24 papers · ranked by Valyu relevance
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…
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…
Justin Lin, Julia Fukuyama
In this growing age of data and technology, large black-box models are becoming the norm due to their ability to handle vast amounts of data and learn incredibly complex data patterns. The deficiency of these methods, however, is their inability to explain the prediction process, making them untrustworthy and their use…
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…
Hyeon-Seok Kim, Do-Hyeon Kim, Sun-Yong Choi, Feier Chen
The Baltic Dry Index (BDI) is a critical benchmark for assessing freight rates and chartering activity in the global shipping market. This study forecasts the BDI using diverse financial data, including commodities, currencies, stock markets, and volatility indices. Unlike previous research, our approach integrates…
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…
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.…
Honglin Song, Yutao Li, Xiaofeng Zou, Ping Hu + 1 more
This study adopts a new approach, SHapley Additive exPlanation (SHAP), to diagnose the table tennis matches based on a hybrid algorithm, namely Long Short-Term Memory-Back Propagation Neural Network (LSTM-BPNN). 100 male singles competitions (8535 rallies) from 2019 to 2022 are analyzed by a hybrid technical-tactical…
Aurod Ounsinegad, Christopher Mitchell, Nicholas Komar
Eastern equine encephalitis virus (EEEV) is a deadly arboviral pathogen with 30% severe case fatality. EEEV exhibits pronounced 2–3 year cyclical outbreak patterns in the northeastern United States, linked to shifts in mosquito feeding preferences between hatch-year and adult avians. We developed an age-structured…
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…
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…
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)…
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…
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…
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…
Andrés Cremades, Sergio Hoyas, Ricardo Vinuesa
For the last 140 years, the mechanisms of transport and dissipation of energy in a turbulent flow have not been completely understood. Previous research has focused on analyzing the so-called coherent structures, organized flow patterns characterized by their spatial coherence, lifespan and significant contribution to…
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…
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…
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…