Search · four archives

Results for Boosting algorithm

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

12 papers · ranked by Valyu relevance

01
BbioRxiv9 Oct 2021
RUSBoost: A suitable species distribution method for imbalanced records of presence and absence. A case study of twenty-five species of Iberian bats
Jaime Carrasco, Fulgencio Lisón, Andrés Weintraub
Traditional Species Distribution Models (SDMs) may not be appropriate when examples of one class (e.g. absence or pseudo-absences) greatly outnumber examples of the other class (e.g. presences or observations), because they tend to favor the learning of observations more frequently. We present an ensemble method called…
82relevance
02
BbioRxiv1 May 2022
Boosting polygenic risk scores
Hannah Klinkhammer, Christian Staerk, Carlo Maj, Peter M. Krawitz + 1 more
Polygenic risk scores (PRS) evaluate the individual genetic liability to a certain trait and are expected to play an increasingly important role in the field of clinical risk stratification. Most often, PRS are estimated based on summary statistics of univariate effects derived from genome-wide association studies. To…
78relevance
03
BbioRxiv13 Sep 2022
Ensemble Machine Learning to “Boost” Ubiquitination-sites Prediction
Xiaoye Mo, Xia Jiang
Ubiquitination-site prediction is an important task because ubiquitination is a critical regulatory function for many biological processes such as proteasome degradation, DNA repair and transcription, signal transduction, endocytoses, and sorting. However, the highly dynamic and reversible nature of ubiquitination…
76relevance
04
BbioRxiv16 Jan 2024
Applying gradient tree boosting to QTL mapping with Shapley additive explanations
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…
76relevance
05
BbioRxiv1 Nov 2023
Extreme gradient boosting machine learning algorithm identifies genome-wide genetic variants in prostate cancer risk prediction
David Enoma, Victor Chukwudi Osamor, Ogunlana Olubanke
Genome-wide association studies (GWAS) identify the variants (Single Nucleotide polymorphisms) associated with a disease phenotype within populations. These genetic differences are essential in variations in incidence and mortalities, especially for Prostate cancer in the African population. Given the complexity of…
74relevance
06
BbioRxiv13 Jan 2025
A diagnostic support system based on interpretable machine learning and oscillometry for accurate diagnosis of respiratory dysfunction in silicosis
Jorge Luís Machado do Amaral, Cíntia Moraes de Sá Sousa, Caroline de Oliveira Ribeiro, Paula Morisco de Sá + 2 more
Silicosis, the most dangerous and common lung illness associated with breathing in mineral dust, is a significant health concern. Spirometry, the traditional method for evaluating pulmonary functions, requires high patient compliance. Respiratory Oscillometry and electrical models are being studied to evaluate the…
73relevance
07
BbioRxiv16 Jan 2023
Transfer learning in heterogeneous drug-target interaction predictions using federated boosting
Dániel Sándor, Péter Antal
In multitask federated learning, when small amounts of data are available, it can be harder to achieve proper predictive performance, especially if the clients’ tasks are different. However, task heterogeneity is common in modern Drug-Target interaction (DTI) prediction problems. As the data available for DTI tasks are…
71relevance
08
BbioRxiv19 Jan 2022
Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech
Joshua P. Kulasingham, Jonathan Z. Simon
The Temporal Response Function (TRF) is a linear model of neural activity time-locked to continuous stimuli, including continuous speech. TRFs based on speech envelopes typically have distinct components that have provided remarkable insights into the cortical processing of speech. However, current methods may lead to…
70relevance
09
BbioRxiv3 Apr 2024
iScore: A ML-Based Scoring Function for *de novo* Drug Discovery
Sayyed Jalil Mahdizadeh, Leif A. Eriksson
In the quest for accelerating de novo drug discovery, the development of efficient and accurate scoring functions represents a fundamental challenge. This study introduces iScore, a novel machine learning (ML)-based scoring function designed to predict the binding affinity of protein-ligand complexes with remarkable…
68relevance
10
BbioRxiv25 Nov 2021
Systematic Evaluation of Machine Learning Algorithms for Neuroanatomically-Based Age Prediction in Youth
Amirhossein Modabbernia, Heather C. Whalley, David C. Glahn, Paul M. Thompson + 2 more
Application of machine learning algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the machine learning approach in estimating brain-age in children and adolescents is important because age-related…
68relevance
11
BbioRxiv15 Mar 2023
Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees
William Manley, Tam Tran, Melissa Prusinski, Dustin Brisson
General linear models have been the foundational statistical framework used to discover the ecological processes that explain the distribution and abundance of natural populations. Analyses of the rapidly expanding cache of environmental and ecological data, however, require advanced statistical methods to contend with…
67relevance
12
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…
62relevance

