AIMC Topic: Neoplasms

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SyntheVAEiser: augmenting traditional machine learning methods with VAE-based gene expression sample generation for improved cancer subtype predictions.

Genome biology
The accuracy of machine learning methods is often limited by the amount of training data that is available. We proposed to improve machine learning training regimes by augmenting datasets with synthetically generated samples. We present a method for ...

Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction.

BMC bioinformatics
The evaluation of drug-gene-disease interactions is key for the identification of drugs effective against disease. However, at present, drugs that are effective against genes that are critical for disease are difficult to identify. Following a diseas...

Machine learning-assisted pattern recognition and imaging of multiplexed cancer cells a porphyrin-embedded dendrimer array.

Journal of materials chemistry. B
Early cancer detection plays a vital role in improving the survival rate of cancer patients, underscoring the importance of developing cancer detection methods. However, it is a great challenge to achieve simple, rapid, and accurate methods for simul...

Deep profiling of gene expression across 18 human cancers.

Nature biomedical engineering
Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this im...

Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting.

Medical oncology (Northwood, London, England)
MicroRNAs (miRNAs), a class of small non-coding RNAs, play a vital role in regulating gene expression at the post-transcriptional level. Their discovery has profoundly impacted therapeutic strategies, particularly in cancer treatment, where RNA thera...

Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants.

Redox biology
OBJECTIVE: To develop and validate a machine learning model incorporating dietary antioxidants to predict cardiovascular disease (CVD)-cancer comorbidity and to elucidate the role of antioxidants in disease prediction.

Utility of Chatbot Literature Search in Radiation Oncology.

Journal of cancer education : the official journal of the American Association for Cancer Education
Artificial intelligence and natural language processing tools have shown promise in oncology by assisting with medical literature retrieval and providing patient support. The potential for these technologies to generate inaccurate yet seemingly corre...

Empowering cancer prevention with AI: unlocking new frontiers in prediction, diagnosis, and intervention.

Cancer causes & control : CCC
Artificial intelligence is rapidly changing our world at an exponential rate and its transformative power has extensively reached important sectors like healthcare. In the fight against cancer, AI proved to be a novel and powerful tool, offering new ...

Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction.

Frontiers in immunology
BACKGROUND: Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. Multi-cancer early detection (MCED) tests have been considered to address the challenge by simultaneously...