Search · four archives
Search · four archives
27 papers · ranked by Valyu relevance
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…
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…
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…
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…
Lukas-Valentin Herm, Kai Heinrich, Jonas Wanner, Christian Janiesch
ARTICLE INFO Keywords: Machine learning Explainability Performance Tradeoff XAI ABSTRACT Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance and explainability. Machine learning models with higher…
K. D. Ahlquist, Lauren Sugden, Sohini Ramachandran
Machine learning has become an important tool across biological disciplines, allowing researchers to draw conclusions from large datasets, and opening up new opportunities for interpreting complex and heterogeneous biological data. Alongside the rapid growth of machine learning, there have also been growing pains: some…
David Sidak, Jana Schwarzerová, Wolfram Weckwerth, Steffen Waldherr
Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by “omics” experiments seem endless, but are often not the main objective…
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…
Genevera I. Allen, Luqin Gan, Lili Zheng
New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only process, visualize, and make predictions from this big data, but also to make data-driven discoveries. These discoveries are often made using…
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…
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…
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…
David Medina-Ortiz, Ashkan Khalifeh, Hoda Anvari-Kazemabad, Mehdi D. Davari
Protein engineering using directed evolution and (semi)rational design has emerged as a powerful strategy for optimizing and enhancing enzymes or proteins with desired properties. Integrating artificial intelligence methods has further enhanced and accelerated protein engineering through predictive models developed in…
Olatomiwa O. Bifarin
Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve understanding of the underlying biomedical problem and to bolster confidence in these discoveries, model interpretability…
Authors not listed
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…
Anne-Marie Nussberger, Lan Luo, L. Elisa Celis, M. J. Crockett
As Artificial Intelligence (AI) proliferates across important social institutions, many of the most powerful AI systems available are difficult to interpret for end-users and engineers alike. Here, we sought to characterize public attitudes towards AI interpretability. Across seven studies (N = 2475), we demonstrate…
Sophie A. Martin, Florence J. Townend, Frederik Barkhof, James H. Cole
3.2### Risk of bias During screening, 200 papers were excluded because they did not address model interpretability. However, ascertaining what qualifies as interpretable raises important questions about what counts as an “explanation.” For instance, many studies involved the use of feature selection methods or…
Supriya Manna, Niladri Sett
Modern Education is not Modern without AI. However, AI's complex nature makes understanding and fixing problems challenging. Research worldwide shows that a parent's income greatly influences a child's education. This led us to explore how AI, especially complex models, makes important decisions using Explainable AI…
Oded Rotem, Tamar Schwartz, Ron Maor, Yishay Tauber + 5 more
The success of deep learning in identifying complex patterns exceeding human intuition comes at the cost of interpretability. Non-linear entanglement of image features makes deep learning a “black box” lacking human meaningful explanations for the models’ decision. We present DISCOVER, a generative model designed to…
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…
Piyush Borole, Tongjie Wang, Antonio Vergari, Ajitha Rajan
Survival analysis refers to statistical procedures used to analyze data that focuses on the time until an event occurs, such as death in cancer patients. Traditionally, the linear Cox Proportional Hazards (CPH) model is widely used due to its inherent interpretability. CPH model help identify key disease-associated…
Authors not listed
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).…
Marta S. Lemanczyk, Jakub M. Bartoszewicz, Bernhard Y. Renard
Post-hoc interpretability methods are commonly used to understand decisions of genomic deep learning models and reveal new biological insights. However, interactions between sequence regions (e.g. regulatory elements) impact the learning process as well as interpretability methods that are sensitive to dependencies…
Authors not listed
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…
Anubhav Jain
The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials…
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…