PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine

Deng, Lei; Pan, Juan; Xu, Xiaojie; Yang, Wenyi; Liu, Chuyao; Liu, Hui

doi:10.1186/s12859-018-2527-1

Cited by 45 publications

(24 citation statements)

References 59 publications

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“…Machine learning techniques have been applied to tasks in radiology 59 , pathology 60 , and critical care 28,30 in retrospective clinical studies. Approaches spanning a spectrum of complexity have been developed to tackle clinical prediction problems, from linear models [61][62][63] to complex deep architectures 64 . In this work, we used gradient-boosted decision trees due to their observed superior performance in our application and ease of interrogation.…”

Section: Discussionmentioning

confidence: 99%

Early prediction of circulatory failure in the intensive care unit using machine learning

Hyland

Faltys

Hüser

et al. 2020

Nat Med

277

207

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Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patientyears of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort.

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Section: Discussionmentioning

confidence: 99%

Early prediction of circulatory failure in the intensive care unit using machine learning

Hyland

Faltys

Hüser

et al. 2020

Nat Med

277

207

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“…Many previous studies have used LightGBM for analysis of medical data 37 – 40 . LightGBM is a GBM-based model that follows XGBoost.…”

Section: Methodsmentioning

confidence: 99%

Development of machine learning model for diagnostic disease prediction based on laboratory tests

Park

Lee³

et al. 2021

Sci Rep

105

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The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.

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“…The stacking heterogeneous ensemble method. Among machine learning methods, the performance of ensemble learning methods [56][57][58][59][60][61][62] is very superior, so we use ensemble learning methods to predict the binding affinity of protein-DNA complexes. As one of the unique ensemble learning algorithms of ensemble learning, the stacking heterogeneous ensemble approach has a superior appearance.…”

Section: Methodsmentioning

confidence: 99%

PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity

Yang

Deng

2020

Sci Rep

Self Cite

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The interaction between protein and DNA plays an essential function in various critical natural processes, like DNA replication, transcription, splicing, and repair. Studying the binding affinity of proteins to DNA helps to understand the recognition mechanism of protein-DNA complexes. Since there are still many limitations on the protein-DNA binding affinity data measured by experiments, accurate and reliable calculation methods are necessarily required. So we put forward a computational approach in this paper, called PreDBA, that can forecast protein-DNA binding affinity effectively by using heterogeneous ensemble models. One hundred protein-DNA complexes are manually collected from the related literature as a data set for protein-DNA binding affinity. Then, 52 sequence and structural features are obtained. Based on this, the correlation between these 52 characteristics and protein-DNA binding affinity is calculated. Furthermore, we found that the protein-DNA binding affinity is affected by the DNA molecule structure of the compound. We classify all protein-DNA compounds into five classifications based on the DNA structure related to the proteins that make up the protein-DNA complexes. In each group, a stacked heterogeneous ensemble model is constructed based on the obtained features. In the end, based on the binding affinity data set, we used the leave-one-out cross-validation to evaluate the proposed method comprehensively. In the five categories, the Pearson correlation coefficient values of our recommended method range from 0.735 to 0.926. We have demonstrated the advantages of the proposed method compared to other machine learning methods and currently existing protein-DNA binding affinity prediction approach.

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PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine

Cited by 45 publications

References 59 publications

Early prediction of circulatory failure in the intensive care unit using machine learning

Early prediction of circulatory failure in the intensive care unit using machine learning

Development of machine learning model for diagnostic disease prediction based on laboratory tests

PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity

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