Results for Boosting algorithm

RUSBoost: A suitable species distribution method for imbalanced records of presence and absence. A case study of twenty-five species of Iberian bats

Boosting polygenic risk scores

Ensemble Machine Learning to “Boost” Ubiquitination-sites Prediction

Applying gradient tree boosting to QTL mapping with Shapley additive explanations

Extreme gradient boosting machine learning algorithm identifies genome-wide genetic variants in prostate cancer risk prediction

A diagnostic support system based on interpretable machine learning and oscillometry for accurate diagnosis of respiratory dysfunction in silicosis

Transfer learning in heterogeneous drug-target interaction predictions using federated boosting

Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech

iScore: A ML-Based Scoring Function for de novo Drug Discovery

Systematic Evaluation of Machine Learning Algorithms for Neuroanatomically-Based Age Prediction in Youth

Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees

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

RUSBoost: A suitable species distribution method for imbalanced records of presence and absence. A case study of twenty-five species of Iberian bats

Boosting polygenic risk scores

Ensemble Machine Learning to “Boost” Ubiquitination-sites Prediction

Applying gradient tree boosting to QTL mapping with Shapley additive explanations

Extreme gradient boosting machine learning algorithm identifies genome-wide genetic variants in prostate cancer risk prediction

A diagnostic support system based on interpretable machine learning and oscillometry for accurate diagnosis of respiratory dysfunction in silicosis

Transfer learning in heterogeneous drug-target interaction predictions using federated boosting

Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech

iScore: A ML-Based Scoring Function for de novo Drug Discovery

Systematic Evaluation of Machine Learning Algorithms for Neuroanatomically-Based Age Prediction in Youth

Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees

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

Results for Boosting algorithm

RUSBoost: A suitable species distribution method for imbalanced records of presence and absence. A case study of twenty-five species of Iberian bats

Boosting polygenic risk scores

Ensemble Machine Learning to “Boost” Ubiquitination-sites Prediction

Applying gradient tree boosting to QTL mapping with Shapley additive explanations

Extreme gradient boosting machine learning algorithm identifies genome-wide genetic variants in prostate cancer risk prediction

A diagnostic support system based on interpretable machine learning and oscillometry for accurate diagnosis of respiratory dysfunction in silicosis

Transfer learning in heterogeneous drug-target interaction predictions using federated boosting

Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech

iScore: A ML-Based Scoring Function for *de novo* Drug Discovery

Systematic Evaluation of Machine Learning Algorithms for Neuroanatomically-Based Age Prediction in Youth

Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees

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

RUSBoost: A suitable species distribution method for imbalanced records of presence and absence. A case study of twenty-five species of Iberian bats

Boosting polygenic risk scores

Ensemble Machine Learning to “Boost” Ubiquitination-sites Prediction

Applying gradient tree boosting to QTL mapping with Shapley additive explanations

Extreme gradient boosting machine learning algorithm identifies genome-wide genetic variants in prostate cancer risk prediction

A diagnostic support system based on interpretable machine learning and oscillometry for accurate diagnosis of respiratory dysfunction in silicosis

Transfer learning in heterogeneous drug-target interaction predictions using federated boosting

Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech

iScore: A ML-Based Scoring Function for *de novo* Drug Discovery

Systematic Evaluation of Machine Learning Algorithms for Neuroanatomically-Based Age Prediction in Youth

Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees

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

iScore: A ML-Based Scoring Function for de novo Drug Discovery

iScore: A ML-Based Scoring Function for de novo Drug Discovery