Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

Mistry, Pritesh; Neagu, Daniel; Trundle, Paul R.; Vessey, Jonathan D.

doi:10.1007/s00500-015-1925-9

Cited by 36 publications

(22 citation statements)

References 45 publications

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“…Similar to kNN algorithms, it is also used to for classification and to predict regression [80]. Compared to DTs, it is impossible that RF over-fits the data, and the RF has been used for bioactivity data classification [81], toxicity modeling [82], and drug target prediction [83], etc. Wang et al [84] used the RF approach to model the binding affinity of protein-ligand on 170 HIV-1 proteases complexes, 110 trypsin complexes, and 126 carbonic anhydrase complexes, which demonstrated that individual representation and model construction for each protein family is a more reasonable way in predicting the affinity of one particular protein family.…”

Section: Classical Qsar Methodsmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure-and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.Molecules 2020, 25, 1375 2 of 17 Biomolecular Simulations in Drug Screening and DesignThe Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt, and Arieh Warshel for their pioneering contributions in the development of multiscale models for complex biochemical systems, recognizing the important role that theory and computational methods play as a direct and necessary complement to experiments [12]. Further, applications of biomolecular simulations in identifying drug binding sites and elucidating the molecular mechanisms of diseases have been subject to rapid developments [13][14][15].Depending on the biological problems to be studied, multiscale models will be used in biomolecular simulations. The combination of quantum mechanics (QM) and molecular mechanics (MM) (QM/MM) can be used to study the electronic properties [16], simulate chemical reactions (e.g., the enzyme catalysis mechanism [17]), and calculate spectra [18] in a single simulation, which can be used to elucidate the action mechanism of certain drugs. Another widely used biomolecular method is molecular dynamics (MD) simulation [19][20][21][22][23][24], which applies empirical molecular mechanics (MM) force fields and is based on classical Newtonian mechanics. According to different accuracy requirements, all-atom (AA), united-atom (UA), and coarse-grained (CG) MD simulations as well as explicit/implicit solvent models, which allow simulations of temporal and spatial scales, can be used to facilitate the drug discovery [25,26]. Generally, MD simulations have been used for the identification of potential drug binding sites on target proteins, the calculation of binding free energy between target proteins and drug molecules, the action mechanism of drug molecules, etc. [27,28]. However, it is worth mentioning that MD simulations are also limited to time and length scales. Currently, AAMD simulations can explore time-dependent phenomena of large systems such as viral capsids in atomic detail as long as microseconds or even milliseconds [21,29,30]. Therefore, different types of ...

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Section: Classical Qsar Methodsmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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“…Scholars explore the correlation between the capacity utilization rate and the market supply of online ride-hailing and traditional taxi services, respectively [14]. They also analyze diverse service modes, service functions, price differences, and service profits in different forms of business [15][16][17][18]. Others investigate the effect of market mechanism on resource distribution [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Imbalanced Profit Margins of Urban Online Car-hailing Services: A Case from Didi Service Platform of China

2019

Teh. vjesn.

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Online car-hailing services are driven by the emergence and development of network technology and information sharing, which breaks the traditional balance of Chinese taxi market and reconstitutes profit allocation, bringing new opportunities and challenges to the development and management of traditional taxi industry. To improve the operation efficiency of taxi and promote the effective utilization of resources, this study presented a calculation method for the capacity utilization rate and constructed a taxi service profit rate model. Taking Beijing and Shanghai of China as examples, the profit margins of ride-hailing and online taxi services using the Didi trajectory data from December 22 to December 24, 2017 were compared and analyzed. Results demonstrate that the profits of different car-hailing services in China are imbalanced in terms of time and space dimensions. The search costs for drivers and passengers are high, especially during morning and evening rush hours. The low capacity utilization rates for taxis and ride-hailing services resulted in a conflict between low supply and high demand. The profit margins of taxis are much higher than those of car-hailing services. Moreover, profits on weekends are higher than those on workdays. Long distance orders (>15 km) and relatively short ones (<3km) generate higher profits. Relevant departments should rationally plan the holding volume of online car-hailing services and coordinate the service profit margins in different service modes to realize the sustainable development of the new and old taxi industry.

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“…A subset of the training dataset (local set) is chosen to grow individual trees, with the remaining samples used to estimate the goodness of fit. Trees are grown by splitting the local set at each node according to the value of a random variable sampled independently from a subset of variables [26].…”

Section: Random Forestmentioning

confidence: 99%

Machine Learning for Analyzing Malware

Liu

Zeng

Yan

et al. 2017

JCSM

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The Internet has become an indispensable part of people's work and life, but it also provides favorable communication conditions for malwares. Therefore, malwares are endless and spread faster and become one of the main threats of current network security. Based on the malware analysis process, from the original feature extraction and feature selection to malware analysis, this paper introduces the machine learning algorithms such as classification, clustering and association analysis, and how to use these machine learning algorithms to effectively analyze the malware and its variants.

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Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

Cited by 36 publications

References 45 publications

A Review on Applications of Computational Methods in Drug Screening and Design

A Review on Applications of Computational Methods in Drug Screening and Design

Imbalanced Profit Margins of Urban Online Car-hailing Services: A Case from Didi Service Platform of China

Machine Learning for Analyzing Malware

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