A Review of Deep Learning with Recurrent Neural Network

Kaur, Manjot

doi:10.1109/icssit46314.2019.8987837

Cited by 48 publications

(11 citation statements)

References 3 publications

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“…LSTM networks, falling under the umbrella of recurrent neural networks (RNNs), exhibit a noteworthy proficiency in grasping long-term dependencies, as exemplified in [22]. The architecture of an LSTM involves the intricate construction of a memory cell utilizing logistic and linear units with multiplicative interactions.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Classification of Cancer Microarray Data Based on Deep Learning: A Review

Fadhil,

Abdulazeez

2024

ijcs

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This review article delves into applying deep learning methodologies in conjunction with microarray data for cancer classification. The study provides a comprehensive overview of recent advancements in utilizing deep learning techniques to accurately categorize cancer types based on intricate patterns discerned from microarray datasets. Various aspects are covered, including integrating deep learning algorithms, exploring diverse cancer types, and analyzing microarray data to enhance classification accuracy. The review synthesizes findings from recent research, highlighting the efficacy of deep learning in uncovering subtle and complex relationships within microarray data that contribute to improved classification outcomes. Key insights into the strengths and limitations of employing deep learning in this context are discussed, offering a critical appraisal of the field's current state. This review aims to provide a valuable resource for researchers, clinicians, and practitioners interested in cutting-edge developments in cancer classification methodologies by exploring the intersection of deep learning and microarray technology. The synthesis of knowledge presented herein contributes to a deeper understanding of the potential and challenges associated with harnessing deep learning for enhanced classification accuracy in the realm of cancer research.

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Classification of Cancer Microarray Data Based on Deep Learning: A Review

Fadhil,

Abdulazeez

2024

ijcs

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“…Molecular generation in SBMolGen is done by ChemTS (Yang et al, 2017 ), and rDock (Ruiz-Carmona et al, 2014 ) is employed for docking generated compounds to the target. In the SBMolGen methodology, ChemTS first generates compounds with MCTS and a recurrent neural network (RNN) (Kaur and Mohta, 2019 ). These generated models are then docked using rDock and evaluated.…”

Section: De Novo Drug Designmentioning

confidence: 99%

Applications of machine learning in computer-aided drug discovery

2022

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Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

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“…The main idea of RNN is to interact with sequential data [ 160 ]. The input and output of a traditional Neural Network are independent of each other.…”

Section: Overview Of Deep Learning In Precision Oncologymentioning

confidence: 99%

A Survey on Deep Learning for Precision Oncology

2022

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Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient’s disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions.

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A Review of Deep Learning with Recurrent Neural Network

Cited by 48 publications

References 3 publications

Classification of Cancer Microarray Data Based on Deep Learning: A Review

Classification of Cancer Microarray Data Based on Deep Learning: A Review

Applications of machine learning in computer-aided drug discovery

A Survey on Deep Learning for Precision Oncology

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