Nigam H. Shah

Molecular Biology · 2007–2020

A researcher in the Science corpus: 21 papers; trusts 8 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 123 citations into it; #12,843 of 17,773 by trust here, #3,891 by citations
• Artificial Intelligence — 79 citations into it; #29,309 of 35,345 by trust here, #4,577 by citations
• Statistics and Probability — 39 citations into it; #11,124 of 12,932 by trust here, #4,371 by citations
• Causal Inference & Discovery — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Nigam H. Shah trusts overall

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

1. Alan R. Aronson
2. Peter C. Austin
3. Donald B. Rubin
4. Olivier Bodenreider

Who trusts Nigam H. Shah overall

1. Olivier Bodenreider
2. Danton S. Char

Papers from Nigam H. Shah's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. The central role of the propensity score in observational studies for causal effects
3. Random Forests
4. Long Short-Term Memory
5. Estimating causal effects of treatments in randomized and nonrandomized studies.
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. Greedy function approximation: A gradient boosting machine.
8. From genomics to chemical genomics: new developments in KEGG (Nucleic Acids Res.)
9. An ontology for cell types (Genome Biology)
10. Representation Learning: A Review and New Perspectives

Their papers

• Scalable and accurate deep learning with electronic health records (2018) — 2,800 citations
• The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration (2007) — 2,805 citations
• Some methods for heterogeneous treatment effect estimation in high dimensions (2017) — 244 citations
• BioPAX – A community standard for pathway data sharing (2010) — 695 citations
• BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications (2011) — 813 citations
• Harnessing next-generation informatics for personalizing medicine: a report from AMIA's 2014 Health Policy Invitational Meeting (2016) — 7 citations
• Biomedical ontologies: a functional perspective (2007) — 319 citations
• BioPortal: ontologies and integrated data resources at the click of a mouse (2009) — 1,011 citations
• The digital revolution in phenotyping (2015) — 59 citations
• Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions (2013) — 171 citations
• Novel Data Mining Methodologies for Adverse Drug Event Discovery and Analysis (2012) — 395 citations
• Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records. (2013) — 59 citations
• Rates of Co-infection Between SARS-CoV-2 and Other Respiratory Pathogens. (2020) — 732 citations
• MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care (2020) — 333 citations
• Making Machine Learning Models Clinically Useful. (2019) — 250 citations
• What This Computer Needs Is a Physician: Humanism and Artificial Intelligence. (2018) — 396 citations
• Implementing Machine Learning in Health Care — Addressing Ethical Challenges (2018) — 1,243 citations
• Improving palliative care with deep learning (2017) — 410 citations
• Bringing cohort studies to the bedside: framework for a 'green button' to support clinical decision-making. (2015) — 48 citations
• Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers (2015) — 1,262 citations