AIMC Topic: Neoplasms

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Artificial intelligence: A transformative tool in precision oncology.

Oncotarget
Artificial intelligence (AI) is revolutionizing society and healthcare, offering new possibilities for precision medicine. Immunotherapy in oncology (IO) has similarly transformed cancer treatment through novel mechanisms of therapeutic action, but h...

MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.

PLoS computational biology
Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a...

Utilizing convolutional neural networks for discriminating cancer and stromal cells in three-dimensional cell culture images with nuclei counterstain.

Journal of biomedical optics
SIGNIFICANCE: Accurate cell segmentation and classification in three-dimensional (3D) images are vital for studying live cell behavior and drug responses in 3D tissue culture. Evaluating diverse cell populations in 3D cell culture over time necessita...

Deep reinforcement learning control of combined chemotherapy and anti-angiogenic drug delivery for cancerous tumor treatment.

Computers in biology and medicine
By virtue of the chronic and dangerous nature of cancer, researchers have explored various approaches to managing the abnormal cell growth associated with this disease using novel treatment methods. This study introduces a control system based on nor...

Evaluation of tumor budding with virtual panCK stains generated by novel multi-model CNN framework.

Computer methods and programs in biomedicine
As the global incidence of cancer continues to rise rapidly, the need for swift and precise diagnoses has become increasingly pressing. Pathologists commonly rely on H&E-panCK stain pairs for various aspects of cancer diagnosis, including the detecti...

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.

Biomolecules
Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships bet...

Deep learning-based multimodal spatial transcriptomics analysis for cancer.

Advances in cancer research
The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostic...

Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses.

Scientific reports
The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug desig...

Perovskite Probe-Based Machine Learning Imaging Model for Rapid Pathologic Diagnosis of Cancers.

ACS nano
Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and...