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Proto-Oncogene Proteins p21(ras)

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A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI-based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear...

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

Proceedings of the National Academy of Sciences of the United States of America
RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-at...

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.

International journal of molecular sciences
Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machin...

Radiogenomic and Deep Learning Network Approaches to Predict Mutation from Radiotherapy Plan CT.

Anticancer research
BACKGROUND/AIM: We aimed to investigate the role of radiogenomic and deep learning approaches in predicting the KRAS mutation status of a tumor using radiotherapy planning computed tomography (CT) images in patients with locally advanced rectal cance...

Artificial Intelligence-Assisted Amphiregulin and Epiregulin IHC Predicts Panitumumab Benefit in Wild-Type Metastatic Colorectal Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: High tumor mRNA levels of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are associated with anti-EGFR agent response in metastatic colorectal cancer (mCRC). However, ligand RNA assays have not been adopted into routine practice ...

Bringing Structural Implications and Deep Learning-Based Drug Identification for Mutants.

Journal of chemical information and modeling
Colorectal cancer is considered one of the leading causes of death that is linked with the Kirsten Rat Sarcoma () harboring codons 13 and 61 mutations. The objective for this study is to search for clinically important codon 61 mutations and analyze ...

Deep Learning Features Improve the Performance of a Radiomics Signature for Predicting KRAS Status in Patients with Colorectal Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: We assess the performance of a model combining a deep convolutional neural network and a hand-crafted radiomics signature for predicting KRAS status in patients with colorectal cancer (CRC).

Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet.

Biochemical and biophysical research communications
We propose an image based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wrinkles when cells grab the substrate and contract, and the wrinkles can be used to visualize the for...

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

Molecular imaging and biology
PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the...