PMC:4331678 / 15732-16726 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331678","sourcedb":"PMC","sourceid":"4331678","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331678","text":"The functional labels are organized in a hierarchy (a directed acyclic graph for GO terms, and a tree for MIPS FunCat). The more specific the functional label is in the hierarchy, the fewer member proteins this label has. If a protein has a confirmed functional label, this protein is also annotated with its ancestor labels in the hierarchy [3,30,34]. Therefore, protein function prediction is an unbalanced classification problem and to achieve a good prediction it's important to take into account this issue [3,11]. Eq. (1) ignores the unbalanced problem and considers all functional labels as equal. To address this limitation, we modify yic into y˜ic=yiclogNnc+ (N=∑c=1Cnc+,nc+ proteins annotated with the c-th function). The added factor has the effect of putting more emphasis on functional labels that are more specific. This forces the optimizer to focus on the more specific functions, versus the general ones which have more member proteins. We set Y˜=[y˜1,⋯,y˜n], and F=(H+L)-1HY˜.","tracks":[{"project":"2_test","denotations":[{"id":"25707434-10802651-14839076","span":{"begin":345,"end":347},"obj":"10802651"},{"id":"25707434-20479498-14839077","span":{"begin":348,"end":350},"obj":"20479498"}],"attributes":[{"subj":"25707434-10802651-14839076","pred":"source","obj":"2_test"},{"subj":"25707434-20479498-14839077","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#9fec93","default":true}]}]}}