27 papers · ranked by Valyu relevance
Andreas Mayr, Harald Binder, Olaf Gefeller, Matthias Schmid
Background: The concept of boosting emerged from the field of machine learning. The basic idea is to boost the accuracy of a weak classifying tool by combining various instances into a more accurate prediction. This general concept was later adapted to the field of statistical modelling. Nowadays, boosting algorithms…
Sarah E. Lindley, Yiyang Lu, Diwakar Shukla
Guide to Machine Learning for Small Molecule Design Authors: ['Sarah\nE. Lindley' 'Yiyang Lu' 'Diwakar Shukla'] Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML…
Mert Bal, M. Fatih Amasyali, Hayri Sever, Guven Kose + 1 more
The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select…
Deepti Rani, Nasib Singh Gill, Preeti Gulia, Jyotir Moy Chatterjee
Internet of Things (IoT) is the fastest growing technology that has applications in various domains such as healthcare, transportation. It interconnects trillions of smart devices through the Internet. A secure network is the basic necessity of the Internet of Things. Due to the increasing rate of interconnected and…
Jisu Kim, De-Shuang Huang, Kyungsook Han
Background Supervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In…
Robert W. Rankin
This study presents a new boosting method for capture-recapture models, rooted in predictive-performance and machine-learning. The regularization algorithm combines Expectation-Maximization and boosting to yield a type of multimodel inference, including automatic variable selection and control of model complexity. By…
Hristos Tyralis, Georgia Papacharalampous
Machine learning algorithms have been extensively exploited in energy research, due to their flexibility, automation and ability to handle big data. Among the most prominent machine learning algorithms are the boosting ones, which are known to be "garnering wisdom from a council of fools", thereby transforming weak…
Gérard Biau, Benoı̂t Cadre, Laurent Rouvìère
Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem. We combine gradient boosting and Nesterov's accelerated descent to design a new algorithm, which we call AGB (for Accelerated…
Jiming Liu, Dongjin Xu
Lithology is a key parameter in reservoir fine description and evaluation. It is difficult to directly identify reservoir lithology using a single logging curve or conventional cross-plot method due to the mud-gravel mixing in complex reservoirs. The accurate identification of conglomerate reservoir lithology has…
Riccardo De Bin, Vegard Grødem Stikbakke
In this paper we propose a boosting algorithm to extend the applicability of a first hitting time model to high-dimensional frameworks. Based on an underlying stochastic process, first hitting time models do not require the proportional hazards assumption, hardly verifiable in the high-dimensional context, and…
Ebenezer Owusu, Prince Boakye-Sekyerehene, Justice Kwame Appati, Julius Yaw Ludu
'Julius Yaw Ludu'] Heart diseases are a leading cause of death worldwide, and they have sparked a lot of interest in the scientific community. Because of the high number of impulsive deaths associated with it, early detection is critical. This study proposes a boosting Support Vector Machine (SVM) technique as the…
Aleksandr Y. Aravkin, Giulio Bottegal, Gianluigi Pillonetto
Boosting combines weak (biased) learners to obtain effective learning algorithms for classification and prediction. In this paper, we show a connection between boosting and kernelbased methods, highlighting both theoretical and practical applications. In the context of `2 boosting, we start with a weak linear learner…
Mohammad Taha Toghani, Genevera I. Allen
—Boosting methods are among the best generalpurpose and off-the-shelf machine learning approaches, gaining widespread popularity. In this paper, we seek to develop a boosting method that yields comparable accuracy to popular AdaBoost and gradient boosting methods, yet is faster computationally and whose solution is…
Kaidong Wang, Yao Wang, Qian Zhao, Deyu Meng + 1 more
—It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers. Therefore, several Boosting…
Michał Sołtys, Szymon Jaroszewicz
Uplift modeling is an area of machine learning which aims at predicting the causal effect of some action on a given individual. The action may be a medical procedure, marketing campaign, or any other circumstance controlled by the experimenter. Building an uplift model requires two training sets: the treatment group…
Kaiqiao Li, Sijie Yao, Zhenyu Zhang, Biwei Cao + 4 more
Gradient boosting decision tree (GBDT) is a powerful ensemble machine learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as Extreme Gradient Boosting (XGB) and Light Gradient Boosting (LGB), have rendered the GBDT training…
Bibhuprasad Sahu, Amrutanshu Panigrahi, Abhilash Pati, Manmath Nath Das + 4 more
'Manmath Nath Das' 'Prince Jain' 'Ghanashyam Sahoo' 'Haipeng Liu' 'Mohammad Rahimi-Gorji'] Objective: The cancer death rate has accelerated at an alarming rate, making accurate diagnosis at the primary stages crucial to enhance prognosis. This has deepened the issue of cancer mortality, which is already at an…
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…
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…
Itamar Borges Jr, Júlio César Duarte, Romulo Dias da Rocha
We decomposed density functional theory charge densities of 53 nitroaromatic molecules into atom-centered electric multipoles using the distributed multipole analysis that provides a detailed picture of the molecular electronic structure. Three electric multipoles, ∑▒〖Q_0 (NO_2)〗 (the charge of the nitro groups)…
Authors not listed
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…
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…
Authors not listed
Hydration free energy (HFE) of molecules is a fundamental property having impor- tance throughout chemistry and biology. Calculation of the HFE can be challenging and expensive with classical molecular dynamics simulation-based approaches. Ma- chine learning (ML) models are increasingly being used to predict HFE.…
Authors not listed
Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
Authors not listed
Background: Janus Kinase 2 (JAK2) is a key kinase in cellular signal transduction. Its abnormal activation is closely related to various myeloproliferative neoplasms and inflammatory diseases. Developing selective JAK2 inhibitors is an important direction in drug discovery. Accurate prediction of compound inhibitory…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Feng Feng, Zhenru Chen, Jianyuan Ni, Yuanxun Zhang + 3 more
Drinking water is essential to public health and socioeconomic growth. Therefore, assessing and ensuring drinking water supply is a critical task in modern society. Conventional approaches to analyzing and controlling drinking water quality are labor-intensive and costly with a low throughput. Machine learning (ML) is…