Efficient prediction of drug–drug interaction using deep learning models

Shukla, Prashant Kumar; Shukla, Piyush Kumar; Sharma, Poonam; Rawat, Prashant; Samar, Jashwant; Moriwal, Rahul; Kaur, Manjit

doi:10.1049/iet-syb.2019.0116

Cited by 80 publications

(16 citation statements)

References 38 publications

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“…In [26], a genetic algorithm was utilized to compute the epoch size, number of layers, units size in every layer, and time window size. However, [24][25][26][27] suffer from the stuck in local optima and poor convergence speed issues.…”

Section: Related Workmentioning

confidence: 99%

Evolving Long Short-Term Memory Network-Based Text Classification

Singh

Dargar

Gupta³

et al. 2022

Computational Intelligence and Neuroscience

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Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a well-known factory reports dataset. Extensive analyses are performed to evaluate the performance of the proposed ELSTM network. From the comparative analysis, it is found that the LSTM network outperforms the competitive models.

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Section: Related Workmentioning

confidence: 99%

Evolving Long Short-Term Memory Network-Based Text Classification

Singh

Dargar

Gupta³

et al. 2022

Computational Intelligence and Neuroscience

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“…This urges the need for educational systems to find alternatives and promote remote learning strategies worldwide. Different approaches in deep learning have been identified using training models and neural networks for specific contextual datasets (Jaiswal et al 2020;Shukla et al 2020), image encrypted techniques based on chaotic maps (Kaur et al 2020;Gupta et al 2020) and image fusion theories (Kaur and Singh 2019). Adjusting learning contents collectively with context-aware reactions, cognitive modules and design principles with these key methods could define better algorithm across the mobile learning platforms and help learners to concentrate more on their abilities to reach out a constructive learning performance.…”

Section: Resultsmentioning

confidence: 99%

Cognitive load management in mobile learning systems: principles and theories

Curum

Khedo

2020

J. Comput. Educ.

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With the widespread adoption of mobile technologies, mobile-assisted learning is gaining lots of momentum. This new learning paradigm promotes education across different contexts, which is a key factor that contributes to enhancing learning irrespective of the conditions and location of the learner. Therefore, it creates an authentic learning setting whereby students can make meaningful connections to the real world while learning takes place. Previous research works in the field of mobile learning showed that improper design of learning elements is still present in mobile systems and consequently results in poor dynamic content adaptation. Some attempts to adapt learning contents with appropriate instructional design principles are conducted, but with moderate exploitation of smart technological assets in mobile learning systems and limited pedagogical reflections and cognitive factors. In this paper, a learning efficiency model chart is derived using important learning factors that can be considered to enhance mobile learning experiences. Some popular learning theories are analysed and compared with the proposed learning efficiency model chart. This investigation is considered to significantly reduce complexities that exist in mobile learning platforms and promote an enhanced mobile learning experience.

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“…The early studies consider DTI a binary classification problem and later various machine learning methods are used to approach DTI problems ( Öztürk et al, 2018 ; Song et al, 2021 ). Drug-Drug Interaction (DDI) studies explore the effect variations of a drug when the drug is taken at the same time with another ( Kumar Shukla et al, 2020 ). Drug interaction profiles can be established to measure drug similarities and associations.…”

Section: Drug Repurposingmentioning

confidence: 99%