Neel S. Madhukar
Molecular Biology · 2016–2021
A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 36 citations into it
- Computational Theory and Mathematics — 31 citations into it
- Artificial Intelligence — 22 citations into it
- Oncology — 20 citations into it
- Radiology, Nuclear Medicine and Imaging — 16 citations into it
- Computer Vision and Pattern Recognition — 6 citations into it
- Materials Chemistry — 6 citations into it
- Genetics — 5 citations into it
Whom Neel S. Madhukar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Gerd Kempermann
- Gábor Csárdi
- Jishnu Das
- Michael Kuhn
- Simon Anders
- Christopher A. Lipinski
- Trevor J. Hastie
- Nicola De Cao
- Anika Cheerla
Who trusts Neel S. Madhukar overall
Papers from Neel S. Madhukar's perspective
Ordered by how strongly the researchers Neel S. Madhukar trusts endorse each paper. Same corpus, a different order for every researcher.
- Random Forests
- HINT: High-quality protein interactomes and their applications in understanding human disease
- DrugBank 4.0: shedding new light on drug metabolism
- Dermatologist-level classification of skin cancer with deep neural networks
- Quantifying the chemical beauty of drugs.
- Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.
- Molecular de-novo design through deep reinforcement learning
- HTSeq—a Python framework to work with high-throughput sequencing data (bioRxiv)
- MolGAN: An implicit generative model for small molecular graphs
- A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases.
Their papers
- A data-driven approach to predicting successes and failures of clinical trials (2016) — 300 citations
- Artificial Intelligence in Cancer Research and Precision Medicine (2021) — 516 citations
- A Bayesian machine learning approach for drug target identification using diverse data types (2019) — 197 citations