AIMC Topic: Drug Resistance, Neoplasm
Clear Filters Showing 61 to 70 of 94 articles
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.
Nature communications
Jan 3, 2018
Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatm...
A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia.
Haematologica
Dec 14, 2017
Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856...
From word models to executable models of signaling networks using automated assembly.
Molecular systems biology
Nov 24, 2017
Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to b...
Humans
Neural Networks, Computer
Proto-Oncogene Proteins B-raf
Vemurafenib
Signal Transduction
Natural Language Processing
DNA Damage
Antineoplastic Agents
Sulfonamides
Cell Line, Tumor
Computer Simulation
Indoles
Drug Resistance, Neoplasm
Gene Expression Regulation, Neoplastic
Enzyme Inhibitors
Language
Tumor Suppressor Protein p53
Models, Genetic
Melanoma
Proto-Oncogene Proteins p21(ras)
Skin Neoplasms
Simultaneous delivery of anti-miR21 with doxorubicin prodrug by mimetic lipoprotein nanoparticles for synergistic effect against drug resistance in cancer cells.
International journal of nanomedicine
Dec 30, 2016
The development of drug resistance in cancer cells is one of the major obstacles to achieving effective chemotherapy. We hypothesized that the combination of a doxorubicin (Dox) prodrug and microRNA (miR)21 inhibitor might show synergistic antitumor ...
Prediction of anti-cancer drug response by kernelized multi-task learning.
Artificial intelligence in medicine
Oct 3, 2016
MOTIVATION: Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer, the success of the therapeutic agents differs among patients. In this sense, determination of chem...
Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning.
Molecular oncology
Aug 22, 2015
Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI5...
Novel reliable model by integrating the discrete wavelet transform with fuzzy intelligent systems for the simultaneous spectrophotometric determination of anticancer drug and anti-acquired resistance drug in biological samples.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Nov 15, 2025
Simultaneous measurement of drugs used to treat cancer and medications prescribed to overcome resistance to these drugs is important in pharmaceutical formulations and biological samples. In this study, a spectrophotometric method with a hybrid of di...
Epigenetic Heritability of Cell Plasticity Drives Cancer Drug Resistance through a One-to-Many Genotype-to-Phenotype Paradigm.
Cancer research
Aug 1, 2025
UNLABELLED: Cancer drug resistance is multifactorial, driven by heritable (epi)genetic changes but also by phenotypic plasticity. In this study, we dissected the drivers of resistance by perturbing organoids derived from patients with colorectal canc...
The Construction of a New Prognostic Model of Breast Cancer and the Exploration of Drug Sensitivity Based on Machine Learning for Glycosylation-Related Genes.
Clinical breast cancer
Aug 1, 2025
AIMS: Breast cancer has become the number 1 killer threatening women's health. In recent years, glycosylation modification has played an increasingly important role in tumor progression. The aim of this study was to explore the key genes that may be ...
A Machine Learning-Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia.
Cancer research
Jul 15, 2025
UNLABELLED: Combination therapies are one potential approach to improve the outcomes of patients with relapsed/refractory (R/R) disease. However, comprehensive testing in scarce primary patient material is hampered by the many drug combination possib...
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