23 papers · ranked by Valyu relevance
Raquel Rodríguez-Pérez, Jürgen Bajorath
Difficulties in interpreting machine learning (ML) models and their predictions limit the practical applicability of and confidence in ML in pharmaceutical research. There is a need for agnostic approaches aiding in the interpretation of ML models regardless of their complexity that is also applicable to deep neural…
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…
Rory Mitchell, Eibe Frank, Geoffrey Holmes, Alberto Cano
SHapley Additive exPlanation (SHAP) values ([24]) provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values ([35]). While exact calculation of SHAP values is computationally intractable in general, a recursive polynomial-time algorithm called TreeShap ([23]) is…
Saeid Saberi, Hamid Nasiri, Omid Ghorbani, Michael I. Friswell + 3 more
'Saullo G. P. Castro' 'Enrique Casarejos' 'Fernando Gomes de Souza Junior'] Material properties, geometrical dimensions, and environmental conditions can greatly influence the characteristics of bistable composite laminates. In the current work, to understand how each input feature contributes to the curvatures of the…
M. Mayer
An important technique to explore a black-box machine learning (ML) model is called SHAP (SHapley Additive exPlanation). SHAP values decompose predictions into contributions of the features in a fair way. We will show that for a boosted trees model with some or all features being additively modeled, the SHAP dependence…
Jin‐Young Lee, Kyungjin Kim, Jung In Seo
statistical validity Authors: ['Jin‐Young Lee' 'Kyungjin Kim' 'Jung In Seo'] Recently, SHapley Additive exPlanations (SHAP) has been widely utilized in various research domains. This is particularly evident in medical applications, where SHAP analysis serves as a crucial tool for identifying biomarkers and assisting in…
Reda Marzouk, Colin de la Higuera
Thanks to its solid theoretical foundation, the SHAP framework is arguably one the most widely utilized frameworks for local explainability of ML models. Despite its popularity, its exact computation is known to be very challenging, proven to be NP-Hard in various configurations. Recent works have unveiled positive…
Lev V. Utkin, Andrei V. Konstantinov
Ensemble-based modifications of the well-known SHapley Additive exPlanations (SHAP) method for the local explanation of a black-box model are proposed. The modifications aim to simplify SHAP which is computationally expensive when there is a large number of features. The main idea behind the proposed modifications is…
Tomohiro Ishibashi, Akio Onogi
Mapping quantitative trait loci (QTLs) is one of the major goals of quantitative genetics; however, identifying the interactions between QTLs (i.e., epistasis) remains challenging. Recently developed machine learning methods, such as deep learning and gradient boosting, are transforming the real world. These methods…
Linwei Hu, Ke Wang
SHAP (SHapley Additive exPlanations) has become a popular method to attribute the prediction of a machine learning model on an input to its features. One main challenge of SHAP is the computation time. An exact computation of Shapley values requires exponential time complexity. Therefore, many approximation methods are…
Quinn Dickinson, Jesse G. Meyer
Machine learning with multi-layered artificial neural networks, also known as “deep learning,” is effective for making biological predictions. However, model interpretation is challenging, especially for recurrent neural network architectures due to sequential input data. Here, we introduce a framework called…
Ecaterina Vasluian, Raoul M Bongers, Heleen A Reinders-Messelink, Pieter U Dijkstra + 1 more
'Pieter U Dijkstra' 'Corry K van der Sluis'] Background The Southampton Hand Assessment Procedure (SHAP) is currently used in the adult population for evaluating the functionality of impaired or prosthetic hands. The SHAP cannot be used for children because of the relatively larger size of the objects used to perform…
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…
Tomohiro Ishibashi, Akio Onogi
Mapping quantitative trait loci (QTLs) is one of the major goals of quantitative genetics; however, identifying the interactions between QTLs remains challenging. Recently developed machine learning methods, such as deep learning and gradient boosting, are transforming the real world. These methods could advance QTL…
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…
Kashyap Chhatbar, Adrian Bird, Guido Sanguinetti
Transcriptional regulation involves complex interactions involving chromatin–associated proteins, but disentangling these mechanistically remains challenging. Here, we generate deep learning models to predict RNA Pol–II occupancy from chromatin–associated protein profiles in unperturbed conditions. We evaluate the…
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…
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…
Authors not listed
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…
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)…
Chen Qian, Xingjian Dong, Kui Hu, Kangkang Chen + 2 more
Interpretability of Intelligent Fault Diagnosis Authors: ['Chen Qian' 'Xingjian Dong' 'Kui Hu' 'Kangkang Chen' 'Zhike Peng' 'Guang Meng'] Neural networks (NNs), with their powerful nonlinear mapping and end-to-end capabilities, are widely applied in mechanical intelligent fault diagnosis (IFD). However, as typical…
Rahul Upadhya, Matthew Tamasi, Elena Di Mare, Sanjeeva Murthy + 1 more
The functional structure of proteins is heavily influenced by their folding behavior. AlphaFold, a powerful artificial intelligence (AI) program trained on information from the Protein Data Bank (PDB), was developed to predict the 3D structure of proteins from its amino acid sequence. Inspired by this, we aim to…