Search · four archives
Search · four archives
27 papers · ranked by Valyu relevance
Miyoung Kim, Shahin Atakishiyev, Housam Khalifa Bashier Babiker, Nawshad Farruque + 7 more
'Nawshad Farruque' 'Randy Goebel' 'Osmar R. Zäıane' 'Mohammad-Hossein Motallebi' 'Juliano Rabelo' 'Talat Iqbal Syed' 'Hengshuai Yao' 'Peter Chun'] The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine…
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…
Alena Kalyakulina, Igor Yusipov, Maria Giulia Bacalini, Claudio Franceschi + 2 more
DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific pipeline for joint data processing. We propose a comprehensive approach of combined DNA methylation datasets to classify…
Muhammad Salar Khan, Mehdi Nayebpour, Meng-Hao Li, Hadi El-Amine + 6 more
'Naoru Koizumi' 'James L. Olds' 'Isa Ebtehaj' 'Sayed M. Bateni' 'Babak Mohammadi' 'Stanislav N. Gorb'] European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect…
David B. Resnik, Mohammad Hosseini
Using artificial intelligence (AI) in research offers many important benefits for science and society but also creates novel and complex ethical issues. While these ethical issues do not necessitate changing established ethical norms of science, they require the scientific community to develop new guidance for the…
Mohammed Saidul Islam, Iqram Hussain, Md Mezbaur Rahman, Se Jin Park + 2 more
'Md Azam Hossain' 'Ki H. Chon'] State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. Users may…
Stephanie Baker, Wei Xiang
Artificial intelligence (AI) has been clearly established as a technology with the potential to revolutionize fields from healthcare to finance - if developed and deployed responsibly. This is the topic of responsible AI, which emphasizes the need to develop trustworthy AI systems that minimize bias, protect privacy…
Raymond Sheh, Isaac Monteath
Explainable Artificial Intelligence (XAI) has become popular in the last few years. The Artificial Intelligence (AI) community in general, and the Machine Learning (ML) community in particular, is coming to the realisation that in many applications, for AI to be trusted, it must not only demonstrate good performance in…
Elisabeth Hildt, Mohamed Shehata
This article reflects on explainability in the context of medical artificial intelligence (AI) applications, focusing on AI-based clinical decision support systems (CDSS). After introducing the concept of explainability in AI and providing a short overview of AI-based clinical decision support systems (CDSSs) and the…
Clive Gomes, Lalitha Natraj, Shijun Liu, Anushka Datta
In this survey paper, we deep dive into the field of Explainable Artificial Intelligence (XAI). After introducing the scope of this paper, we start by discussing what an "explanation" really is. We then move on to discuss some of the existing approaches to XAI and build a taxonomy of the most popular methods. Next, we…
Dennis Siegel, Christian Kraetzer, Stefan Seidlitz, Jana Dittmann + 1 more
In recent discussions in the European Parliament, the need for regulations for so-called high-risk artificial intelligence (AI) systems was identified, which are currently codified in the upcoming EU Artificial Intelligence Act (AIA) and approved by the European Parliament. The AIA is the first document to be turned…
Zahra Sadeghi, Roohallah Alizadehsani, Mehmet Akif Çifçi, Samina Kausar + 12 more
'Samina Kausar' 'Rizwan Rehman' 'Priyakshi Mahanta' 'Pranjal Kumar Bora' 'Ammar Almasri' 'Rami S. Alkhawaldeh' 'Sadiq Hussain' 'Bilal Alataş' 'Afshin Shoeibi' 'Hossein Moosaei' 'Milan Hladík' 'Saeid Nahavandi' 'Pãnos M. Pardalos'] XAI refers to the techniques and methods for building AI applications which assist end…
Alexander Blanchard, Mariarosaria Taddeo
Intelligence agencies have identified artificial intelligence (AI) as a key technology for maintaining an edge over adversaries. As a result, efforts to develop, acquire, and employ AI capabilities for purposes of national security are growing. This article reviews the ethical challenges presented by the use of AI for…
Anastasia Angelopoulou, Epaminondas Kapetanios, David Harris Smith, Volker Steuber + 2 more
'Volker Steuber' 'Bencie Woll' 'Frauke Zeller'] Autonomous vehicles, social and industrial robots, image-based medical diagnosis, voice-based knowledge and control systems (e.g., Alexa, Siri), and recommendation systems are some application domains, where AI/ML-assisted digital artifacts already support daily routines…
MD Abdullah Al Nasim, Parag Biswas, Abdur Rashid, Angona Biswas + 1 more
'Kishor Datta Gupta'] One of today's most significant and transformative technologies is the rapidly developing field of artificial intelligence (AI). Defined as a computer system that simulates human cognitive processes, AI is present in many aspects of our daily lives, from the self-driving cars on the road to the…
David Martens, Galit Shmueli, Theodoros Evgeniou, Kevin Bauer + 13 more
'Christian Janiesch' 'Stefan Feuerriegel' 'Sebastian Gabel' 'Sofie Goethals' 'Travis Greene' 'Nadja Klein' 'Mathias Kraus' 'Niklas Kühl' 'Claudia Perlich' 'Wouter Verbeke' 'Alona Zharova' 'Patrick Zschech' 'Foster Provost'] Understanding the decisions made and actions taken by increasingly complex AI system remains a…
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…
Mara Graziani, Lidia Dutkiewicz, Davide Calvaresi, José Pereira Amorim + 12 more
'José Pereira Amorim' 'Katerina Yordanova' 'Mor Vered' 'Rahul Nair' 'Pedro Henriques Abreu' 'Tobias Blanke' 'Valeria Pulignano' 'John O. Prior' 'Lode Lauwaert' 'Wessel Reijers' 'Adrien Depeursinge' 'Vincent Andrearczyk' 'Henning Müller'] Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to…
Sahil Sharma, Muskaan Singh, Liam McDaid, Saugat Bhattacharyya
Explainable Artificial Intelligence (XAI) is crucial in healthcare as it helps make intricate machine learning models understandable and clear, especially when working with diverse medical data, enhancing trust, improving diagnostic accuracy, and facilitating better patient outcomes. This paper thoroughly examines the…
Peter B. R. Hartog, Fabian Krüger, Samuel Genheden, Igor V. Tetko
Stakeholders of machine learning models desire explainable artificial intelligence (XAI) to produce human-understandable and consistent interpretations. In computational toxicity, augmentation of text-based molecular representations has been used successfully for transfer learning on downstream tasks. Augmentations of…
Bryant Han, Qingling Duan, Ting Hu
Machine learning models in biomedicine have become increasingly complex, often functioning as black boxes. However, understanding contributors to disease and making actionable health interventions requires interpretable models. Common explainable AI methods like SHAP focus on feature importance but fall short in…
Christina Humer, Henry Heberle, Floriane Montanari, Thomas Wolf + 4 more
The introduction of machine learning to small molecule research – an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate – has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or…
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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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Accurate prediction of melting points for pure molecules remains a significant challenge in predictive chemistry, with implications across various scientific fields, including materials science, drug discovery, and separations chemistry. Traditional methods, such as group contribution (GC) techniques, have shown…
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Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
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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…
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Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…