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23 papers · ranked by Valyu relevance

01
PPubMed1 Dec 2022
Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series
Liangyuan Hu, Lihua Li, Paul B. Tchounwou
Tree-based machine learning methods have gained traction in the statistical and data science fields. They have been shown to provide better solutions to various research questions than traditional analysis approaches. To encourage the uptake of tree-based methods in health research, we review the methodological…
82relevance
02
AarXiv2022
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization
Jungtaek Kim, Seungjin Choi
Sequential model-based optimization sequentially selects a candidate point by constructing a surrogate model with the history of evaluations, to solve a black-box optimization problem. Gaussian process (GP) regression is a popular choice as a surrogate model, because of its capability of calculating prediction…
81relevance
03
PPubMed26 Jul 2023
Decision trees: from efficient prediction to responsible AI
Hendrik Blockeel, Laurens Devos, Benoît Frénay, Géraldin Nanfack + 1 more
'Siegfried Nijssen'] This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, describes the broader context in which the research is situated, and summarizes strengths and…
81relevance
04
BbioRxiv10 Oct 2025
An individual, mechanistic and dynamical model to simulate urban tree growth and ecosystem services supply under future scenarios
Davide Stucchi, Javier Babí Almenar, Renato Casagrandi
Urban trees represent a key nature-based solution and an essential component of green infrastructures, providing multiple ecosystem services but increasingly subjected to environmental stressors. Here we present a dynamic, mechanistic, and individual-based model designed to simulate growth of urban trees and the…
80relevance
05
BbioRxiv21 Dec 2020
Decision tree models and cell fate choice
Ivan Croydon Veleslavov, Michael P.H. Stumpf
Single cell transcriptomics has laid bare the heterogeneity of apparently identical cells at the level of gene expression. For many cell-types we now know that there is variability in the abundance of many transcripts, and that average transcript abun-dance or average gene expression can be a unhelpful concept. A range…
79relevance
06
AarXiv2020
Hybrid Tree-based Models for Insurance Claims
Zhiyu Quan, Zhiguo Wang, Guojun Gan, Emiliano A. Valdez
Two-part models and Tweedie generalized linear models (GLMs) have been used to model loss costs for short-term insurance contract. For most portfolios of insurance claims, there is typically a large proportion of zero claims that leads to imbalances resulting in inferior prediction accuracy of these traditional…
79relevance
07
PPubMed31 Aug 2011
Bayesian Classification and Regression Trees for Predicting Incidence of Cryptosporidiosis Bayesian Classification and Regression Trees
Wenbiao Hu, Rebecca A. O'Leary, Kerrie Mengersen, Samantha Low Choy + 1 more
'Zheng Su'] Background Classification and regression tree (CART) models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART model is described and…
77relevance
08
AarXiv2017
Optimization of Tree Ensembles
Velibor V. Mišić
Tree ensemble models such as random forests and boosted trees are among the most widely used and practically successful predictive models in applied machine learning and business analytics. Although such models have been used to make predictions based on exogenous, uncontrollable independent variables, they are…
76relevance
09
PPubMed20 Sep 2017
Decision trees in epidemiological research
Ashwini Venkatasubramaniam, Julian Wolfson, Nathan Mitchell, Timothy Barnes + 2 more
'Timothy Barnes' 'Meghan JaKa' 'Simone French'] Background In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However…
76relevance
10
PPubMed23 Jun 2026
Experiments with optimal model trees
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…
76relevance
11
AarXiv2013
Interpolating Conditional Density Trees
Scott Davies, Andrew Moore
Joint distributions over many variables are frequently modeled by decomposing them into products of simpler, lower-dimensional conditional distributions, such as in sparsely connected Bayesian networks. However, automatically learning such models can be very computationally expensive when there are many datapoints and…
76relevance
12
AarXiv17 Mar 2025
Experiments with Optimal Model Trees
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…
76relevance
13
BbioRxiv15 May 2022
Identification of representative trees in random forests based on a new tree-based distance measure
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…
74relevance
14
CchemRxiv24 Oct 2025
XGBoost and Physical-Informed Neural Networks as Surrogate Models for Vapor-Liquid Equilibrium in PC-SAFT
Authors not listed
Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…
74relevance
15
PPubMed18 Jun 2021
Meta-Tree Random Forest: Probabilistic Data-Generative Model and Bayes Optimal Prediction
Nao Dobashi, Shota Saito, Yuta Nakahara, Toshiyasu Matsushima + 1 more
'Udo Von Toussaint'] This paper deals with a prediction problem of a new targeting variable corresponding to a new explanatory variable given a training dataset. To predict the targeting variable, we consider a model tree, which is used to represent a conditional probabilistic structure of a targeting variable given an…
73relevance
16
CchemRxiv31 Oct 2023
Laryngeal Cancer Diagnosis via miRNA-based Decision Tree Model
Aarav Arora, Igor Tsigelny, Valentina Kouznetsova
Purpose Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the expensiveness and inaccessible nature of current diagnosis methods. Many recent studies have shown that microRNAs (miRNAs) are crucial biomarkers for a variety of cancers. Methods In this study, we create a…
71relevance
17
AarXiv2020
The R Package stagedtrees for Structural Learning of Stratified Staged Trees
Federico Carli, Manuele Leonelli, Eva Riccomagno, Gherardo Varando
stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data. Score-based and clustering-based algorithms are implemented, as well as various functionalities to plot the models and perform inference. The capabilities of stagedtrees are…
70relevance
18
BbioRxiv5 May 2022
Gaining insight into the allometric scaling of trees by utilizing 3d reconstructed tree models - a SimpleForest study
Jan Hackenberg, Mathias Disney, Jean-Daniel Bontemps
Forestry utilizes volume predictor functions utilizing as input the diameter at breast height. Some of those functions take the power form Y = a ∗ X^b^. In fact this function is fundamental for the biology field of allometric scaling theories founded round about a century ago. The theory describes the relationships…
69relevance
19
CchemRxiv1 Jun 2022
Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning
Prashanth Athri, Vidhya Murali, Pradyumna Y Muralidhar, Cassandra Königs + 4 more
- 1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India - 2. PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India - 3. Bioinformatics and Medical Informatics, Bielefeld University…
68relevance
20
BbioRxiv29 Aug 2018
TreeBASEdmp: A Toolkit for Phyloinformatic Research
William H. Piel, Rutger A. Vos
Over the last 20 years, TreeBASE has acquired a substantial body of phylogenetic data, including more than 20,000 published phylogenies. Given latency issues and limited options when it comes to querying the database remotely, a simplified and consolidated version of the database, here called TreeBASEdmp, is made…
67relevance
21
BbioRxiv3 Dec 2024
Informative priors contribute to quantifying the occurrence rate of a rare tree-related microhabitat in a managed forest
Pierre Cottais, Benoît Courbaud, Laurent Larrieu, Nicolas Gouix + 1 more
Tree-related microhabitats (TreMs) are key features for forest biodiversity, and knowing their accumulation rate is essential to design integrative management strategies. Many types of TreMs are associated to large old trees and show slow ontogenical processes. The rarity of such TreMs (particularly in intensively…
65relevance
22
CchemRxiv2 Mar 2022
Scaffold Generator - A Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)
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…
59relevance
23
CchemRxiv27 Jun 2023
MoFlowGAN: Combining adversarial and likelihood learning for targeted molecular generation
Nathan Mancheun Lui, Max D Li, Matthew Ford
Deep generative models for molecular graphs offer a new avenue for property optimization in drug discovery. Optimizing differentiable models that generate molecular graphs is certainly faster, cheaper, and much more accessible than traditional methods of chemical synthesis. Recent advances in generative modeling have…
51relevance

