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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…
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…
Pol Borrellas, Irene Unceta, Sotiris Kotsiantis
The deployment of machine learning models is expected to bring several benefits. Nevertheless, as a result of the complexity of the ecosystem in which models are generally trained and deployed, this technology also raises concerns regarding its (1) interpretability, (2) fairness, (3) safety, and (4) privacy. These…
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…
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…
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…
Vaishak Belle
Artificial Intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in computational biology, finance, law and robotics. However, such a…
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…
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…
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…
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…
Raghad Zenki, Mu Mu
Machine learning (ML) has shown increasing abilities for predictive analytics over the last decades. It is becoming ubiquitous in different fields, such as healthcare, criminal justice, finance and smart city. For instance, the University of Northampton is building a smart system with multiple layers of IoT and…
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…
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…
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…
Zachary C. Lipton
"I work with medical data. We work with doctors and they're interested in predicting risk of mortality, recognizing cancer in radiologic scans, and spotting diagnoses based on electronic health record data. We can train a model, and it can even give us the right answer. But we can't just tell the doctor "my neural…
Chang Ho Yoon, Robert Torrance, Naomi Scheinerman
We argue why interpretability should have primacy alongside empiricism for several reasons: first, if machine learning (ML) models are beginning to render some of the high-risk healthcare decisions instead of clinicians, these models pose a novel medicolegal and ethical frontier that is incompletely addressed by…
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…
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).…
Laura Lema-Perez, Rafael Muñoz-Tamayo, Jose Garcia-Tirado, Hernan Alvarez
Empirical and phenomenological based models are used to represent biological and physiological processes. Phenomenological models are derived from the knowledge of the mechanisms that underlie the behaviour of the system under study, while empirical models are derived from analysis of data to quantify relationships…
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…
Joshua J. Levy, A. James O’Malley
Machine learning approaches have become increasingly popular modeling techniques, relying on data-driven heuristics to arrive at its solutions. Recent comparisons between these algorithms and traditional statistical modeling techniques have largely ignored the superiority gained by the former approaches due to…
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…