Jeremy Goecks
Molecular Biology · 2010–2024
A researcher in the Science corpus: 8 papers; trusts 5 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 95 citations into it
- Artificial Intelligence — 30 citations into it
- Biophysics — 17 citations into it
- Information Systems — 14 citations into it; #1,866 of 8,771 by trust here, #567 by citations
- Biomedical Engineering — 7 citations into it
Whom Jeremy Goecks trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Friedrich Leisch
- Jill P. Mesirov
- E. Zdobnov
- Peter M. Rice
- Alvin Rajkomar
- Stephen F. Altschul
- Qiang Yang
- Jia Deng
- Benjamin Shickel
- Andre Esteva
Who trusts Jeremy Goecks overall
- Christoph Bock
- Abhishek Niroula
- Sangtae Kim
- Johannes Köster
- Robert Rein
- Friedrich Fauser
- Fidel Ramírez
- Ji Liu
- Ritambhara Singh
- Geir Kjetil Ferkingstad Sandve
Papers from Jeremy Goecks's perspective
Ordered by how strongly the researchers Jeremy Goecks trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- ImageNet: A large-scale hierarchical image database
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Going deeper with convolutions
- Learning Multiple Layers of Features from Tiny Images
Their papers
- The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update (2018) — 1,614 citations
- The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update (2020) — 637 citations
- The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update (2024) — 740 citations
- The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update (2022) — 622 citations
- How Machine Learning will Transform Biomedicine (2020) — 480 citations
- The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution (2020) — 482 citations
- The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update (2016) — 3,541 citations
- Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences (2010) — 3,703 citations