Search · four archives
Search · four archives
30 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…
Mattia Billa, Giovanni Orlandi, Veronica Guidetti, Federica Mandreoli
In recent years, machine learning models have seen widespread adoption across a broad range of sectors, including high-stakes domains such as healthcare, finance, and law. This growing reliance has raised increasing concerns regarding model interpretability and accountability, particularly as legal and regulatory…
Riccardo Guidotti, Martina Cinquini, Marta Marchiori Manerba, Mattia Setzu + 1 more
Interpretable-by-design models are crucial for fostering trust, accountability, and safe adoption of automated decision-making models in real-world applications. In this paper we formalize the ground for the MIMOSA (Mining Interpretable Models explOiting Sophisticated Algorithms) framework, a comprehensive methodology…
Julia Kropiunig, Øystein Sørensen
Global interpretability in machine learning holds great potential for extracting meaningful insights from neuroimaging data to improve our understanding of brain function. Although various approaches exist to identify key contributing features at both local and global levels, the high dimensionality and correlations in…
A H M Osama Haque, Abdullah Al Fahad, M Sohel Rahman, Md Abul Hassan Samee
Alzheimer’s Disease remains a major public health challenge, requiring insights into feature interactions and temporal trends of feature importance. Community-wide data science competitions such as the TADPOLE Challenge provide platforms to benchmark predictive models using ADNI datasets. While top-performing models…
Boris Babic, I. Glenn Cohen, Julian Savulescu
As artificial intelligence and machine learning (AI/ML) systems become increasingly pervasive in society, their opacity-i.e., the difficulty, and sometimes impossibility, of understanding why they make the decisions they make-has become a serious problem. This is especially true in sensitive decision-making contexts…
Simon Mählkvist, Pontus Netzell, Thomas Helander, Konstantinos Kyprianidis + 1 more
In industrial machine learning, predictive performance alone is insufficient to ensure reliable deployment, as model behaviour may vary across different regions of the input space under limited data and evolving process conditions. This work investigates whether such variation can be systematically analysed through…
Valeru Vision Paul, Jafar Ali Ibrahim Syed Masood
1.1### Artificial intelligence and the need for transparent predictive models These days, artificial intelligence is changing how researchers handle organized health and behavior information. Instead of relying only on traditional statistics, scientists now turn to machine learning to spot hidden trends in large…
David Zapata Gonzalez
The growing reliance on machine learning for decisions across sectors underscores the importance of model transparency and interpretability. Existing post hoc explainability methods and inherently interpretable approaches shed light on model behavior, yet they primarily reveal how models exploit correlations to…
M.A. Lieftinck, T. Verlaan, M.J.T. Reinders
Deep Neural Networks (DNNs) are renowned for their high accuracy and versatility, which has led to their application in many fields of research, including biology. However, this accuracy often comes at the expense of interpretability, making it challenging to reason about the inner workings of most DNNs. Particularly…
Bruno R. Florentino, Robson P. Bonidia, Ulisses Rocha, André C. P. L. F. de Carvalho
Proteins are essential in biological processes, primarily through their interactions with other molecules, including proteins. These interactions are crucial for cellular functions and maintaining life. Predicting Protein-Protein Interactions (PPIs) is very important, although challenging, for understanding cellular…
Nehleh Kargarfard, Robert Dunne, Carol Lee, Laurence Wilson + 2 more
Machine learning (ML) has become a powerful tool in biological and clinical research, supporting tasks such as vaccine response prediction, biomarker discovery, and integration of heterogeneous omics data (, , ). This was emphasized by the recent SARS-CoV-2 pandemic, where ML was deployed to answer questions including…
Ahmed Al Marouf, Jon George Rokne, Reda Alhajj, Magdalena Görtz + 1 more
Title: Simple Summary Prostate cancer is one of the most common and deadly cancers in men. Accurate diagnosis and determining disease severity are essential for personalized treatment and better outcomes. This study introduces an Explainable Machine Learning (XML) approach to identify biomarkers linked to different…
Mahbuba Tasmin, Saishradha Mohanty, Sanjana Kulkarni, Maha R. Farhat + 1 more
