AIMC Topic: Drug Resistance, Neoplasm
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Machine Learning Developed a MYC Expression Feature-Based Signature for Predicting Prognosis and Chemoresistance in Pancreatic Adenocarcinoma.
Biochemical genetics
Jan 21, 2024
MYC has been identified to profoundly influence a wide range of pathologic processes in cancers. However, the prognostic value of MYC-related genes in pancreatic adenocarcinoma (PAAD) remains unclarified. Gene expression data and clinical information...
T6496 targeting EGFR mediated by T790M or C797S mutant: machine learning, virtual screening and bioactivity evaluation study.
Journal of biomolecular structure & dynamics
Jan 4, 2024
Acquired resistance to EGFR is a major impediment in lung cancer treatment, highlighting the urgent need to discover novel compounds to overcome EGFR drug resistance. In this study, we utilized in silico methods and bioactivity evaluation for drug di...
Noninvasive Computed Tomography-Based Deep Learning Model Predicts In Vitro Chemosensitivity Assay Results in Pancreatic Cancer.
Pancreas
Nov 24, 2023
OBJECTIVES: We aimed to predict in vitro chemosensitivity assay results from computed tomography (CT) images by applying deep learning (DL) to optimize chemotherapy for pancreatic ductal adenocarcinoma (PDAC).
Biologically informed deep neural network for prostate cancer discovery.
Nature
Sep 22, 2021
The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge. Recent advances in interpretability of machine learning models as applied to biomedical proble...
Predicting drug sensitivity of cancer cells based on DNA methylation levels.
PloS one
Sep 10, 2021
Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular featur...
A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer.
Scientific reports
Aug 19, 2021
Ovarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to identify a set of molecular markers for predicting platinum sensitivity. A signature predicting cisplat...
A computational method for drug sensitivity prediction of cancer cell lines based on various molecular information.
PloS one
Apr 29, 2021
Determining sensitive drugs for a patient is one of the most critical problems in precision medicine. Using genomic profiles of the tumor and drug information can help in tailoring the most efficient treatment for a patient. In this paper, we propose...
DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 6, 2021
High-throughput screening technologies have provided a large amount of drug sensitivity data for a panel of cancer cell lines and hundreds of compounds. Computational approaches to analyzing these data can benefit anticancer therapeutics by identifyi...
Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.
Clinical cancer research : an official journal of the American Association for Cancer Research
Nov 18, 2020
PURPOSE: Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI respon...
Follow-Up Studies
Skin Neoplasms
Neoplasm Staging
Risk Assessment
Humans
Disease Progression
Prognosis
Aged
Drug Resistance, Neoplasm
Machine Learning
ROC Curve
Male
Female
Adult
Skin
Prospective Studies
Immune Checkpoint Inhibitors
Middle Aged
Melanoma
Progression-Free Survival
Image Processing, Computer-Assisted
Prediction of cancer dependencies from expression data using deep learning.
Molecular omics
Nov 2, 2020
Detecting cancer dependencies is key to disease treatment. Recent efforts have mapped gene dependencies and drug sensitivities in hundreds of cancer cell lines. These data allow us to learn for the first time models of tumor vulnerabilities and apply...
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