Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Branson, Kim"'
Autor:
Chauhan, Vinod Kumar, Clifton, Lei, Salaün, Achille, Lu, Huiqi Yvonne, Branson, Kim, Schwab, Patrick, Nigam, Gaurav, Clifton, David A.
While machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited. One critical factor contributing to this restraint is sample selection bias (SSB) which refers to the study population being less repre
Externí odkaz:
http://arxiv.org/abs/2405.07841
Sequence modelling approaches for epigenetic profile prediction have recently expanded in terms of sequence length, model size, and profile diversity. However, current models cannot infer on many experimentally feasible tissue and assay pairs due to
Externí odkaz:
http://arxiv.org/abs/2308.11671
Image-based profiling techniques have become increasingly popular over the past decade for their applications in target identification, mechanism-of-action inference, and assay development. These techniques have generated large datasets of cellular m
Externí odkaz:
http://arxiv.org/abs/2305.09790
Autor:
Elias, Pierre, Damle, Ash, Casale, Michael, Branson, Kim, Peterson, Nick, Churi, Chaitanya, Komatireddy, Ravi, Feramisco, Jamison
Publikováno v:
JMIR Medical Informatics, Vol 3, Iss 2, p e23 (2015)
BackgroundWe evaluated the concordance between triage scores generated by a novel Internet clinical decision support tool, Clinical GPS (cGPS) (Lumiata Inc, San Mateo, CA), and the Emergency Severity Index (ESI), a well-established and clinically val
Externí odkaz:
https://doaj.org/article/d966630e0da149859cdf700cdadd3390
Autor:
Slade, Emma, Branson, Kim M.
High accuracy medical image classification can be limited by the costs of acquiring more data as well as the time and expertise needed to label existing images. In this paper, we apply active learning to medical image classification, a method which a
Externí odkaz:
http://arxiv.org/abs/2206.13391
Large scale self-supervised pre-training of Transformer language models has advanced the field of Natural Language Processing and shown promise in cross-application to the biological `languages' of proteins and DNA. Learning effective representations
Externí odkaz:
http://arxiv.org/abs/2112.07571
"Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption characteristics?" In this work, we propose a "stain-aware" machine learning
Externí odkaz:
http://arxiv.org/abs/2108.10446
Cellular composition prediction, i.e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H&E) stained tissue section can be used for various tasks in comput
Externí odkaz:
http://arxiv.org/abs/2108.08306
The discovery of structure from time series data is a key problem in fields of study working with complex systems. Most identifiability results and learning algorithms assume the underlying dynamics to be discrete in time. Comparatively few, in contr
Externí odkaz:
http://arxiv.org/abs/2105.02522
Building in silico models to predict chemical properties and activities is a crucial step in drug discovery. However, limited labeled data often hinders the application of deep learning in this setting. Meanwhile advances in meta-learning have enable
Externí odkaz:
http://arxiv.org/abs/2003.05996