Deep learning for biology

By Sarah Webb. Nature, 2018

329 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5e39ec7bb7fbf77fc15224ee0dc71bb4f2c44174

How researchers see this paper

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

In Biophysics

1. Malte D. Luecken
2. Fabian J Theis
3. Sebastian Raisch
4. Sebastian Krakowski

In Molecular Biology

1. Malte D. Luecken
2. Fabian J Theis
3. Roi Reichart
4. Sebastian Raisch
5. Sebastian Krakowski
6. Sunipa Dev

Citations in the corpus

• A white-box machine learning approach for revealing antibiotic mechanisms of action (2019, unknown): “DISCUSSION Recent advances in high-throughput experimental technologies and data science have stimulated considerable interest in the potential for artificial intelligence to transform biological discovery and healthcare (Gil et al., 2014; Topol, 2019; Webb, 2018; Yu et al., 2018a).”
• Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, background): “Due to its flexibility to scale to large data, deep learning has revolutionized fields from computer vision to natural language processing, and is starting to have a strong impact in genomics (Webb, 2018).”
• Artificial Intelligence and Management: The Automation–Augmentation Paradox (2020, background): “Accordingly, researchers from disciplines such as astronomy (Haiman, 2019), biology (Webb, 2018), law (Corrales, Fenwick, & Forgo ́ , 2018), medicine (Topol, 2019), politics (Helbing et al., 2019), psychology (Jaeger, 2016), and sociology (McFarland, Lewis, & Goldberg, 2016) have addressed and…”
• Data-centric Artificial Intelligence: A Survey (2023, unknown): “…in Artificial Intelligence (AI) across various directions, such as natural language processing [45] and computer vision [224], which has also made a profound impact on nearly every other domain, spanning recommender system [267], healthcare [150], biology [237], finance [164], and so forth.”
• Annotation-efficient deep learning for automatic medical image segmentation (2020, background): “Nevertheless, the applicability of deep learning methods to clinical practice is limited because of the heavy reliance on training data, especially training annotations 14,15 .”
• The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention (2021, background): “In fact, the DNN revolution had a substantial impact on a variety of ML-related fields, including, for example, computer vision (Szegedy et al., 2016) and bioinformatics (Webb, 2018).”