High-accuracy prostate cancer pathology using deep learning
Deep learning (DL) is a powerful methodology for the recognition and classification of tissue
structures in digital pathology. Its performance in prostate cancer pathology is still under …
structures in digital pathology. Its performance in prostate cancer pathology is still under …
Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer
H Chereda, A Bleckmann, K Menck, J Perera-Bel… - Genome medicine, 2021 - Springer
Background Contemporary deep learning approaches show cutting-edge performance in a
variety of complex prediction tasks. Nonetheless, the application of deep learning in …
variety of complex prediction tasks. Nonetheless, the application of deep learning in …
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Visual inspection of histopathology slides is one of the main methods used by pathologists
to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and …
to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and …
Predicting cancer outcomes from histology and genomics using convolutional networks
Cancer histology reflects underlying molecular processes and disease progression and
contains rich phenotypic information that is predictive of patient outcomes. In this study, we …
contains rich phenotypic information that is predictive of patient outcomes. In this study, we …
Transfer learning enables predictions in network biology
Mapping gene networks requires large amounts of transcriptomic data to learn the
connections between genes, which impedes discoveries in settings with limited data …
connections between genes, which impedes discoveries in settings with limited data …
Phenotype-driven precision oncology as a guide for clinical decisions one patient at a time
S Chia, JL Low, X Zhang, XL Kwang, FT Chong… - Nature …, 2017 - nature.com
Genomics-driven cancer therapeutics has gained prominence in personalized cancer
treatment. However, its utility in indications lacking biomarker-driven treatment strategies …
treatment. However, its utility in indications lacking biomarker-driven treatment strategies …
Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data
N Fortelny, C Bock - Genome biology, 2020 - Springer
Background Deep learning has emerged as a versatile approach for predicting complex
biological phenomena. However, its utility for biological discovery has so far been limited …
biological phenomena. However, its utility for biological discovery has so far been limited …
Gene expression based inference of cancer drug sensitivity
S Chawla, A Rockstroh, M Lehman, E Ratther… - Nature …, 2022 - nature.com
Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer
and are responsible for imparting differential drug responses in cancer patients. Recently …
and are responsible for imparting differential drug responses in cancer patients. Recently …
Genome-wide germline correlates of the epigenetic landscape of prostate cancer
Oncogenesis is driven by germline, environmental and stochastic factors. It is unknown how
these interact to produce the molecular phenotypes of tumors. We therefore quantified the …
these interact to produce the molecular phenotypes of tumors. We therefore quantified the …
Artificial intelligence in cancer research, diagnosis and therapy
Artificial intelligence and machine learning techniques are breaking into biomedical
research and health care, which importantly includes cancer research and oncology, where …
research and health care, which importantly includes cancer research and oncology, where …