27 papers · ranked by Valyu relevance
Rikab Gambhir, Luisa Lucie-Smith, Jesse Thaler
We review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning the structural transparency of a model (the ability to understand or approximate its inner workings) and explainability as concerning the scientific content of a model…
Laura Moss, David Corsar, Martin Shaw, Ian Piper + 1 more
'Christopher Hawthorne'] Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously been developed. More recently, techniques from the field of machine learning have been applied to neurocritical care patient data to develop models…
Tim Räz
The interpretability of ML models is important, but it is not clear what it amounts to. So far, most philosophers have discussed the lack of interpretability of black-box models such as neural networks, and methods such as explainable AI that aim to make these models more transparent. The goal of this paper is to…
Olga Ciobanu-Caraus, Anatol Aicher, Julius M. Kernbach, Luca Regli + 2 more
'Carlo Serra' 'Victor E. Staartjes'] Over the past two decades, advances in computational power and data availability combined with increased accessibility to pre-trained models have led to an exponential rise in machine learning (ML) publications. While ML may have the potential to transform healthcare, this sharp…
Mohammad Ennab, Hamid Mcheick
Artificial Intelligence (AI) has demonstrated exceptional performance in automating critical healthcare tasks, such as diagnostic imaging analysis and predictive modeling, often surpassing human capabilities. The integration of AI in healthcare promises substantial improvements in patient outcomes, including faster…
Yongbing Zhao, Jinfeng Shao, Yan W Asmann
While explainable artificial intelligence has emerged with aim at interpreting how the machine learning models make decisions, many model explainers have been developed in computer vision field. By far, there still lacks an understanding of the applicability of these model explainers in biological study. To address…
Sheng-Chieh Lu, Christine L. Swisher, Caroline Chung, David Jaffray + 1 more
'Chris Sidey-Gibbons'] Machine learning-based tools are capable of guiding individualized clinical management and decision-making by providing predictions of a patient’s future health state. Through their ability to model complex nonlinear relationships, ML algorithms can often outperform traditional statistical…
Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu + 4 more
'Jiang Bian' 'Dejing Dou'] Abstract Deep neural networks have been well-known for their superb handling of various machine learning and artificial intelligence tasks. However, due to their over-parameterized black-box nature, it is often difficult to understand the prediction results of deep models. In recent years…
Frans van der Sluis, Egon L. van den Broek
Title: Summary Balancing prediction accuracy, model interpretability, and domain generalization (also known as [a.k.a.] out-of-distribution testing/evaluation) is a central challenge in machine learning. To assess this challenge, we took 120 interpretable and 166 opaque models from 77,640 tuned configurations…
Mohammad Ennab, Hamid Mcheick, Jae-Ho Han
The lack of interpretability in artificial intelligence models (i.e., deep learning, machine learning, and rules-based) is an obstacle to their widespread adoption in the healthcare domain. The absence of understandability and transparency frequently leads to (i) inadequate accountability and (ii) a consequent…
Marina Dubova, Suyog Chandramouli, Gerd Gigerenzer, Peter Grünwald + 11 more
'William Holmes' 'Tania Lombrozo' 'Marco Marelli' 'Sebastian Musslick' 'Bruno Nicenboim' 'Lauren N. Ross' 'Richard Shiffrin' 'Martha White' 'Eric-Jan Wagenmakers' 'Paul-Christian Bürkner' 'Sabina J. Sloman'] The preference for simple explanations, known as the parsimony principle, has long guided the development of…
Conor Rowan, Alireza Doostan
In the context of scientific machine learning (SciML), the "black box" nature of models involving neural networks makes researchers uneasy. Though neural networks trained on large data sets have been successfully used to describe and predict many physical phenomena, there is a sense that, unlike traditional scientific…
Benjamin Leblanc, Pascal Germain
Interpretability and explainability have gained more and more attention in the field of machine learning as they are crucial when it comes to high-stakes decisions and troubleshooting. Since both provide information about predictors and their decision process, they are often seen as two independent means for one single…
Mehrshad Sadria, Anita Layton, Gary D. Bader
For predictive computational models to be considered reliable in crucial areas such as biology and medicine, it is essential for them to be accurate, robust, and interpretable. A sufficiently robust model should not have its output affected significantly by a slight change in the input. Also, these models should be…
