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Search · four archives
27 papers · ranked by Valyu relevance
Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis
Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into…
Kevin Bauer, Oliver Hinz, Wil van der Aalst, Christof Weinhardt
The examination and development of techniques that render the outputs of opaque, high performing machine learning models interpretable have gained increasing attention recently. A growing number of international conferences and workshops focus on sensitizing researchers and partitioners for the topic and combining…
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…
Mengnan Du, Ninghao Liu, Xia Hu
Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still…
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…
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…
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…
Numair Sani, Jaron Lee, Razieh Nabi, Ilya Shpitser
Black-box models in machine learning have demonstrated excellent predictive performance in complex problems and high-dimensional settings. However, their lack of transparency and interpretability restrict the applicability of such models in critical decision-making processes. In order to combat this shortcoming, we…
Guandong Xu, Tri Dung Duong, Qian Li, Shaowu Liu + 1 more
—Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc. In spite of their great performance in different sectors, researchers still concern about the mechanism under any machine…
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…
Mateusz Garbulowski, Klev Diamanti, Karolina Smolińska, Nicholas Baltzer + 5 more
For machine learning to matter beyond intellectual curiosity, the models developed therefrom must be adopted within the greater scientific community. In this study, we developed an interpretable machine learning framework that allows identification of semantics from various datatypes. Our package can analyze and…
Cynthia Rudin
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying…
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…
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…
Abdul Karim, Avinash Mishra, M. A. Hakim Newton, Abdul Sattar
The term "interpretability" is oftenly used by machine learning researchers each with their own intuitive understanding of it. There is no universal well agreed upon definition of interpretability in machine learning. As any type of science discipline is mainly driven by the set of formulated questions rather than by…
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…
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…
Zohreh Shams, Botty Dimanov, Sumaiyah Kola, Nikola Simidjievski + 6 more
Deep learning models are receiving increasing attention in clinical decision-making, however the lack of interpretability and explainability impedes their deployment in day-to-day clinical practice. We propose REM, an interpretable and explainable methodology for extracting rules from deep neural networks and combining…
Ričards Marcinkevičs, Julia E. Vogt
In this review, we examine the problem of designing interpretable and explainable machine learning models. Interpretability and explainability lie at the core of many machine learning and statistical applications in medicine, economics, law, and natural sciences. Although interpretability and explainability have…
D. Petkovic, A. Alavi, D. Cai, J. Yang + 1 more
Machine Learning (ML) is becoming an increasingly critical technology in many areas. However, its complexity and its frequent non-transparency create significant challenges, especially in the biomedical and health areas. One of the critical components in addressing the above challenges is the explainability or…
Etienne Thoret, Thomas Andrillon, Damien Léger, Daniel Pressnitzer
Many scientific fields now use machine-learning tools to assist with complex classification tasks. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. However, such tools often remain…
John Mitros, Brian Mac Namee
The ubiquity of machine learning based predictive models in modern society naturally leads people to ask how trustworthy those models are? In predictive modeling, it is quite common to induce a trade-off between accuracy and interpretability. For instance, doctors would like to know how effective some treatment will be…
Steven Torrisi, Matthew Carbone, Brian Rohr, Joseph H. Montoya + 4 more
X-ray absorption spectroscopy (XAS) produces a wealth of information about the local structure of materials, but interpretation of spectra often relies on easily accessible trends and prior assumptions about the structure. Recently, researchers have demonstrated that machine learning models can automate this process to…
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
We present a transferable, interpretable, and modular machine-learning framework that enhances the accuracy of density functional theory (DFT) reaction energies using physically meaningful energy-decomposition descriptors. Reaction energies computed at the DFT level with standard basis sets are first decomposed into…
Prashanth Athri, Vidhya Murali, Pradyumna Y Muralidhar, Cassandra Königs + 4 more
- 1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India - 2. PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India - 3. Bioinformatics and Medical Informatics, Bielefeld University…
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…