AIMC Topic: Cell Line, Tumor
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Learning deep features for dead and living breast cancer cell classification without staining.
Scientific reports
May 13, 2021
Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer ce...
Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.
Nature communications
May 11, 2021
Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learnin...
Regression plane concept for analysing continuous cellular processes with machine learning.
Nature communications
May 5, 2021
Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool e...
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...
Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.
PLoS computational biology
Apr 16, 2021
Advancements in sequencing have led to the proliferation of multi-omic profiles of human cells under different conditions and perturbations. In addition, many databases have amassed information about pathways and gene "signatures"-patterns of gene ex...
Democratising deep learning for microscopy with ZeroCostDL4Mic.
Nature communications
Apr 15, 2021
Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources ...
Magnetic tri-bead microrobot assisted near-infrared triggered combined photothermal and chemotherapy of cancer cells.
Scientific reports
Apr 12, 2021
Magnetic micro/nanorobots attracted much attention in biomedical fields because of their precise movement, manipulation, and targeting abilities. However, there is a lack of research on intelligent micro/nanorobots with stimuli-responsive drug delive...
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...
Machine learning-based investigation of the cancer protein secretory pathway.
PLoS computational biology
Apr 5, 2021
Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is ofte...
Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.
Nature communications
Mar 25, 2021
Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which use...
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