Results for tree‑based models

Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series

On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization

Decision trees: from efficient prediction to responsible AI

An individual, mechanistic and dynamical model to simulate urban tree growth and ecosystem services supply under future scenarios

Decision tree models and cell fate choice

Hybrid Tree-based Models for Insurance Claims

Bayesian Classification and Regression Trees for Predicting Incidence of Cryptosporidiosis Bayesian Classification and Regression Trees

Optimization of Tree Ensembles

Decision trees in epidemiological research

Experiments with optimal model trees

Interpolating Conditional Density Trees

Experiments with Optimal Model Trees

Identification of representative trees in random forests based on a new tree-based distance measure

XGBoost and Physical-Informed Neural Networks as Surrogate Models for Vapor-Liquid Equilibrium in PC-SAFT

Meta-Tree Random Forest: Probabilistic Data-Generative Model and Bayes Optimal Prediction

Laryngeal Cancer Diagnosis via miRNA-based Decision Tree Model

The R Package stagedtrees for Structural Learning of Stratified Staged Trees

Gaining insight into the allometric scaling of trees by utilizing 3d reconstructed tree models - a SimpleForest study

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

TreeBASEdmp: A Toolkit for Phyloinformatic Research

Informative priors contribute to quantifying the occurrence rate of a rare tree-related microhabitat in a managed forest

Scaffold Generator - A Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)

MoFlowGAN: Combining adversarial and likelihood learning for targeted molecular generation

Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series

On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization

Decision trees: from efficient prediction to responsible AI

An individual, mechanistic and dynamical model to simulate urban tree growth and ecosystem services supply under future scenarios

Decision tree models and cell fate choice

Hybrid Tree-based Models for Insurance Claims

Bayesian Classification and Regression Trees for Predicting Incidence of Cryptosporidiosis Bayesian Classification and Regression Trees

Optimization of Tree Ensembles

Decision trees in epidemiological research

Experiments with optimal model trees

Interpolating Conditional Density Trees

Experiments with Optimal Model Trees

Identification of representative trees in random forests based on a new tree-based distance measure

XGBoost and Physical-Informed Neural Networks as Surrogate Models for Vapor-Liquid Equilibrium in PC-SAFT

Meta-Tree Random Forest: Probabilistic Data-Generative Model and Bayes Optimal Prediction

Laryngeal Cancer Diagnosis via miRNA-based Decision Tree Model

The R Package stagedtrees for Structural Learning of Stratified Staged Trees

Gaining insight into the allometric scaling of trees by utilizing 3d reconstructed tree models - a SimpleForest study

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

TreeBASEdmp: A Toolkit for Phyloinformatic Research

Informative priors contribute to quantifying the occurrence rate of a rare tree-related microhabitat in a managed forest

Scaffold Generator - A Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)

MoFlowGAN: Combining adversarial and likelihood learning for targeted molecular generation