Foundation models aim to learn useful representations of biological sequences. However, the applicability of these representations for a wide range of tasks, including phenotype prediction and variant discovery, is still in question, in large part due to the relatively small set of benchmark tasks. To this end, we…
Frederico Guilherme Santana Da Silva Filho, Igor Wenner Silva Falcão, Tobias Moraes de Souza, Saul Rassy Carneiro + 3 more
Background/Objectives: Treatment adherence challenges affect 10-20% of tuberculosis patients globally, contributing to drug resistance and continued transmission. While artificial intelligence approaches show promise for identifying patients who may benefit from additional treatment support, most models lack the…
Zidong Yan, Jiaqi Li, Weican Zhang, Haonan Wen + 4 more
on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research Authors: Zidong Yan, Jiaqi Li, Weican Zhang, Haonan Wen, Hao Yu, Miao Yu, Qian Liu, Guibin Jiang Machine learning (ML) has become a powerful paradigm for extracting structures from complex environmental data and supporting scientific…
Srikumar Krishnamoorthy
Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The marginal feature-screening step common to these methods can discard variables whose predictive value emerges only through joint configurations with other variables. We present…
Authors not listed
High-entropy layered double hydroxides (HE-LDHs) have shown great potential in oxygen evolution reaction (OER) catalysis due to their tunable compositions and electronic structures. However, the synergistic effects between multiple vacancies, such as metal and oxygen vacancies, remain poorly understood and challenging…
Ziba Jabbar Zare, Ulrich Aïvodji, Julien Ferry, Thibaut Vidal
Hybrid interpretable models combine a transparent component with a black-box model by assigning some examples to the former and deferring the rest to the latter. While this design enables flexible tradeoffs between accuracy and interpretability, it also raises a distinct procedural fairness concern: some demographic…
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…
Dennis Gankin, Pedro Beltrao
Biologically inspired neural networks (BINNs) embed pathway, ontology, or protein-interaction structure directly into neural networks, promising interpretable disease prediction where hidden nodes map to named biological entities. Yet BINNs have been hard to train at biobank scale, and the reliability of their…
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…
Matvei Fedin, Andrei Morozov
Machine learning nowadays becomes a useful instrument in many subjects. In this paper we use interpretable machine learning to build quantum algorithm. By studying the parameters of the machine learning algorithm we were able to construct universal shortest analytic quantum algorithm for arbitrary diagonal matrix of…
Authors not listed
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…
Marcelo Hurtado, Vera Pancaldi
Machine learning approaches are increasingly applied to high-dimensional biological data in which features are often dataset-dependent. In many omics workflows, features are computed using information derived from the entire dataset, such as correlations between variables, clustering structures, or enrichment scores.…
Authors not listed
Machine learning (ML) models are increasingly used in quantum chemistry, but their reliability hinges on uncertainty quantification (UQ). In this study, we compare two prominent UQ paradigms—Deep Evidential Regression (DER) and Deep Ensembles—on the QM9 and WS22 datasets, with a specific emphasis on the role of post…
Authors not listed
The Suzuki-Miyaura cross-coupling reaction remains one of the most widely used C-C bond-forming transformations in synthetic chemistry. Machine learning models promise to accelerate reaction optimization, yet systematic benchmarking across different catalytic systems remains limited. Here, we develop and validate a…
Authors not listed
Pharmacophores are widely used to describe protein-ligand interactions, and the Grids of Pharmacophore Interaction Fields (GRAIL) method extends this concept by representing binding pockets as interpretable sets of interaction type-specific pharmacophoric maps. In this work, we propose a hybrid framework for binding…
Authors not listed
Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional…
Areen Arabiat, Hamza Abu Owida, Suhaila Abuowaida, Nawaf Alshdaifat + 2 more
This study emphasizes the potential of computational techniques in cancer risk assessment, highlighting opportunities for specific and data-driven healthcare solutions. It examines the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches to improve cancer risk assessment using a…