model of OHDSI OMOP. Evaluation on clinical notes and medical dialogues demonstrated promising results. It is freely accessible to the community as a downloadable package of APIs (https://clamp.uth.edu/covid/nlp.php). We believe COVID-19 SignSym will provide fundamental supports to the secondary use of EHRs, thus accelerating the global research of COVID-19.