Deep learning in biomedicine

By Michael Wainberg, Daniele Merico, Andrew Delong, Brendan J. Frey. 2018

521 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xin Gao
2. Chao Huang
3. Yu Li
4. Huajun Chen
5. Lizhong Ding
6. Jianzhu Ma
7. Qiyang Hu
8. Zhongxiao Li

In Biophysics

1. Jianzhu Ma

Citations in the corpus

• Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells (2020, background): “…include disease diagnosis from biomedical images and interpretation of electronic medical records (Esteva et al., 2019; Rajkomar et al., 2019; Wainberg et al., 2018), machine learning models are also of high interest in predicting drug responses (Barretina et al., 2012; Costello et al.,…”
• Addressing bias in big data and AI for health care: A call for open science (2021, background): “4 In one of many examples, in the field of dermatology, convolutional neural networks (CNNs) are able to classify images of skin lesions as accurately as trained dermatologists.”
• A white-box machine learning approach for revealing antibiotic mechanisms of action (2019, unknown): “Therefore, there is a need to develop biological discovery approaches that integrate biochemical screens with network modeling and advanced data analysis techniques to enhance our understanding of complex drug mechanisms (Camacho et al., 2018; Wainberg et al., 2018; Xie et al., 2017).”
• Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey (2019, background): “It has been a long time since they are used in finance (Trippi and Turban 1992), physics (Duch and Diercksen 1994) or biomedicine (Wainberg et al. 2018).”
• Deep learning in cancer diagnosis, prognosis and treatment selection (2021, background): “Excitingly, DL models have the potential to harness this complexity to provide meaningful insights and identify relevant granular features from multiple data types [4, 5].”
• Fast and flexible design of novel proteins using graph neural networks (2019, methodology): “Such methods have been applied to a vast diversity of fields with impressive results ( 12 – 14 ), partly because they can infer hitherto hidden patterns from sufficiently large training sets.”
• scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network (2021, methodology): “Given the state-of-the-art accuracy deep learning has achieved in numerous prediction tasks, it has been increasingly used in biological research (22) and e122 Nucleic Acids Research, 2021, Vol. 49, No. 21 P AGE 2 OF 13 biomedical applications (23,24).”
• Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, methodology): “[65] Protein sequences A tutorial-style review of using deep learning to handle sequence data, with three application examples [18] Biomedicine, Human disease It provides an review of the deep learning applications in biomedicine, with comprehensive discussion of the obstacles when using deep learni”