GSU Magazine: Searching for Patterns
Computer Science; Advances
Computer Science; Advances
This article for the Advances science department of the GSU Magazine highlights the researcher of a Ph.D. student investigating the use of computers to search for patterns.
In life, patterns abound. From DNA molecules to the habits of shoppers, there are sequences that repeat and tell researchers more about the world.
Finding these complex patterns and determining what they mean is not easy, however, and the process requires immense computing power.
Ken Nguyen is working on a way to analyze patterns more efficiently - helping scientists gain a more accurate picture of complex sequences and leading to better research in fields ranging from drug discovery to linguistics and business.
A recipient of the university's William Suttles Fellowship for doctoral scholars, Nguyen is searching for an algorithm to speed up the analysis of multiple sequence alignments, or MSAs.
In biology, MSAs are arrangements of three or more biological sequences, like proteins or DNA and RNA molecules. They tend to repeat, and by finding MSAs and analyzing them, scientists can detect patterns that tell more about their function, or whether or not they might cause disease.
These insights could give researchers better leads on which drugs might combat a particular disease or condition, Nguyen said.
As a whole, optimally analyzing even 100 sequences could take months or years. And when examining a more complex set of MSAs, the number of sequences increases exponentially to hundreds of thousands of MSAs.
"It can take a lot of time and computing power resources, and it's very hard to do in a practical manner," Nguyen said.
Nguyen's new algorithm will help researchers to not only work faster, but smarter, through more accurate analysis of the sequence alignments. Nguyen, who is conducting his research under Department of Computer Science Chair Yi Pan, anticipates that his work will have applications beyond biology and drug discovery.
MSAs can exist in other fields, Nguyen said. For example, researchers have also used multiple sequence alignment in linguistics. In studying historical and comparative linguistics - examining languages of old and comparing them - multiple sequence alignment has been used to partially automate the method that linguists use to reconstruct languages.
In business, sequences can be seen as symbols, and the items that people purchase become repeating symbols that can be analyzed mathematically.
"You can see how people tend to buy certain things at certain times," Nguyen said. "It is especially good for marketing research, and you can better detect certain patterns and habits."
Finding these complex patterns and determining what they mean is not easy, however, and the process requires immense computing power.
Ken Nguyen is working on a way to analyze patterns more efficiently - helping scientists gain a more accurate picture of complex sequences and leading to better research in fields ranging from drug discovery to linguistics and business.
A recipient of the university's William Suttles Fellowship for doctoral scholars, Nguyen is searching for an algorithm to speed up the analysis of multiple sequence alignments, or MSAs.
In biology, MSAs are arrangements of three or more biological sequences, like proteins or DNA and RNA molecules. They tend to repeat, and by finding MSAs and analyzing them, scientists can detect patterns that tell more about their function, or whether or not they might cause disease.
These insights could give researchers better leads on which drugs might combat a particular disease or condition, Nguyen said.
As a whole, optimally analyzing even 100 sequences could take months or years. And when examining a more complex set of MSAs, the number of sequences increases exponentially to hundreds of thousands of MSAs.
"It can take a lot of time and computing power resources, and it's very hard to do in a practical manner," Nguyen said.
Nguyen's new algorithm will help researchers to not only work faster, but smarter, through more accurate analysis of the sequence alignments. Nguyen, who is conducting his research under Department of Computer Science Chair Yi Pan, anticipates that his work will have applications beyond biology and drug discovery.
MSAs can exist in other fields, Nguyen said. For example, researchers have also used multiple sequence alignment in linguistics. In studying historical and comparative linguistics - examining languages of old and comparing them - multiple sequence alignment has been used to partially automate the method that linguists use to reconstruct languages.
In business, sequences can be seen as symbols, and the items that people purchase become repeating symbols that can be analyzed mathematically.
"You can see how people tend to buy certain things at certain times," Nguyen said. "It is especially good for marketing research, and you can better detect certain patterns and habits."