26 papers · ranked by Valyu relevance
Fatma Hilal Yagin, Radwa El Shawi, Abdulmohsen Algarni, Cemil Colak + 3 more
'Fahaid Al-Hashem' 'Luca Paolo Ardigò' 'Gemma Piella'] Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis…
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…
Chi Han, Mingyu Jin, Fuying Dong, Pengchong Xu + 6 more
Learning for Evaluating Nanogenerators’ Structural Design Authors: ['Chi Han' 'Mingyu Jin' 'Fuying Dong' 'Pengchong Xu' 'Xinnian Jiang' 'Sheling T. Cai' 'Yuanwen Jiang' 'Yongfeng Zhang' 'Yin Fang' 'Simiao Niu'] The limited battery life in modern mobile, wearable, and implantable electronics critically constrains their…
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…
Gabriel Laberge, Yann Pequignot
Shapley values are ubiquitous in interpretable Machine Learning due to their strong theoretical background and efficient implementation in the SHAP library. Computing these values previously induced an exponential cost with respect to the number of input features of an opaque model. Now, with efficient implementations…
Rory Mitchell, Eibe Frank, Geoffrey Holmes
SHAP (SHapley Additive exPlanation) values (Lundberg and Lee, 2017) provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values (Shapley, 1953). While exact calculation of SHAP values is computationally intractable in general, a recursive polynomialtime algorithm called…
Jilei Yang
SHAP (SHapley Additive exPlanation) values are one of the leading tools for interpreting machine learning models, with strong theoretical guarantees (consistency, local accuracy) and a wide availability of implementations and use cases. Even though computing SHAP values takes exponential time in general, TreeSHAP takes…
Iqbal Madakkatel, Elina Hyppönen
Background Shapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley…
Akshat Dubey, Aleksandar Anžel, Georges Hattab
The field of health informatics has been profoundly influenced by the development of random forest models, which have led to significant advances in the interpretability of feature interactions. These models are characterized by their robustness to overfitting and parallelization, making them particularly useful in…
Peng Yu, Chao Xu, Albert Bifet, Jesse Read
Decision trees are well-known due to their ease of interpretability. To improve accuracy, we need to grow deep trees or ensembles of trees. These are hard to interpret, offsetting their original benefits. Shapley values have recently become a popular way to explain the predictions of tree-based machine learning models.…
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…
Zhikang Liu, Yiyang Niu, Tian Le, Daniel G Chen + 2 more
The rapid maturation of single-cell multi-omics technologies has enabled unprecedented resolution for mapping disease states and identifying disease-associated biomarkers. In practice, biomarkers are often discovered through differential detection that treat genomic features as independent contributors to phenotypes…
Guangyi Zhang, Aristides Gionis
Decision trees are popular classification models, providing high accuracy and intuitive explanations. However, as the tree size grows the model interpretability deteriorates. Traditional tree-induction algorithms, such as C4.5 and CART, rely on impurity-reduction functions that promote the discriminative power of each…
Björn-Hergen Laabs von Holt, Ana Westenberger, Inke R. König
In life sciences random forests are often used to train predictive models. However, gaining any explanatory insight into the mechanics leading to a specific outcome is rather complex, which impedes the implementation of random forests into clinical practice. By simplifying a complex ensemble of decision trees to a…
Jonathan Warrell, Hussein Mohsen, Mark Gerstein
Deep learning methods have achieved state-of-the-art performance in many domains of artificial intelligence, but are typically hard to interpret. Network interpretation is important for multiple reasons, including knowledge discovery, hypothesis generation, fairness and establishing trust. Model transformations provide…
Sabino Francesco Roselli, Eibe Frank
Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems. In contrast to “classic” decision trees with constant values in their leaves, model trees can use linear combinations of predictor variables in their leaf nodes to form predictions, which can…
Sungbum Jun, Vassilis Plagianakos
Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need…
Mehrshad Sadria, Anita Layton, Gary D. Bader
For predictive computational models to be considered reliable in crucial areas such as biology and medicine, it is essential for them to be accurate, robust, and interpretable. A sufficiently robust model should not have its output affected significantly by a slight change in the input. Also, these models should be…
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…
Alexander Smith, Spencer Runde, Alex Chew, Atharva Kelkar + 3 more
Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex datasets that capture the 3D spatial positions, dynamics, and interactions of thousands…
Friedrich Hastedt, Rowan M. Bailey, Klaus Hellgardt, Sophia N. Yaliraki + 2 more
Machine learning models for chemical retrosynthesis have attracted substantial interest in recent years. Unaddressed challenges, particularly the absence of robust evaluation metrics for performance comparison, and the lack of black-box interpretability, obscure model limitations and impede progress in the field. We…
Christina Humer, Henry Heberle, Floriane Montanari, Thomas Wolf + 4 more
The introduction of machine learning to small molecule research – an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate – has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or…
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).…
Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang + 1 more
Discovering meaningful molecules in the vast combinatorial chemical space has been a longstanding challenge in many fields from materials science to drug discovery. Recent advances in machine learning, especially generative models, have made remarkable progress and demonstrate considerable promise for automated…
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…
Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang + 1 more
Discovering new structures in the chemical space is a long-standing challenge and has important applications to various fields such as chemistry, material science, and drug discovery. Deep generative models have been used in de novo molecule design to embed molecules in a meaningful latent space and then sample new…