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| 1 | BET Bromodomain Inhibition as a Therapeutic Strategy to Target c-Myc显示文摘 | Jake E. Delmore Ghayas C. Issa Madeleine E. Lemieux Peter B. Rahl Junwei Shi Hannah M. Jacobs Efstathios Kastritis Timothy Gilpatrick Ronald M. Paranal Jun Qi Marta Chesi Anna C. Schinzel Michael R. McKeown Timothy P. Heffernan Christopher R. Vakoc P. Lei | 2011 | Cell2011，，6： | 3 |
| 2 | Reciprocity calibration of impulse responses of acoustic emission transducers 显示文摘 | Hatano H Ghaya T Watanabe S | 1998 | IEEE transactions on ultrasonics ferroelectrics and frequency control1998，45，5： | 1 |
| 3 | Is forced oscillation technique the next respiratory function test of choice in childhood asthma显示文摘Respiratory diseases, especially asthma, are common in children. While spirometry contributes to asthma diagnosis and management in older children, it has a limited role in younger children whom are often unable to perform forced expiratory manoeuvre. The development of novel diagnostic methods which require minimal effort, such as forced oscillation technique(FOT) is, therefore, a welcome and promising addition. FOT involves applying external, small amplitude oscillations to the respiratory system during tidal breathing. Therefore, it requires minimal effort and cooperation. The FOT has the potential to facilitate asthma diagnosis and management in preschool children by faciliting the objective measurement of baseline lung function and airway reactivity in children unable to successfully perform spirometry. Traditionally the use of FOT was limited to specialised centres. However, the availability of commercial equipment resulted in its use both in research and in clinical practice. In this article, we review the available literature on the use of FOT in childhood asthma. The technical aspects of FOT are described followed by a discussion of its practical aspects in the clinical field including the measurement of baseline lung function and associated reference ranges, bronchodilator responsiveness and bronchial hyperresponsiveness. We also highlight the difficulties and limitations that might be encountered and future research directions. | Afaf Alblooshi Alia Alkalbani Ghaya Albadi Hassib Narchi Graham Hall | 2017 | World Journal of Methodology2017，7，4： | 1 |
| 4 | AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector显示文摘Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs.Machine Learning(ML)models are also being used to make predictions in these applications.Accurate forecasting requires knowledge of these machine learning models and their distinguishing features.The datasets we use as input for each of these different types of ML models,yielding different results.The choice of an ML model for a dataset is critical.A loan risk model is used to show how ML models for a dataset can be linked together.The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk.This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields.If credit risk is considered,a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried.Various approaches to credit risk calculation will be examined,ranging from the most basic to the most complex.In addition,we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches,logistic regression,decision tree,and gradient boost to see which one produced the most commercially useful insights. | Vikas Kumar Shaiku Shahida Saheb Preeti Atif Ghayas Sunil Kumari Jai Kishan Chandel Saroj Kumar Pandey Santosh Kumar | 2023 | Big Data Mining and Analytics2023，6，4： | 0 |
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