Empirical Analysis using Feature Selection and Bootstrap Data for Small Sample Size Problems

Zhao, Yuying; Duangsoithong, Rakkrit

doi:10.1109/ecti-con47248.2019.8955366

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…First, XGBoost is equipped to handle data with relatively fewer samples. 61 Second, with only six tuning parameters, the XGBoost model can be more easily tuned than any moderately complex neural network model. 62 Third, boosting has proven itself as a formidable predictive modeling technique and often learns complex relationships similarly to neural network architectures that use gradient decent for optimization.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase

Cheng,

Aitha,

Thomas

et al. 2024

J. Chem. Inf. Model.

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The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved MBL inhibitors are lacking in the clinic even though many strategies have been used in inhibitor development, including quantitative high-throughput screening (qHTS), fragment-based drug discovery (FBDD), and molecular docking. Herein, a machine learningbased prediction tool is described, which was generated using results from HTS of a large chemical library and previously published inhibition data. The prediction tool was then used for virtual screening of the NIH Genesis library, which was subsequently screened using qHTS. A novel MBL inhibitor was identified and shown to lower minimum inhibitory concentrations (MICs) of Meropenem for a panel of E. coli and K. pneumoniae clinical isolates expressing NDM-1. The mechanism of inhibition of this novel scaffold was probed utilizing equilibrium dialyses with metal analyses, native state electrospray ionization mass spectrometry, UV−vis spectrophotometry, and molecular docking. The uncovered inhibitor, compound 72922413, was shown to be 9-hydroxy-3-[(5-hydroxy-1-oxa-9-azaspiro[5.5]undec-9-yl)carbonyl]-4Hpyrido[1,2-a]pyrimidin-4-one.

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Section: ■ Results and Discussionmentioning

confidence: 99%

“…There are several advantages to using XGBoost over neural network architectures. First, XGBoost is equipped to handle data with relatively fewer samples . Second, with only six tuning parameters, the XGBoost model can be more easily tuned than any moderately complex neural network model .…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase

Cheng,

Aitha,

Thomas

et al. 2024

J. Chem. Inf. Model.

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show abstract

“…al., (2018)] is an effective imputing technique for the numerical and categorical attribute values. Other methods such as Expectation Maximization Imputation (EMI) [Zhao and Duangsoithong, (2020)] calculates the imputation values by exploring the average mean and covariance matrix of the dataset. But the problem with this method is that it uses the highly correlated data amongst the attributes.…”

Section: Previous Research Studymentioning

confidence: 99%

A Machine Learning Algorithm for Multi-Source Heterogeneous Data With Block-Wise Missing Information

Ghorpade-Aher¹,

Sonkamble²

2022

INDJCSE

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The heterogeneous data coming from varied sources is integrated to induce learning as it may have complementary information. The growing research for multi-source heterogeneous data analysis has extended the traditional methods of single source homogeneous data. One of the major challenges is processing the heterogeneous data while integrating the information from different sources with various missing data patterns. The potential features act as the useful predictors, but rejecting the observations with missing data values will lose the information which may create biased conclusions affecting the model strength for uniform decisions. A general imputation technique should be preferred for its computational simplicity and capability to induce a diminutive bias in the dataset. The proposed 'Heterogeneous Data and Weight Algorithm' constructs the robust model averaging techniques and feasible estimations with advanced Machine Learning techniques that implements the weighted probability approach to enhance the performance of the model with better predictive power.

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“…Thus, gradient boosted trees result in the identification of variables that have the overall greatest influence on predictive accuracy. The method is amenable to analysis with relatively smaller sample sizes (Zhao & Duangsoithong, 2019), is not impacted by multicollinearity (Ding, Wang, Ma, & Li, 2016), and has been used in the past for prediction tasks involving social media popularity (Li et al, 2017) and tweets (Ong, Rahmanto, Suhartono, Nugroho, & Andangsari, 2017). In this analysis gradient boosted decision trees were implemented via extreme gradient boosting (commonly known as XGBoost; Chen & Guestrin, 2016).…”

Section: The Effect Of Press Releases On Fearmentioning

confidence: 99%

Scared into Action: How Partisanship and Fear are Associated with Reactions to Public Health Directives

Lindow¹,

DeFranza²,

Mishra³

et al. 2021

Preprint

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Differences in political ideology are increasingly appearing as an impediment to successful bipartisan communication from local leadership. For example, recent empirical findings have shown that conservatives are less likely to adhere to COVID-19 health directives. This behavior is in direct contradiction to past research which indicates that conservatives are more rule abiding, prefer to avoid loss, and are more prevention-motivated than liberals. We reconcile this disconnect between recent empirical findings and past research by using insights gathered from press releases, millions of tweets, and mobility data capturing local movement in retail, grocery, workplace, parks, and transit domains during COVID-19 shelter-in-place orders. We find that conservatives adhere to health directives when they express more fear of the virus. In order to better understand this phenomenon, we analyze both official and citizen communications and find that press releases from local and federal government, along with the number of confirmed COVID-19 cases, lead to an increase in expressions of fear on Twitter.

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Empirical Analysis using Feature Selection and Bootstrap Data for Small Sample Size Problems

Cited by 9 publications

References 9 publications

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase

A Machine Learning Algorithm for Multi-Source Heterogeneous Data With Block-Wise Missing Information

Scared into Action: How Partisanship and Fear are Associated with Reactions to Public Health Directives

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