27 papers · ranked by Valyu relevance
Maryam Hajjar, Ghadah Aldabbagh, Somayah Albaradei, Kai-Chih Pai
Background: Tumor-educated platelets (TEPs) represent a promising biosource for non-invasive multi-cancer early detection (MCED). While machine learning (ML) has been applied to TEP data, the integration of explainability to reveal gene-level contributions and regulatory associations remains underutilized. This study…
Xianlong Zeng
Models Authors: ['Xianlong Zeng'] Model interpretability is crucial for understanding and trusting the decisions made by complex machine learning models, such as those built with XGBoost. SHAP (SHapley Additive exPlanations) values have become a popular tool for interpreting these models by attributing the output to…
Xiao-li Deng, Chongze Yang, Lan-hui Qin, Xue-feng Lin + 2 more
Rationale and objectives Early recurrence after curative resection remains a major determinant of poor prognosis in intrahepatic cholangiocarcinoma (ICC). Existing multimodal prediction models often lack interpretability due to feature interference during fusion. This study aimed to develop and externally validate an…
Viswan Vimbi, Noushath Shaffi, Mufti Mahmud
Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning (DL) models. The Local Interpretable Model-agnostic Explanations (LIME) and Shaply Additive exPlanation (SHAP) frameworks have…
Emanuell Rodrigues de Souza, Higor Almeida Cordeiro Nogueira, Victor dos Santos Lopes, Enrique Medina-Acosta
Regulated cell death (RCD) pathways profoundly influence tumor progression and immune modulation. In prior work, we constructed a comprehensive database mapping 25 forms of RCD across seven multi-omic layers encompassing 33 tumor types (CancerRCDShiny). Despite their robust ability to identify risk populations…
Akshat Dubey, Aleksandar Anžel, Bahar İlgen, Georges Hattab
Explainable Artificial Intelligence (XAI) techniques, such as SHapley Additive exPlanations (SHAP), have become essential tools for interpreting complex ensemble tree-based models, especially in highstakes domains such as healthcare analytics. However, SHAP values are usually treated as point estimates, which…
Robin Khalfa, Naomi Theinert, Wim Hardyns
This study empirically compares multiple eXplainable Artificial Intelligence (XAI) techniques to interpret short-term (weekly) machine learning-based burglary predictions at the micro-place level in Ghent, Belgium. While previous research predominantly relies on SHAP to interpret spatiotemporal crime predictions, this…
Authors not listed
Background: Batch reactor process optimization has traditionally relied on Analysis of Variance (ANOVA) for factor effect quantification. However, Structural Equation Modeling (SEM) and machine learning (ML) offer complementary mechanistic and predictive capabilities that remain underexplored in chemical engineering…
Guilherme Dean Pelegrina, Leonardo Tomazeli Duarte, Michel Grabisch
Besides accuracy, recent studies on machine learning models have been addressing the question on how the obtained results can be interpreted. Indeed, while complex machine learning models are able to provide very good results in terms of accuracy even in challenging applications, it is difficult to interpret them.…
Md Tanzim Rafat
The refinement of scaffold materials is essential in tissue engineering to promote cellular growth and tissue regeneration. This study applied Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to predict and interpret the biocompatibility of poly(lactic-co-glycolic acid) (PLGA)-based…
Ramtin Zargari Marandi, Yoshihiro Yamanishi
Machine learning, especially its application in processing clinical datasets, has gained immense popularity in recent years. As the introduction of new complex algorithms allows the machine learning field to grow, it also comes along with the challenge of unravelling how these algorithms produce an output. For example…
Hayato Yoshioka, Pavla Debeljak, Soizic Prado, Hiroyoshi Iwata
Plant-microbe interactions in the rhizosphere are central to plant growth, nutrient acquisition, and stress resilience. Multi-omics approaches enable comprehensive profiling of different biological layers, yet integrating these data to understand the mechanisms underlying plant-microbe symbiosis, particularly under…
Kyungtae Lee, Mukil V. Ayyasamy, Yangfeng Ji, Prasanna V. Balachandran
