astronomy

Melissa Haendel

Melissa Haendel is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Melissa Haendel rather than just read about it. In short: Melissa Anne Haendel is an American bioinformaticist who is the Sarah Graham Kenan Distinguished Professor at the UNC School of Medicine. She is also the Director of Precision Health & Translational Informatics, deputy director of Computational Science at The North Carolina Translational and Clinical Sciences Institute.

Key takeaways

Melissa Haendel belongs to astronomy; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Melissa Haendel to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Melissa Haendel from memory before moving on to harder problems.

Reference excerpt

Melissa Anne Haendel is an American bioinformaticist who is the Sarah Graham Kenan Distinguished Professor at the UNC School of Medicine. She is also the Director of Precision Health & Translational Informatics, deputy director of Computational Science at The North Carolina Translational and Clinical Sciences Institute. She serves as Director of the Center for Data to Health (CD2H). Her research makes use of data to improve the discovery and diagnosis of diseases. During the COVID-19 pandemic, Haendel joined with the National Institutes of Health to launch the National COVID Cohort Collaborative (N3C), which looks to identify the risk factors that can predict severity of disease outcome and help to identify treatments.

Early life and education Haendel earned her undergraduate degree in chemistry at Reed College. Her undergraduate dissertation looked at designing pharmaceuticals using molecular electrostatic potentials (MEPs) to construct quantitative structure-activity relationships. She moved to the University of Wisconsin–Madison for her graduate studies, where she used in vitro gene trapping to study the gene axotrophin. Her early career focussed on genetics and molecular biology. In 2000 she moved to the University of Oregon as a postdoctoral researcher studying the role of thyroid hormones in the neural development of zebrafish.

Research and career

Haendel started working in healthcare informatics in 2004. She switched the focus of her research from neuroscience and the biology of zebrafish to the development of resources for the Oregon Health & Science University library. She was promoted to associate professor of Medical Informatics in 2015. Haendel's research considers ontology development, biocuration and data harmonization. Biocuration assembles information from patient records, research outputs and medical literature to create a quality-controlled, computable format. She was previously the Director of Translational Data Science at the Linus Pauling Institute. She previously held the position of Chief Research Informatics Officer at the University of Colorado Anschutz Medical Campus. Haendel believes that a globally consistent set of criteria, more comprehensive data collection, sharing and analysis will help to diagnose rare diseases. Rare diseases are thought to impact 10% of the global population, meaning that there are considerable numbers of patients who are underserved by their healthcare systems. In 2019, Haendel and the CD2H were awarded almost $9 million to make data related to cancer research more centralised and organised. The Center for Cancer Data Harmonization makes use of a cloud-based portal to share data between physicians and cancer researchers across the country. During the COVID-19 pandemic, the United States had no standardised means to collect and share clinical data. Haendel was concerned that the number of deaths and infections were not being accurately counted, and that this might compromise safe reopening. In June 2020, Haendel formed the National COVID Cohort Collaborative (N3C), which collates and analyses the medical record data of people with coronavirus disease. The N3C looks to identify the risk factors that can predict severity of coronavirus disease and help to identify potential treatments. The collaborative has work streams in data partnership, phenotypes, collaborative analytics, data harmonisation and data synthesis.

Selected publications Mungall, Christopher J; Torniai, Carlo; Gkoutos, Georgios V; Lewis, Suzanna E; Haendel, Melissa A (2012). "Uberon, an integrative multi-species anatomy ontology". Genome Biology. 13 (1): R5. doi:10.1186/gb-2012-13-1-r5. ISSN 1465-6906. PMC 3334586. PMID 22293552. S2CID 15453742. Day-Richter, J.; Harris, M. A.; Haendel, M.; Lewis, S. (2007-06-01). "OBO-Edit an ontology editor for biologists". Bioinformatics. 23 (16): 2198–2200. doi:10.1093/bioinformatics/btm112. ISSN 1367-4803. PMID 17545183. Shimoyama, Mary; Dwinell, Melinda; Jacob, Howard (2009-08-05). "Multiple Ontologies for Integrating Complex Phenotype Datasets". Nature Precedings. doi:10.1038/npre.2009.3554 (inactive 18 July 2025). ISSN 1756-0357.{{cite journal}}: CS1 maint: DOI inactive as of July 2025 (link)

References

Illustrations

Worked examples

Example 1 — a first encounter with Melissa Haendel

Start with the simplest possible case. Write down what Melissa Haendel claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Melissa Haendel before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Melissa Haendel ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Melissa Haendel

In research: Melissa Haendel appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Melissa Haendel in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Melissa Haendel is common in secondary-school and first-year university syllabi. It links to neighbouring topics 21st-century American women scientists, American bioinformaticians, American women academics, so understanding it makes those chapters shorter.
In everyday life: Look for Melissa Haendel outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Melissa Haendel in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Melissa Haendel means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Melissa Haendel out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Melissa Haendel in simple terms?

Why does Melissa Haendel matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Melissa Haendel?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Melissa Haendel.