AIMC Topic: Neoplasms

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3-D Quantification of Filopodia in Motile Cancer Cells.

IEEE transactions on medical imaging
We present a 3D bioimage analysis workflow to quantitatively analyze single, actin-stained cells with filopodial protrusions of diverse structural and temporal attributes, such as number, length, thickness, level of branching, and lifetime, in time-l...

Dictionary Representations for Electrode Displacement Elastography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound electrode displacement elastography (EDE) has demonstrated the potential to monitor ablated regions in human patients after minimally invasive microwave ablation procedures. Displacement estimation for EDE is commonly plagued by decorrelat...

Machine Learning Classification and Structure-Functional Analysis of Cancer Mutations Reveal Unique Dynamic and Network Signatures of Driver Sites in Oncogenes and Tumor Suppressor Genes.

Journal of chemical information and modeling
In this study, we developed two cancer-specific machine learning classifiers for prediction of driver mutations in cancer-associated genes that were validated on canonical data sets of functionally validated mutations and applied to a large cancer ge...

A Convolutional Neural Network Uses Microscopic Images to Differentiate between Mouse and Human Cell Lines and Their Radioresistant Clones.

Cancer research
: Artificial intelligence (AI) trained with a convolutional neural network (CNN) is a recent technological advancement. Previously, several attempts have been made to train AI using medical images for clinical applications. However, whether AI can di...

Organization of the cancer network in SUS: evolution of the care model.

Clinics (Sao Paulo, Brazil)
In the current context of epidemiological transition, demographic changes, changes in consumption and lifestyle habits, and pressure on care costs and organized health systems for acute conditions, the Integrated Care Model by Shortell has become a c...

G2Vec: Distributed gene representations for identification of cancer prognostic genes.

Scientific reports
Identification of cancer prognostic genes is important in that it can lead to accurate outcome prediction and better therapeutic trials for cancer patients. Many computational approaches have been proposed to achieve this goal; however, there is room...

Identification of synthetic lethality based on a functional network by using machine learning algorithms.

Journal of cellular biochemistry
Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 ge...

Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks.

BMC genomics
BACKGROUND: With the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opportunities for advanced association studies between somatic mutations and cancer types/subtypes which f...

Boosting support vector machines for cancer discrimination tasks.

Computers in biology and medicine
Cancer is a complex disease that is caused by rapid alteration of genes. Prediction of the state of cancer in advance contributes to a better understanding of its mechanism and improves the cancer therapy process. For example, predicting the malignan...