AIMC Topic: Drug Resistance, Neoplasm

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Prediction of anti-cancer drug response by kernelized multi-task learning.

Artificial intelligence in medicine
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
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
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...

Spatial discovery of pyrotinib overcoming HER2-positive breast cancer resistance by breaking fibroblast-induced immune barriers.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy
BACKGROUND: The TCbHP regimen, consisting of combining docetaxel (T), carboplatin (Cb), trastuzumab (H), and pertuzumab (P), is the preferred neoadjuvant treatment for locally advanced human epidermal growth factor 2 (HER2)-positive breast cancer. Ho...

Epigenetic Heritability of Cell Plasticity Drives Cancer Drug Resistance through a One-to-Many Genotype-to-Phenotype Paradigm.

Cancer research
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
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
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...

AGCLNDA: Enhancing the Prediction of ncRNA-Drug Resistance Association Using Adaptive Graph Contrastive Learning.

IEEE journal of biomedical and health informatics
Non-coding RNAs (ncRNAs), which do not encode proteins, have been implicated in chemotherapy resistance in cancer treatment. Given the high costs and time requirements of traditional biological experiments, there is an increasing need for computation...

Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer.

Scientific reports
Platinum-based therapy is an integral part of the standard treatment for ovarian cancer. However, despite extensive research spanning several decades, the identification of dependable predictive biomarkers for platinum response in clinical practice h...