Nav: Home

Finding missing network links could help develop new drugs, stop disease, ease traffic

June 25, 2019

WASHINGTON, D.C., June 25, 2019 -- A new mathematical model of the structure of networks could help find new cancer drugs, speed up traffic flow and combat sexually transmitted disease.

Although the three challenges seem diverse, they all could benefit from a theory that helps to uncover unknown information about a network by analyzing its structure. The study was published in the journal Chaos, from AIP Publishing.

An example of how filling in missing links could be useful is in choosing a genetic target for a cancer drug, said team member Michael Small, from the University of Western Australia.

"Say you have a network of genes that are somehow connected, and there are some known drug targets. But if you don't know all the genes, you would like to make guesses at information that you don't have to work out what might be other likely targets to investigate," Small said.

Successful link prediction algorithms already exist for certain types of networks, but the researchers analyzed differently structured networks to come up with their alternative algorithm.

An example of traditional link prediction might be a social network suggesting friends with which to connect. Two people who share a friend are likely to themselves be friends with a direct connection. Adding this third connection creates a triangle, which is a structure where existing algorithms focus.

In contrast, the authors focused on treelike networks, which have many branches but very few cross links between branches. They studied three example datasets: the social network Twitter, a water distribution network and a sexual contact network.

They found these three treelike networks could be characterized by a number of parameters, such as the average distance between branch points in the network, the size of loops and a comparison of the number of links that adjacent nodes have -- a measure of the network's regularity describing heterogeneity.

The authors then developed an algorithm that suggested links that would preserve these characteristics of the network.

They tested this approach by taking a known network and removing links from it and seeing if the algorithm could predict where the missing links should be.

The team found the algorithm performed better (around 44% for the Twitter network, around 15% for the sexual contact network and around 4% for the water distribution network) for treelike networks than most of conventional algorithms which rely on other parameters, such as assuming highly connected individuals will attract more connections (known as preferential attachment) or building lots of triangular connections (known as clustering).

Author Keke Shang attributes the team's success to thinking about examples of real-world networks.

"I hope we can make network technology serve our lives better," he said.
-end-
The article, "Link prediction for tree-like networks," is authored by Keke Shang, Tong-chen Li, Michael Small, David Burton and Yan Wang. The article will appear in the journal Chaos on June 25, 2019 (DOI: 10.1063/1.5107440). After that date, it can be accessed at http://aip.scitation.org/doi/full/10.1063/1.5107440.

ABOUT THE JOURNAL

Chaos is devoted to increasing the understanding of nonlinear phenomena in all disciplines and describing their manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.

American Institute of Physics

Related Cancer Drugs Articles:

Researchers find way to better use current drugs to target cancer
The drugs helped to understand the biology. The researchers worked backwards, employing a series of drugs used in the clinic to understand a new way that cancer stem cells can be killed.
Bacteria used as factories to produce cancer drugs
Researchers at the Novo Nordisk Foundation Center for Biosustainability in Denmark have developed a method of producing P450 enzymes -- used by plants to defend against predators and microbes -- in bacterial cell factories.
Supercomputers assist in search for new, better cancer drugs
Finding new drugs that can more effectively kill cancer cells or disrupt the growth of tumors is one way to improve survival rates for ailing patients.
Finding new cancer drugs in the neighborhood
Computational biologists have looked at the complex networks of interacting proteins that drive cancer formation, and found that targeting the neighbors of cancer-causing proteins may be just as effective as focusing on the cancer proteins themselves.
Treating cancer with drugs for diabetes and hypertension
A combination of a diabetes medication and an antihypertensive drug can effectively combat cancer cells.
More Cancer Drugs News and Cancer Drugs Current Events

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Anthropomorphic
Do animals grieve? Do they have language or consciousness? For a long time, scientists resisted the urge to look for human qualities in animals. This hour, TED speakers explore how that is changing. Guests include biological anthropologist Barbara King, dolphin researcher Denise Herzing, primatologist Frans de Waal, and ecologist Carl Safina.
Now Playing: Science for the People

#534 Bacteria are Coming for Your OJ
What makes breakfast, breakfast? Well, according to every movie and TV show we've ever seen, a big glass of orange juice is basically required. But our morning grapefruit might be in danger. Why? Citrus greening, a bacteria carried by a bug, has infected 90% of the citrus groves in Florida. It's coming for your OJ. We'll talk with University of Maryland plant virologist Anne Simon about ways to stop the citrus killer, and with science writer and journalist Maryn McKenna about why throwing antibiotics at the problem is probably not the solution. Related links: A Review of the Citrus Greening...