PubMed · 10586484
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S Degani. 1999. The year 2000 software bug.. https://pubmed.ncbi.nlm.nih.gov/10586484/
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Using a derivation data set of 1253 patients, we built several logistic regression and neural network models to estimate the likelihood of myocardial infarction based upon patient-reportable clinical history factors only. The best performing logistic regression model and neural network model had C-indices of 0.8444 and 0.8503, respectively, when validated on an independent data set of 500 patients. We conclude that both logistic regression and neural network models can be built that successfully predict the probability of myocardial infarction based on patient-reportable history factors alone. These models could have important utility in applications outside of a hospital setting when objective diagnostic test information is not yet be available.
"Natively unfolded" proteins occupy a unique niche within the protein kingdom in that they lack ordered structure under conditions of neutral pH in vitro. Analysis of amino acid sequences, based on the normalized net charge and mean hydrophobicity, has been applied to two sets of proteins: small globular folded proteins and "natively unfolded" ones. The results show that "natively unfolded" proteins are specifically localized within a unique region of charge-hydrophobicity phase space and indicate that a combination of low overall hydrophobicity and large net charge represent a unique structural feature of "natively unfolded" proteins.
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