… against a panel of genomically heterogeneous cancer cell lines have unveiled multiple …
, we developed machine learning models to predict the response of cancer cell lines to drug …

… the cancerous breast cells. Accurate discovery of this type of cancer cells is essential in its
early stages, which can be attained via. various data mining and machine learning techniques…

… cancer cells and four types of blood cells. For cancer cells, we used a pair of isogenic cancer
cell lines: colorectal adenocarcinoma cells, SW-480 (ATCC CCL-228), and metastatic from …

… In this study, machine learning methods (eg, deep learning) … the effectiveness of drugs in
cancer cell lines. The methodology … basis, can identify sensitive cancer cells with an average …

… application of machine learning trained on optical phase features of epithelial and mesenchymal
cells to grade cancer cells’ morphologies, relevant to evaluation of cancer phenotype in …

… In this study, we collected datasets of phenotypic compound-cell association bioactivity
toward 13 breast cancer cell lines and one normal breast cell line and constructed 588 models …

… cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells)
using machine-learning-… on machine learning methods proved capable of presenting the …

… Also, it’s very challenging to differentiate cancer cell from normal cell as they look similar in
… cancer cell detection by extracting important features from the blood cell images and learning …

… In this paper, the aim of study was to gene regulation with single cell interaction using
advance machine learning based SVM classifier with data augmentation strategies were …

… on cancer cells, would lead to quantitative understanding of processes relevant to cancer …
phase maps of invasive breast cancer cells reflect cell morphological and cytoskeletal features, …