Meghana Kshirsagar

Molecular Biology · 2013–2023

A researcher in the Science corpus: 4 papers; trusts 23 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 67 citations into it; #5,903 of 17,773 by trust here, #15,203 by citations
• Computational Biology & Bioinformatics — 18 citations into it
• Materials Chemistry — 18 citations into it
• Artificial Intelligence — 15 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Spectroscopy — 10 citations into it
• Computational Mechanics — 8 citations into it
• Information Systems — 6 citations into it

Whom Meghana Kshirsagar trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Qian Xu
2. Theodoros Evgeniou
3. Ajit Paul Singh
4. Yanjun Qi
5. Alan L. Yuille
6. Chun-Nam John Yu
7. E.J. Candes
8. Jesse Davis
9. Nagarajan Natarajan
10. Rong-En Fan

Who trusts Meghana Kshirsagar overall

1. Yu Zhang
2. Nagarajan Natarajan
3. Qiang Yang
4. Keerthiram Murugesan
5. Jaime G. Carbonell
6. Inderjit S. Dhillon

Papers from Meghana Kshirsagar's perspective

Ordered by how strongly the researchers Meghana Kshirsagar trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Et al
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Multitask learning for host–pathogen protein interactions (2013) — 83 citations
• Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network (2022) — 69 citations
• Multitask Matrix Completion for Learning Protein Interactions Across Diseases (2016) — 13 citations
• Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network (2023) — 109 citations