Manu Aggarwal, NG Cogan, Vipul Periwal
Deep neural networks (DNNs) are powerful tools for data-driven predictive machine learning, but their complex architecture obscures mechanistic relations that they have learned from data. This information is critical to the scientific method of hypotheses development, experiment design, and model validation, especially…
Valerie Chen, Muyu Yang, Wenbo Cui, Joon Sik Kim + 2 more
Advances in machine learning (ML) have enabled the development of next-generation prediction models for complex computational biology problems. These developments have spurred the use of interpretable machine learning (IML) to unveil fundamental biological insights through data-driven knowledge discovery. However, in…
Pranjal Atrey, Michael P. Brundage, Min Wu, Sanghamitra Dutta
Interpretable machine learning models offer understandable reasoning behind their decision-making process, though they may not always match the performance of their blackbox counterparts. This trade-off between interpretability and model performance has sparked discussions around the deployment of AI, particularly in…
Suvo Banik, Karthik Balasubramanian, Sukriti Manna, Sybil Derrible + 1 more
Identifying key descriptors and understanding important features across different classes of materials are crucial for machine learning (ML) tools to both predict material properties and reveal the physics underlying any process of interest. Traditionally, the predictive modeling of elastic properties of materials is…
Haomiao Wang, Julien Aligon, Julien May, Emmanuel Doumard + 6 more
Although the benefits of machine learning (ML) are undeniable in health-care, explainability plays a vital role in improving transparency and understanding the most decisive and persuasive variables for prediction. The challenge is to identify explanations that make sense to the biomedical expert. This work proposes…
David Martínez-Enguita, Thomas Hillerton, Julia Åkesson, Maria Lerm + 1 more
Genome-scale DNA methylation (DNAm) profiles capture organismal physiology, but most predictive models lack transparency and multi-level applicability. Here we develop an explainable framework that quantifies respiratory, cardiovascular, and metabolic status as bounded health scores (0–1) derived from sex-specific…
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Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations. In this perspective, we critically examine the integration of machine learning into clinical proteomics…
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Supervised deep learning has become a standard approach to deliver competitive predictive tools that allow relating the structure of molecules and their physicochemical features to properties such as binding to protein targets, performance as electronic materials, and reactivity. However, efforts to understand how…
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Explainability methods in machine learning-driven research are increasingly being used, but it remains challenging to assess their reliability without deeply investigating the specific problem at hand. In this work, we present a Python-based Workflow for Interpretability Scoring using matched molecular Pairs (WISP).…
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While metallodrugs have been known for decades, developing new effective formulations still remains a challenge, underscoring the need for new tools to assist identification of potent metal-containing anticancer agents. In this work, we developed a straightforward data-driven approach to predict cytotoxicity of metal…
Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang + 1 more
Discovering meaningful molecules in the vast combinatorial chemical space has been a longstanding challenge in many fields from materials science to drug discovery. Recent advances in machine learning, especially generative models, have made remarkable progress and demonstrate considerable promise for automated…
Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
The field of neuroimaging has increasingly sought to develop artificial intelligence-based models for neurological and neuropsychiatric disorder automated diagnosis and clinical decision support. However, if these models are to be implemented in a clinical setting, transparency will be vital. Two aspects of…
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Predicting molecular dipole moments is essential for quantum chemistry and materials science applications. In this study, we introduce Q-DFTNet: a Chemistry-Informed Neural Network framework designed to systematically benchmark and interpret graph neural networks (GNNs) for molecular dipole prediction. Seven GNN…