'Prasanna V. Balachandran'] We demonstrate the capabilities of two model-agnostic local post-hoc model interpretability methods, namely breakDown (BD) and shapley (SHAP), to explain the predictions of a black-box classification learning model that establishes a quantitative relationship between chemical composition and…
Hansani Weeratunge, Dominic Robe, Elnaz Hajizadeh
We developed an interpretability informed Bayesian optimization framework to optimize underwater acoustic coatings based on polyurethane elastomers with embedded metamaterial features. A data driven model was employed to analyze the relationship between acoustic performance, specifically sound absorption and the…
Luisa Bouneder, Yannick Léo, Aimé Lachapelle
This paper introduces X-SHAP, a model-agnostic method that assesses multiplicative contributions of variables for both local and global predictions. This method theoretically and operationally extends the so-called additive SHAP approach. It proves useful underlying multiplicative interactions of factors, typically…
Ji Yoon Kim
Purpose This study aimed to leverage Shapley additive explanation (SHAP)-based feature engineering to predict appendix cancer. Traditional models often lack transparency, hindering clinical adoption. We propose a framework that integrates SHAP for feature selection, construction, and weighting to enhance accuracy and…
Manoj S. Kambara, Onyinye Chukka, Kathryn J. Choi, Joseph Tsenum + 4 more
Type 2 diabetes (T2D) is a disease with high morbidity and mortality and a disproportionate impact on minority groups. Machine learning (ML) is increasingly used to characterize T2D risk factors; however, it has not been used to study T2D health disparities. Our objective was to use explainable ML methods to discover…
Amal Saadallah
Feature attribution methods such as SHapley Additive exPlanations (SHAP) have become instrumental in understanding machine learning models, but their role in guiding model optimization remains underexplored. In this paper, we propose a SHAP-guided regularization framework that incorporates feature importance…
Paul Czodrowski, Aishvarya Tandon, Anna Santura, Axel Pahl + 1 more
Lysosomotropism is a phenomenon of diverse pharmaceutical interests because it is a property of compounds with diverse chemical structures and primary targets. While it is primarily reported to be caused by compounds having suitable lipophilicity and basicity values, not all compounds that fulfill such criteria are in…
Leonoor E.M. Tideman, Lukasz G. Migas, Katerina V. Djambazova, Nathan Heath Patterson + 3 more
The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets.…
Jirui Jin, Somayeh Faraji, Bin Liu, Mingjie Liu
Perovskite materials, renowned for their versatility and remarkable properties, pose challenges in discovering optimal candidates due to the vast compositional space. Data-driven machine learning (ML) offers promise in expediting material discovery; however, the trade-off between accuracy and efficiency across…
Hayato Yoshioka, Pavla Debeljak, Soizic Prado, Yushiro Fuji + 2 more
Plant-microbe interactions in the rhizosphere are central to plant growth, nutrient acquisition, and stress resilience. Although multi-omics approaches enable comprehensive profiling of different biological layers, integrating these data to understand the mechanisms underlying plant-microbe symbiosis, particularly…
Jason Yang, Lei Tao, Jinlong He, Jeffrey McCutcheon + 1 more
Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research on membrane technologies, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine-learning (ML)…
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
Chalcogenide Hybrid Inorganic/Organic Polymers (CHIPs) have the potential to revolutionize infrared (IR) optics and create sustainable and recyclable devices. CHIPs combine elemental sulfur with organic comonomers via inverse vulcanization to create a high-sulfur content polymer, with optical properties that rival…
Authors not listed
Highly fluorinated aromatic compounds exhibit unique electronic structures, however their selective transformation remains a longstanding challenge. Halogenation of F7 naphthalene previously required low temperatures (–40 to 0 °C) for high yields, while room-temperature reactions suffered from side reactions and…
Christina Humer, Rachel Nicholls, Henry Heberle, Moritz Heckmann + 7 more
Chemical reaction optimization (RO) is an iterative process that results in large and high-dimensional datasets. Current tools only allow for limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using…