6,060 research outputs found
MEGAN: Multi-Explanation Graph Attention Network
Explainable artificial intelligence (XAI) methods are expected to improve
trust during human-AI interactions, provide tools for model analysis and extend
human understanding of complex problems. Explanation-supervised training allows
to improve explanation quality by training self-explaining XAI models on ground
truth or human-generated explanations. However, existing explanation methods
have limited expressiveness and interoperability due to the fact that only
single explanations in form of node and edge importance are generated. To that
end we propose the novel multi-explanation graph attention network (MEGAN). Our
fully differentiable, attention-based model features multiple explanation
channels, which can be chosen independently of the task specifications. We
first validate our model on a synthetic graph regression dataset. We show that
for the special single explanation case, our model significantly outperforms
existing post-hoc and explanation-supervised baseline methods. Furthermore, we
demonstrate significant advantages when using two explanations, both in
quantitative explanation measures as well as in human interpretability.
Finally, we demonstrate our model's capabilities on multiple real-world
datasets. We find that our model produces sparse high-fidelity explanations
consistent with human intuition about those tasks and at the same time matches
state-of-the-art graph neural networks in predictive performance, indicating
that explanations and accuracy are not necessarily a trade-off.Comment: 9 pages main text, 29 pages total, 19 figure
Graph Representation Learning in Biomedicine
Biomedical networks are universal descriptors of systems of interacting
elements, from protein interactions to disease networks, all the way to
healthcare systems and scientific knowledge. With the remarkable success of
representation learning in providing powerful predictions and insights, we have
witnessed a rapid expansion of representation learning techniques into
modeling, analyzing, and learning with such networks. In this review, we put
forward an observation that long-standing principles of networks in biology and
medicine -- while often unspoken in machine learning research -- can provide
the conceptual grounding for representation learning, explain its current
successes and limitations, and inform future advances. We synthesize a spectrum
of algorithmic approaches that, at their core, leverage graph topology to embed
networks into compact vector spaces, and capture the breadth of ways in which
representation learning is proving useful. Areas of profound impact include
identifying variants underlying complex traits, disentangling behaviors of
single cells and their effects on health, assisting in diagnosis and treatment
of patients, and developing safe and effective medicines
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has
contributed significantly to breakthroughs in various fields, including
biomedicine. The objective of this survey is to review the latest advancements
in GRL methods and their applications in the biomedical field. We also
highlight key challenges currently faced by GRL and outline potential
directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic
Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphs
Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in
drug discovery and pharmaceutical research as they provide a structured way to
integrate diverse information sources, enhancing AI system interpretability.
This interpretability is crucial in healthcare, where trust and transparency
matter, and eXplainable AI (XAI) supports decision making for healthcare
professionals. This overview summarizes recent literature on the impact of KGs
in healthcare and their role in developing explainable AI models. We cover KG
workflow, including construction, relationship extraction, reasoning, and their
applications in areas like Drug-Drug Interactions (DDI), Drug Target
Interactions (DTI), Drug Development (DD), Adverse Drug Reactions (ADR), and
bioinformatics. We emphasize the importance of making KGs more interpretable
through knowledge-infused learning in healthcare. Finally, we highlight
research challenges and provide insights for future directions.Comment: IEEE AIKE 2023, 8 Page
Learning Invariant Molecular Representation in Latent Discrete Space
Molecular representation learning lays the foundation for drug discovery.
