PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm

Xu, Qing; Xiong, Yi; Dai, Hao; Kumari, Kotni Meena; Xu, Qin; Ou, Hong‐Yu; Wei, Dong‐Qing

doi:10.1016/j.jtbi.2017.01.019

Cited by 90 publications

(50 citation statements)

References 64 publications

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“…FN represents the data that are mistaken as negative cases by the model but are actually positive cases. The precision rate is the proportion of true positive cases (TP) relative to all positive cases (TP+FP) judged by the model (Xiong et al, 2012;Xu et al, 2017;.…”

Section: Evaluation Indexes and Methodsmentioning

confidence: 99%

Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

et al. 2020

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Complex diseases seriously affect people's physical and mental health. The discovery of disease-causing genes has become a target of research. With the emergence of bioinformatics and the rapid development of biotechnology, to overcome the inherent difficulties of the long experimental period and high cost of traditional biomedical methods, researchers have proposed many gene prioritization algorithms that use a large amount of biological data to mine pathogenic genes. However, because the currently known genedisease association matrix is still very sparse and lacks evidence that genes and diseases are unrelated, there are limits to the predictive performance of gene prioritization algorithms. Based on the hypothesis that functionally related gene mutations may lead to similar disease phenotypes, this paper proposes a PU induction matrix completion algorithm based on heterogeneous information fusion (PUIMCHIF) to predict candidate genes involved in the pathogenicity of human diseases. On the one hand, PUIMCHIF uses different compact feature learning methods to extract features of genes and diseases from multiple data sources, making up for the lack of sparse data. On the other hand, based on the prior knowledge that most of the unknown gene-disease associations are unrelated, we use the PU-Learning strategy to treat the unknown unlabeled data as negative examples for biased learning. The experimental results of the PUIMCHIF algorithm regarding the three indexes of precision, recall, and mean percentile ranking (MPR) were significantly better than those of other algorithms. In the top 100 global prediction analysis of multiple genes and multiple diseases, the probability of recovering true gene associations using PUIMCHIF reached 50% and the MPR value was 10.94%. The PUIMCHIF algorithm has higher priority than those from other methods, such as IMC and CATAPULT.

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Section: Evaluation Indexes and Methodsmentioning

confidence: 99%

Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

et al. 2020

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“…Yi Xiong et al [49], developed a computational method for Prediction of effective Drug Combinations using a Stochastic Gradient Boosting algorithm, termed PDC-SGB. They integrated six features to describe the drug combinations, which include the molecular 2D structures, structural similarity, anatomical therapeutic similarity, protein-protein interaction, chemical-chemical interaction, and disease pathways.…”

Section: Supervised Methodsmentioning

confidence: 99%

In-Silico Methodologies for Cancer Multidrug Optimization

Hasan¹,

Eldin

Khedr

et al. 2018

IJCT

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Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear. It is not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer and discuss the limitations and challenges of these methods.

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“…However, unlike the previous two methods, Boosting methods learn multiple classifiers (Decision Trees in this case) on different versions of the training data and eventually combine them to form a single decision rule that has better accuracy than the individual components. Different versions of Boosting techniques have been successfully used in several biomedical studies (Xu et al 2017;Veta et al 2015;Ochs et al 2007). Classification trees (base components in boosting) can handle mixed data (numeric and categorical) and it has practical benefits for our problem.…”

Section: Problem Formalizationmentioning

confidence: 99%

Machine Learning Approach to Predicting Stem-Cell Donor Availabilitys

Sivasankaran

Williams²,

Albrecht³

et al. 2018

Preprint

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Abstract:The success of Unrelated Donor stem-cell transplants depends not only on finding genetically matched donors but also on donor availability. On average 50% of potential donors are unavailable for a variety of reasons after initially matching a patient with significant variations in availability among subgroups (e.g., by race or age). Several studies have established univariate donor characteristics associated with availability. Individual consideration of each applicable characteristic is laborious. Extrapolating group averages to individual donor level tends to be highly inaccurate. In the current environment with enhanced donor data collection, we can make better estimates of individual donor availability. In this study, we propose a Machine Learning based approach to predict availability of every registered donor, to be used during donor selection and reduce the time taken to complete a transplant.

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PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm

Cited by 90 publications

References 64 publications

Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

In-Silico Methodologies for Cancer Multidrug Optimization

Machine Learning Approach to Predicting Stem-Cell Donor Availabilitys

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