An Introduction to Fundamentals of Cancer Biology

Sriharikrishnaa, S.; Suresh, Padmanaban S.; Kabekkodu, Shama Prasada

doi:10.1007/978-3-031-31852-8_11

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2024

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Cited by 7 publications

References 122 publications

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3D Segmentation and Subtypes Classification of Breast Cancer Using Ultrasound Images Through Hybrid Approach of CNN and LSTM

Bala,

Priyadharshini,

Palani

2024

Learning and Analytics in Intelligent Systems

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3D Segmentation and Subtypes Classification of Breast Cancer Using Ultrasound Images Through Hybrid Approach of CNN and LSTM

Bala,

Priyadharshini,

Palani

2024

Learning and Analytics in Intelligent Systems

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Spheroids in cancer research: Recent advances and opportunities

Arora,

Singh,

Mittal

et al. 2024

Journal of Drug Delivery Science and Technology

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Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems

Jahandoost,

Dashti,

Houshmand

et al. 2024

Sci Rep

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Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Nanotherapeutics, which merge nanotechnology with targeted drug delivery, exemplify this advancement by offering improved precision and reduced side effects in cancer therapy. The development of these nanotherapeutic agents depends critically on understanding nanoparticle (NP) properties and their biological interactions, often analyzed through molecular dynamics (MD) simulations. This study enhances these analyses by integrating ML with MD simulations, significantly improving both prediction accuracy and computational efficiency. We introduce a comprehensive three-stage methodology for predicting the solvent-accessible surface area (SASA) of NPs, which is crucial for their therapeutic efficacy. The process involves training an ML model to forecast the many-body tensor representation (MBTR) for future time steps, applying data augmentation to increase dataset realism, and refining the SASA predictor with both augmented and original data. Results demonstrate that our methodology can predict SASA values 299 time steps ahead with a 40-fold speed improvement and a 25% accuracy increase over existing methods. Importantly, it provides a 300-fold increase in computational speed compared to traditional simulation techniques, offering substantial cost and time savings for nanotherapeutic research and development.

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An Introduction to Fundamentals of Cancer Biology

Cited by 7 publications

References 122 publications

3D Segmentation and Subtypes Classification of Breast Cancer Using Ultrasound Images Through Hybrid Approach of CNN and LSTM

3D Segmentation and Subtypes Classification of Breast Cancer Using Ultrasound Images Through Hybrid Approach of CNN and LSTM

Spheroids in cancer research: Recent advances and opportunities

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems

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