However, existing methods suffer from poor out-of-distribution (OOD)
generalization, particularly when data for training and testing originate from
different environments. To address this issue, we propose a new framework for
learning molecular representations that exhibit invariance and robustness
against distribution shifts. Specifically, we propose a strategy called
``first-encoding-then-separation'' to identify invariant molecule features in
the latent space, which deviates from conventional practices. Prior to the
separation step, we introduce a residual vector quantization module that
mitigates the over-fitting to training data distributions while preserving the
expressivity of encoders. Furthermore, we design a task-agnostic
self-supervised learning objective to encourage precise invariance
identification, which enables our method widely applicable to a variety of
tasks, such as regression and multi-label classification. Extensive experiments
on 18 real-world molecular datasets demonstrate that our model achieves
stronger generalization against state-of-the-art baselines in the presence of
various distribution shifts. Our code is available at
https://github.com/HICAI-ZJU/iMoLD
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μ μΌλ‘ μμ°¨μ μΈ μλ£ κΈ°λ‘μ ν¨μΆν νμ ννκ³Ό λλΆμ΄ κ°μΈνλ μν μ§μμ ν¨μΆν ννμ ν¨κ» μ¬μ©νμ¬ ν₯ν μ§λ³ λ° μ§λ¨ μμΈ‘ λ¬Έμ μ νμ©νμλ€.This dissertation proposes a deep neural network-based medical concept and patient representation learning methods using medical claims data to solve two healthcare tasks, i.e., clinical outcome prediction and post-marketing adverse drug reaction (ADR) signal detection. First, we propose SAF-RNN, a Recurrent Neural Network (RNN)-based model that learns a deep patient representation based on the clinical sequences and patient characteristics. Our proposed model fuses different types of patient records using feature-based gating and self-attention. We demonstrate that high-level associations between two heterogeneous records are effectively extracted by our model, thus achieving state-of-the-art performances for predicting the risk probability of cardiovascular disease. Secondly, based on the observation that the distributed medical code embeddings represent temporal proximity between the medical codes, we introduce a graph structure to enhance the code embeddings with such temporal information. We construct a graph using the distributed code embeddings and the statistical information from the claims data. We then propose the Graph Neural Network(GNN)-based representation learning for post-marketing ADR detection. Our model shows competitive performances and provides valid ADR candidates. Finally, rather than using patient records alone, we utilize a knowledge graph to augment the patient representation with prior medical knowledge. Using SAF-RNN and GNN, the deep patient representation is learned from the clinical sequences and the personalized medical knowledge. It is then used to predict clinical outcomes, i.e., next diagnosis prediction and CVD risk prediction, resulting in state-of-the-art performances.1 Introduction 1
2 Background 8
2.1 Medical Concept Embedding 8
2.2 Encoding Sequential Information in Clinical Records 11
3 Deep Patient Representation with Heterogeneous Information 14
3.1 Related Work 16
3.2 Problem Statement 19
3.3 Method 20
3.3.1 RNN-based Disease Prediction Model 20
3.3.2 Self-Attentive Fusion (SAF) Encoder 23
3.4 Dataset and Experimental Setup 24
3.4.1 Dataset 24
3.4.2 Experimental Design 26
ii 3.4.3 Implementation Details 27
3.5 Experimental Results 28
3.5.1 Evaluation of CVD Prediction 28
3.5.2 Sensitivity Analysis 28
3.5.3 Ablation Studies 31
3.6 Further Investigation 32
3.6.1 Case Study: Patient-Centered Analysis 32
3.6.2 Data-Driven CVD Risk Factors 32
3.7 Conclusion 33
4 Graph-Enhanced Medical Concept Embedding 40
4.1 Related Work 42
4.2 Problem Statement 43
4.3 Method 44
4.3.1 Code Embedding Learning with Skip-gram Model 44
4.3.2 Drug-disease Graph Construction 45
4.3.3 A GNN-based Method for Learning Graph Structure 47
4.4 Dataset and Experimental Setup 49
4.4.1 Dataset 49
4.4.2 Experimental Design 50
4.4.3 Implementation Details 52
4.5 Experimental Results 53
4.5.1 Evaluation of ADR Detection 53
4.5.2 Newly-Described ADR Candidates 54
4.6 Conclusion 55
5 Knowledge-Augmented Deep Patient Representation 57
5.1 Related Work 60
5.1.1 Incorporating Prior Medical Knowledge for Clinical Outcome Prediction 60
5.1.2 Inductive KGC based on Subgraph Learning 61
5.2 Method 61
5.2.1 Extracting Personalized KG 61
5.2.2 KA-SAF: Knowledge-Augmented Self-Attentive Fusion Encoder 64
5.2.3 KGC as a Pre-training Task 68
5.2.4 Subgraph Infomax: SGI 69
5.3 Dataset and Experimental Setup 72
5.3.1 Clinical Outcome Prediction 72
5.3.2 Next Diagnosis Prediction 72
5.4 Experimental Results 73
5.4.1 Cardiovascular Disease Prediction 73
5.4.2 Next Diagnosis Prediction 73
5.4.3 KGC on SemMed KG 73
5.5 Conclusion 74
6 Conclusion 77
Abstract (In Korean) 90
Acknowlegement 92λ°
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
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