22 papers · ranked by Valyu relevance
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…
Thi-Thu-Huong Le, Haeyoung Kim, Hyoeun Kang, Howon Kim + 2 more
'Zhongyun Hua' 'Yushu Zhang'] In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples applied widely in IDS. However, DNN…
G. Peggy McFall, Linzy Bohn, Myrlene Gee, Shannon M. Drouin + 5 more
'Harrison Fah' 'Wei Han' 'Liang Li' 'Richard Camicioli' 'Roger A. Dixon'] Background Persons with Parkinson’s disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that…
Olatomiwa O. Bifarin, Imran Ashraf
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…
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…
Ron Wettenstein, Alexander Nadel, Udi Boker
Decision-tree ensembles are a cornerstone of predictive modeling, and SHAP is a standard framework for interpreting their predictions. Among its variants, Background SHAP offers high accuracy by modeling missing features using a background dataset. Historically, this approach did not scale well, as the time complexity…
Luís Vinícius de Moura, Christian Mattjie, Caroline Machado Dartora, Rodrigo C. Barros + 1 more
'Rodrigo C. Barros' 'Ana Maria Marques da Silva'] Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and…
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…
Alexander Nadel, Ron Wettenstein
SHapley Additive exPlanations (SHAP) is a key tool for interpreting decision tree ensembles by assigning contribution values to features. It is widely used in finance, advertising, medicine, and other domains. Two main approaches to SHAP calculation exist: Path-Dependent SHAP, which leverages the tree structure for…
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…
Spencer Matthews, Brian Hartman
Two-part models are important to and used throughout insurance and actuarial science. Since insurance is required for registering a car, obtaining a mortgage, and participating in certain businesses, it is especially important that the models which price insurance policies are fair and non-discriminatory. Black box…
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…
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…
Masrur Sobhan, Ananda Mohan Mondal
Lung cancer is the leading cause of cancer compared to other cancers in the USA despite being the most commonly diagnosed. The overall survival rate of lung cancer is not satisfactory even though having cutting edge treatment methods for cancers. Genomic profiling and biomarker gene identification of lung cancer…
Reda Marzouk, Shahaf Bassan, Guy Katz, Colin de la Higuera
Recent studies have examined the computational complexity of computing Shapley additive explanations (also known as SHAP) across various models and distributions, revealing their tractability or intractability in different settings. However, these studies primarily focused on a specific variant called Conditional SHAP…
Jingyi Gao, Mitchell G Newberry, Guruswami Ravichandran
Leonardo da Vinci left guidelines for painting trees that have inspired landscape painters and tree physiologists alike, yet his prescriptions depend on a parameter, α, now known as the radius scaling exponent in self-similar branching. While da Vinci seems to imply $α=2$, contemporary vascular biology considers other…
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…
Marzieh Khodaei, Megan Owen, Peter Beerli
Phylogenetic trees are fundamental for understanding the evolutionary history of a set of species. The local neighborhoods of a phylogenetic tree provide important information, but since trees are high-dimensional objects, characterizing these neighborhoods is difficult. Based on the Billera-Holmes-Vogtmann (BHV)…
Lena Collienne, Chris Whidden, Alex Gavryushkin
Phylogenetic trees are a mathematical formalisation of evolutionary histories between organisms, species, genes, cancer cells, etc. For many applications, e.g. when analysing virus transmission trees or cancer evolution, (phylogenetic) time trees are of interest, where branch lengths represent times. Computational…
Jonas Schaub, Julian Zander, Achim Zielesny, Christoph Steinbeck
The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for 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…
Authors not listed
We present a simple yet efficient random (brute-force) algorithm for constructing solvated molecular systems. By placing solvent molecules at random positions and orientations within a simulation box, we circumvent the complexities typically associated with more sophisticated packing algorithms. The main computational…