AIMC Topic: Neoplasms

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Interpretable deep learning for improving cancer patient survival based on personal transcriptomes.

Scientific reports
Precision medicine chooses the optimal drug for a patient by considering individual differences. With the tremendous amount of data accumulated for cancers, we develop an interpretable neural network to predict cancer patient survival based on drug p...

Immunodiagnosis - the promise of personalized immunotherapy.

Frontiers in immunology
Immunotherapy showed remarkable efficacy in several cancer types. However, the majority of patients do not benefit from immunotherapy. Evaluating tumor heterogeneity and immune status before treatment is key to identifying patients that are more like...

Multi-institutional PET/CT image segmentation using federated deep transformer learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Generalizable and trustworthy deep learning models for PET/CT image segmentation necessitates large diverse multi-institutional datasets. However, legal, ethical, and patient privacy issues challenge sharing of datasets betw...

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.

BMC bioinformatics
BACKGROUND: P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of ...

The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer.

Computers in biology and medicine
Cancer drug response prediction based on genomic information plays a crucial role in modern pharmacogenomics, enabling individualized therapy. Given the expensive and complexity of biological experiments, computational methods serve as effective tool...

Aligned deep neural network for integrative analysis with high-dimensional input.

Journal of biomedical informatics
OBJECTIVE: Deep neural network (DNN) techniques have demonstrated significant advantages over regression and some other techniques. In recent studies, DNN-based analysis has been conducted on data with high-dimensional input such as omics measurement...

Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images.

Scientific reports
This study aimed to evaluate the performance of traditional-deep learning combination model based on Doppler ultrasound for diagnosing malignant complex cystic and solid breast nodules. A conventional statistical prediction model based on the ultraso...

Emerging Pharmacotherapeutic Strategies to Overcome Undruggable Proteins in Cancer.

International journal of biological sciences
Targeted therapies in cancer treatment can improve efficacy and reduce adverse effects by altering the tissue exposure of specific biomolecules. However, there are still large number of target proteins in cancer are still undruggable, owing to the f...

Intelligent solution predictive networks for non-linear tumor-immune delayed model.

Computer methods in biomechanics and biomedical engineering
In this article, we analyze the dynamics of the non-linear tumor-immune delayed (TID) model illustrating the interaction among tumor cells and the immune system (cytotoxic T lymphocytes, T helper cells), where the delays portray the times required fo...

Using a deep learning neural network for the identification of malignant cells in effusion cytology material.

Cytopathology : official journal of the British Society for Clinical Cytology
AIM: To evaluate the application of an artificial neural network in the detection of malignant cells in effusion samples.