At a recent annual conference hosted by the University of California, Berkeley, the researchers demonstrated their progress in accelerating research in cancer gene therapy and expanding computer theory. David A. Patterson, a professor of computer science at the University of California, Berkeley, called on the industry to use millions of gene pools together to promote cancer treatment. At present, independent gene banks have less than 10,000 pieces of gene information, many of which are only partial fragments of genes.
"It can be said that computer science is currently used to build a fast and precise genetic pipeline and promote a good opportunity for personalized treatment. I hope to use this as soon as possible to help me and my family," Patterson also revealed that current researchers Often the genetic data is deleted after completing the experiment.
Computer scientist David Patterson hopes to overcome the problem of cancer genome through massive data analysis.
Patterson assisted in the development of a tool called SNAP, which obviously provides faster and more accurate genetic analysis and is now widely used by cancer researchers. However, there is still a need for benchmarking tools to improve methods that are still highly subjective in this field, he pointed out.
In addition, the University of California is preparing to spend $ 60 million to establish a new college, hoping to achieve new breakthroughs in computer science in the next ten years. "We want to develop a new computing theory that will go far beyond current research and cover problems in other fields," said Richard Karp, who directs this work.
"Many phenomena can be regarded as having computational characteristics," he pointed out. "If we carefully observe the working method of living cells, we can think of it as an information processing; and the economy is also an information processing activity."
The school will also hold an academic seminar in May this year to gather experts from all walks of life to jointly study the possibility of new theory.
Using huge amounts of data to treat cancer
Patterson's goal is to use Berkeley's huge data tools in the development of corrections to enable it to import and analyze large amounts of genetic data for cancer research.
These toolkits include Spark, a programming language for computer clusters, that provides Map Reduce functionality similar to that used by Google's search engine. Berkeley Data Analysis System (Data AnalyTIcs System) is an open source engine based on Spark.
Scientists at Berkeley Lab have used algorithms and computational pipelines to sort out a large number of image combinations and find out the subtypes of tumors. It can also analyze the tissue structure contained in heterogeneity or tumors at different degrees. Then, the calculation pipeline can use clinical data to sort the cell characteristics to predict the prognosis of patients. At the same time, it also uses large-scale genomic data to determine the molecular correlation of each subtype through massive data analysis.
Researchers have developed methods that can automatically analyze a large number of tumor images, helping to predict the response of cancer treatment. The arrow enlarges the distinct area of ​​the tumor image. (Source: Berkeley Labs)
At present, the challenge of huge data research is to develop a technology that can obtain timely and cost-effective answers to huge data problems. The Berkeley research team is addressing this challenge through several actions: developing algorithms based on statistical machine learning; using multiple machines in the cloud; and developing fund-raising techniques, combined with the power of the masses, to help answer their calculations Law and machine are too difficult.
Through these technologies will help to break through the treatment of cancer. In order to achieve faster and more cost-effective processing of genomic data, what is needed is a new infrastructure that can simultaneously use cloud machines. And when the problems related to the discovery and diagnosis of cancer genes go beyond these algorithms and machines, it is necessary to gather the resources and wisdom of everyone to overcome the challenges together.
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