13 papers · ranked by Valyu relevance
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…
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…
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…
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…