There are many possible reasons for such biased annotation, ranging from bias in scientific interest��yeast has historically been a major model for studying many core Catharanthine sulfate cellular processes including eukaryotic protein biosynthesis��to bias in technological feasibility��it is generally easier to study highly expressed proteins such as ribosomal proteins��to intrinsic bias in the cellular system themselves��core molecular machines such as the ribosome legitimately incorporate more genes than many other cellular systems. We suspect that such bias is inevitable; nonetheless, we need to minimize its adverse effects for network reconstruction. We examined the consequences of this bias by ”masking”this dominant term in the annotation reference set, thereby removing all reference gene pairs linked via this term, and then testing data sets for their performance on the full and masked reference sets. For example, mRNA co-expression relationships between yeast genes across various heat-shock treatments appear to strongly predict functional associations when benchmarked using the full, biased reference set. However, that strong relationship largely disappears after masking only the single reference term ”protein biosynthesis”. This Ginsenoside-F5 observation clearly indicates that the strong functional associations derived from co-expression over these particular arrays are limited largely to protein biosynthesis genes. Thus, assigning a high likelihood score for gene pairs that co-express highly but are not in protein biosynthesis would be misleading. Examination of the frequency distribution of reference set gene pairs shows that the next most dominant term accounts for,5% of reference pairs, with contributions from remaining terms decaying fairly smoothly. We therefore removed only the dominant ”protein biosynthesis”term before reconstructing the probabilistic yeast gene network. Because of the generally strong correlation between protein physical or genetic 
interactions and functional associations, a map of such interactions among proteins is an invaluable source for learning about protein functions and pathways. Among many techniques of mapping protein physical interaction, yeast two hybrid assays and affinity purification followed by mass spectrometry have proved to be the most popular for their scalability. Two major genome-scale yeast two hybrid screens reported more than 4,000 binary interactions. While these interactions passed minimum quality criteria, we might not expect all to be equally informative for inferring functional associations. The original confidence measures��dividing interactions into a more reproducible ”core”set and less reproducible ”non-core”set ��is coarse-grained and may often miss functionally informative interactions. Mass-spectrometry-derived interaction data, usually provided as a list of baits of affinity purification and their identified preys, is even more complicated for inferring binary physical or functional associations. Two different models of inferring binary interactions from the lists of identifications have been widely used��the spoke and matrix models. The spoke model allows pair-wise relationships only between baits and preys in the same complexes, whereas the matrix model includes additional relationships inferred by pairing preys in the same complexes. These interpretative models exhibit different trade-offs between completeness and accuracy��the spoke model achieves high accuracy at the cost of incompleteness, whereas the matrix model provides a more complete model but relatively low accuracy due to pairing all prey proteins from a given bait with each other.