Hi,
I have some questions about bioinformatics:
1) What is the newest problem of bioinformatics?
2) Which algorithms of machine learning are using for them right now?
3) What is the application of deep learning on bioinformatics problems?
Thank you very much
1) Too much data, too little good solid annotation. The "deep" or "next generation" sequencing methods flood us with DNA sequences and for many organisms (bacteria, invertebrates, protists, fungi, plants, viruses, etc) it is difficult to assemble and annotate (identify and classify each gene etc) the genomes.
2) Genome annotation pipelines identify the similar sequences in different organisms and tell us something like "your gene x is similar to gene y in Clostridium baratii which was identified as being similar to gene q in Salmonella which was identified as being similar to the transposons classified in E. coli".
3) There are nearly an infintite number of ways that analysis of biological data can be, and is being, improved upon. A lot of it is at the boundary of current ability to access and process large data sets. Database and data transfer technologies are continually being upgraded.
@Brian
Thank you for your answer. Since my background is computer and I am studying bioinformatics, I want to learn more about the challenges and conventional problems as well as tools of bioinformatics. I hope my question makes a bridge between different viewpoints of biologists (like you) and computer engineers (like Wen and I).
Please share me if you have any idea about this field and the related issues as well. Thank you again.
Dear Vahid Nouri ,
1)The most problems of bioinformatics is size of DNA sequence especially when working on next generation sequence it is tremendous and need clouds or big memory in PC ,also the time withe cost of processing this type of date.
2)The machine learning is good for some application in bioinformatics and help a lot, neural network is the trend for majority of the applications.
3)The applications of bioinformatic are countless , but it effect correctly in biological problems (drug design, discover biomarkers, gene prediction , clinical pharmacy)ans so on
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