Deep learning in cancer diagnosis, prognosis and treatment selection
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …
technique called artificial neural networks to extract patterns and make predictions from …
Multimodal machine learning in precision health: A scoping review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
Single-cell and spatial analysis reveal interaction of FAP+ fibroblasts and SPP1+ macrophages in colorectal cancer
Colorectal cancer (CRC) is among the most common malignancies with limited treatments
other than surgery. The tumor microenvironment (TME) profiling enables the discovery of …
other than surgery. The tumor microenvironment (TME) profiling enables the discovery of …
Towards a general-purpose foundation model for computational pathology
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …
requiring the objective characterization of histopathological entities from whole-slide images …
oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data
D Maeser, RF Gruener, RS Huang - Briefings in bioinformatics, 2021 - academic.oup.com
Cell line drug screening datasets can be utilized for a range of different drug discovery
applications from drug biomarker discovery to building translational models of drug …
applications from drug biomarker discovery to building translational models of drug …
GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels
Cancer initiation and progression are likely caused by the dysregulation of biological
pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify …
pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify …
TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment
Abstract The Tumor Immune Single Cell Hub 2 (TISCH2) is a resource of single-cell RNA-
seq (scRNA-seq) data from human and mouse tumors, which enables comprehensive …
seq (scRNA-seq) data from human and mouse tumors, which enables comprehensive …
[PDF][PDF] Evaluation of cell-free DNA approaches for multi-cancer early detection
Summary In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we
evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer …
evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer …
Single-cell analyses define a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer
WR Becker, SA Nevins, DC Chen, R Chiu… - Nature …, 2022 - nature.com
To chart cell composition and cell state changes that occur during the transformation of
healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated single …
healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated single …
Federated learning and differential privacy for medical image analysis
The artificial intelligence revolution has been spurred forward by the availability of large-
scale datasets. In contrast, the paucity of large-scale medical datasets hinders the …
scale datasets. In contrast, the paucity of large-scale medical datasets hinders the …