mathematics

Paola Sebastiani

Paola Sebastiani is a mathematics 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 Paola Sebastiani rather than just read about it. In short: Paola Sebastiani is a biostatistician and a professor at Boston University working in the field of genetic epidemiology, building prognostic models that can be used for the dissection of complex traits. Her research interests include Bayesian modeling of biomedical data, particularly genetic and genomic data.

Key takeaways

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

Reference excerpt

Paola Sebastiani is a biostatistician and a professor at Boston University working in the field of genetic epidemiology, building prognostic models that can be used for the dissection of complex traits. Her research interests include Bayesian modeling of biomedical data, particularly genetic and genomic data.

Education and career Sebastiani obtained a first degree in mathematics from the University of Perugia, Italy (1987), an M.Sc. in statistics from University College London (1990), and a Ph.D. in statistics from the Sapienza University of Rome (1992). She came to Boston University in 2003, after previously having been an assistant professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst.

Contributions Her most important contribution is a model based on a Bayesian network that integrates more than 60 single-nucleotide polymorphisms (SNPs) and other biomarkers to compute the risk for stroke in patients with sickle cell anemia. This model was shown to have high sensitivity and specificity and demonstrated, for the first time, how an accurate risk prediction model of a complex genetic trait that is modulated by several interacting genes can be built using Bayesian networks. A controversial paper regarding the genetics of aging with which she was associated was retracted from the journal Science in 2011 due to flawed data. The corrected version was published in PLOS ONE, and several of the genes found associated with exceptional human longevity were replicated in other studies of centenarians.

Publications She has published several peer-reviewed papers. According to Scopus the most cited ones are:

Ramoni M.F.; Sebastiani P.; Kohane I.S. (2002). "Cluster analysis of gene expression dynamics" (2002)". Proceedings of the National Academy of Sciences of the United States of America. 99 (14): 9121–9126. doi:10.1073/pnas.132656399. PMC 123104. PMID 12082179. Sebastiani P.; Ramoni M.F.; Nolan V.; Baldwin C.T.; Steinberg M.H. (2005). "Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia" (2005)". Nature Genetics. 37 (4): 435–440. doi:10.1038/ng1533. PMC 2896308. PMID 15778708. Mandl K.D.; Overhage J.M.; Wagner M.M.; Lober W.B.; Sebastiani P.; Mostashari F.; Pavlin J.A.; Gesteland P.H.; Treadwell T.; Koski E.; Hutwagner L.; Buckeridge D.L.; Aller R.D.; Grannis S. (2003). "Implementing syndromic surveillance: A practical guide informed by the early experience" (2004)". Journal of the American Medical Informatics Association. 11 (2): 141–150. doi:10.1197/jamia.m1356. PMC 353021. PMID 14633933. Sebastiani P.; Gussoni E.; Kohane I.S.; Ramoni M.F.; Baker H.V. (2003). "Statistical challenges in functional genomics" (2003)". Statistical Science. 18 (1): 33–70. doi:10.1214/ss/1056397486.

Awards and honors She became a fellow of the American Statistical Association in 2017.

References

Worked examples

Example 1 — a first encounter with Paola Sebastiani

Start with the simplest possible case. Write down what Paola Sebastiani claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, 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 Paola Sebastiani 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 Paola Sebastiani 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 Paola Sebastiani

In research: Paola Sebastiani appears in mathematics 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 Paola Sebastiani 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: Paola Sebastiani is common in secondary-school and first-year university syllabi. It links to neighbouring topics Alumni of University College London, Biostatisticians, Boston University faculty, so understanding it makes those chapters shorter.
In everyday life: Look for Paola Sebastiani 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 Paola Sebastiani in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Paola Sebastiani 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 Paola Sebastiani out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Paola Sebastiani in simple terms?

Why does Paola Sebastiani matter?

Because it connects several mathematics 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 Paola Sebastiani?

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 Paola Sebastiani.