AIMC Topic: Cell Line, Tumor

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Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures.

Analytical chemistry
The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analys...

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.

Journal of chemical information and modeling
Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale dr...

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.

Cell reports methods
Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest...

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning.

BMC bioinformatics
BACKGROUND: The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretabili...

Biomimetic piezoelectric nanomaterial-modified oral microrobots for targeted catalytic and immunotherapy of colorectal cancer.

Science advances
Lactic acid (LA) accumulation in the tumor microenvironment poses notable challenges to effective tumor immunotherapy. Here, an intelligent tumor treatment microrobot based on the unique physiological structure and metabolic characteristics of (VA) ...

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.

PLoS computational biology
Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Ye...

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations.

Journal of chemical information and modeling
Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, becaus...

Development and validation of AI/ML derived splice-switching oligonucleotides.

Molecular systems biology
Splice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identificat...

Deep learning-enabled detection of rare circulating tumor cell clusters in whole blood using label-free, flow cytometry.

Lab on a chip
Metastatic tumors have poor prognoses for progression-free and overall survival for all cancer patients. Rare circulating tumor cells (CTCs) and rarer circulating tumor cell clusters (CTCCs) are potential biomarkers of metastatic growth, with CTCCs r...

DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction.

PLoS computational biology
Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wid...