AIMC Topic: Neoplasms

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Gut microbiome, big data and machine learning to promote precision medicine for cancer.

Nature reviews. Gastroenterology & hepatology
The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome anal...

Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers.

ACS sensors
Extracellular vesicles (EVs) are a frontier class of circulating biomarkers for the diagnosis and prognosis of different diseases. These lipid structures afford various biomarkers such as the concentrations of the EVs () themselves and carried protei...

Automated spheroid generation, drug application and efficacy screening using a deep learning classification: a feasibility study.

Scientific reports
The last two decades saw the establishment of three-dimensional (3D) cell cultures as an acknowledged tool to investigate cell behaviour in a tissue-like environment. Cells growing in spheroids differentiate and develop different characteristics in c...

DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.

Scientific reports
Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the...

A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The application of machine learning and artificial intelligence to high-dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, wher...

Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics.

Expert review of molecular diagnostics
INTRODUCTION: Raman and Infrared spectroscopic techniques are being used for the analysis of different types of cancers and other biological molecules. It is possible to identify cancers from normal tissues both in fresh and fixed tissues. These tech...

Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles.

Biomolecules
In silico models to predict which tumors will respond to a given drug are necessary for Precision Oncology. However, predictive models are only available for a handful of cases (each case being a given drug acting on tumors of a specific cancer type)...

Deep learning-based survival prediction for multiple cancer types using histopathology images.

PloS one
Providing prognostic information at the time of cancer diagnosis has important implications for treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic ...

Artificial intelligence system for detecting superficial laryngopharyngeal cancer with high efficiency of deep learning.

Head & neck
BACKGROUND: There are no published reports evaluating the ability of artificial intelligence (AI) in the endoscopic diagnosis of superficial laryngopharyngeal cancer (SLPC). We presented our newly developed diagnostic AI model for SLPC detection.

Stratifying patients using fast multiple kernel learning framework: case studies of Alzheimer's disease and cancers.

BMC medical informatics and decision making
BACKGROUND: Predictive patient stratification is greatly emerging, because it allows us to prospectively identify which patients will benefit from what interventions before their condition worsens. In the biomedical research, a number of stratificati...