computer science

Human disease network

Human disease network is a computer science 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 Human disease network rather than just read about it. In short: A human disease network is a network of human disorders and diseases with reference to their genetic origins or other features. More specifically, it is the map of human disease associations referring mostly to disease genes.

Key takeaways

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

Reference excerpt

A human disease network is a network of human disorders and diseases with reference to their genetic origins or other features. More specifically, it is the map of human disease associations referring mostly to disease genes. For example, in a human disease network, two diseases are linked if they share at least one associated gene. A typical human disease network usually derives from bipartite networks which consist of both diseases and genes information. Additionally, some human disease networks use other features such as symptoms and proteins to associate diseases.

History In 2007, Goh et al. constructed a disease-gene bipartite graph using information from OMIM database and termed human disease network. In 2009, Barrenas et al. derived complex disease-gene network using GWAs (Genome Wide Association studies). In the same year, Hidalgo et al. published a novel way of building human phenotypic disease networks in which diseases were connected according to their calculated distance. In 2011, Cusick et al. summarized studies on genotype-phenotype associations in cellular context. In 2014, Zhou, et al. built a symptom-based human disease network by mining biomedical literature database.

Properties A large-scale human disease network shows scale-free property. The degree distribution follows a power law suggesting that only a few diseases connect to a large number of diseases, whereas most diseases have few links to others. Such network also shows a clustering tendency by disease classes. In a symptom-based disease network, disease are also clustered according to their categories. Moreover, diseases sharing the same symptom are more likely to share the same genes and protein interactions.

See also Bioinformatics Genome Network theory Network medicine

References

External links https://web.archive.org/web/20080625034729/http://hudine.neu.edu/ http://www.barabasilab.com/pubs/CCNR-ALB_Publications/200705-14_PNAS-HumanDisease/200705-14_PNAS-HumanDisease-poster.pdf [2] https://www.nytimes.com/2008/05/06/health/research/06dise.html

Illustrations

Worked examples

Example 1 — a first encounter with Human disease network

Start with the simplest possible case. Write down what Human disease network claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, 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 Human disease network 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 Human disease network 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 Human disease network

In research: Human disease network appears in computer science 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 Human disease network 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: Human disease network is common in secondary-school and first-year university syllabi. It links to neighbouring topics Network theory, so understanding it makes those chapters shorter.
In everyday life: Look for Human disease network 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 Human disease network in 20 minutes

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

Frequently asked questions

What is Human disease network in simple terms?

Why does Human disease network matter?

Because it connects several computer science 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 Human disease network?

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 Human disease network.