Opportunities and obstacles for deep learning in biology and medicine
T Ching, DS Himmelstein… - Journal of the …, 2018 - royalsocietypublishing.org
Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …
combining raw inputs into layers of intermediate features. These algorithms have recently …
[HTML][HTML] Machine learning in healthcare
H Habehh, S Gohel - Current genomics, 2021 - ncbi.nlm.nih.gov
Abstract Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML)
technology have brought on substantial strides in predicting and identifying health …
technology have brought on substantial strides in predicting and identifying health …
Application of artificial intelligence technology in oncology: Towards the establishment of precision medicine
Simple Summary Artificial intelligence (AI) technology has been advancing rapidly in recent
years and is being implemented in society. The medical field is no exception, and the clinical …
years and is being implemented in society. The medical field is no exception, and the clinical …
Parameter prediction for unseen deep architectures
Deep learning has been successful in automating the design of features in machine learning
pipelines. However, the algorithms optimizing neural network parameters remain largely …
pipelines. However, the algorithms optimizing neural network parameters remain largely …
A survey on active deep learning: from model driven to data driven
Which samples should be labelled in a large dataset is one of the most important problems
for the training of deep learning. So far, a variety of active sample selection strategies related …
for the training of deep learning. So far, a variety of active sample selection strategies related …
Harnessing deep learning for population genetic inference
In population genetics, the emergence of large-scale genomic data for various species and
populations has provided new opportunities to understand the evolutionary forces that drive …
populations has provided new opportunities to understand the evolutionary forces that drive …
Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research
Antimicrobial resistance (AMR) remains one of the most challenging phenomena of modern
medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the …
medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the …
Machine learning for lung cancer diagnosis, treatment, and prognosis
The recent development of imaging and sequencing technologies enables systematic
advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in …
advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in …
Epigenetics analysis and integrated analysis of multiomics data, including epigenetic data, using artificial intelligence in the era of precision medicine
To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations
have been actively conducted for a long time, and a large number of achievements have …
have been actively conducted for a long time, and a large number of achievements have …
High dimensional, tabular deep learning with an auxiliary knowledge graph
Abstract Machine learning models exhibit strong performance on datasets with abundant
labeled samples. However, for tabular datasets with extremely high $ d $-dimensional …
labeled samples. However, for tabular datasets with extremely high $ d $-dimensional …