Update README.md

README.md

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 # CF/MS Elution Profile PPI Dataset
 
-Proteins are the functional basis of life, but it is often their interactions with other proteins which gives rise to said functions. Therefore, we are often interested in whether two proteins participate in the same *protein complex*, or if they **'co-complex'**. If they do, both proteins will typically separate out into the same fractions, or **'co-elute'**, during fractionation. As a result, their counts will be *highly correlated* across all the fractions measured. CF/MS leverages this fact to identify new protein complexes by attempting to statistically correlate the elution profiles of groups of proteins. Typically, we use Pearson correlation coefficient to determine correlation between protein pairs. While this often works quite well, Pearson is a linear function. Current research is exploring whether there are non-linear, higher-order signals between these elution profiles that might have better predictive power than Pearson. As deep learning models excel at estimating non-linear relationships in data, the goal of this dataset is to act as training data for such models, especially **Siamese networks**.
+Proteins are the functional basis of life, but it is often their interactions with other proteins which gives rise to said functions. Therefore, we are often interested in whether two proteins participate in the same *protein complex*, or if they **'co-complex'**. Co-fractionation mass spectrometry (CF/MS) is a high-throughput method for determining whether proteins form complexes. If they do, both proteins will typically separate out into the same fractions, or **'co-elute'**, during column chromatography experiments. As a result, their abundances will be *highly correlated* across all the fractions measured. CF/MS leverages this fact to identify new protein complexes by attempting to statistically correlate the elution profiles of groups of proteins. Typically, we use Pearson correlation coefficient to determine correlation between protein pairs. While this often works quite well, Pearson is a linear function. Current research is exploring whether there are non-linear, higher-order signals between these elution profiles that might have better predictive power than Pearson. As deep learning models excel at estimating non-linear relationships in data, the goal of this dataset is to act as training data for such models, especially **Siamese networks**.
 
 Includes processed data from several *Homo sapiens* protein **co-fractionation mass spectrometry (CF/MS)** experiments, as well as positive/negative protein-protein interaction (PPI) labels for each pair.