BACKGROUND

In the United States, approximately one (1) in eight (8) women (i.e., twelve (12) percent) will develop invasive breast cancer over the course of a lifetime. Prior to 2017, public health organization have estimated that 252,710 new cases of invasive breast cancer would be diagnosed in women in the United States, with 63,410 new cases of non-invasive (in situ) breast cancer. In situ cases of cancer are those where malignant cells are present as a tumor, but have neither metastasized nor invaded beyond the basement membrane of where the tumor was discovered. Despite improvements in treatments, the death toll for women in the United States from breast cancer in 2017 was estimated to be about 40,610, which represents a decrease, as the toll has decreased each year from 1989, especially in women under fifty (50). However, breast cancer remains the leading cause of death from any type of cancer, among women in the United States. It is estimated that about thirty (30) percent of newly diagnosed cancers in women will be breast cancers. Although there are certain genetic indicators for increased breast cancer risks, about eight five (85) percent of breast cancers occur in women who have no family history of breast cancer. Presently, the most significant risk factors for breast cancer are gender (being a woman) and age (growing older).

SUMMARY

Shortcomings of the prior art are overcome and additional advantages are provided through the provision of a computer program product for predicting likelihood of a condition. The computer program product comprises a storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method. The method includes, for instance: obtaining, by the one or more processors, a raw data set comprising peptide libraries of patients designated as either diagnosed or pre-diagnosed with a condition or not diagnosed with the condition; segmenting, by the one or more processors, the raw data set into a pre-defined number of groups, wherein the segmenting comprises separating out a holdout group of data from the remaining groups; performing, by the one or more processors, a principal component analysis on the remaining groups to identify, based on a frequency of features in the remaining groups, by the one or more processors, common features in data of the remaining groups and weighting the common features based on frequency of occurrence in the remaining groups, wherein the common features comprise principal components represented by coefficients; determining, by the one or more programs, a smallest number of the principal components that yields a pre-defined level of validation accuracy; and generating, by the one or more processors, a predictive model, by utilizing the smallest number of principal components as parameters for a best fit in a logistic regression model, wherein the predictive model provides binary outcomes selected from the group consisting of: likelihood of presence of the condition within a pre-defined threshold or no likelihood of the presence of the condition within the pre-defined threshold.

Shortcomings of the prior art are overcome and additional advantages are provided through the provision of a method of predicting likelihood of a condition. The method includes, for instance: obtaining, by one or more processors, a raw data set comprising peptide libraries of patients designated as either diagnosed or pre-diagnosed with a condition or not diagnosed with the condition; segmenting, by the one or more processors, the raw data set into a pre-defined number of groups, wherein the segmenting comprises separating out a holdout group of data from the remaining groups; performing, by the one or more processors, a principal component analysis on the remaining groups to identify, based on a frequency of features in the remaining groups, by the one or more processors, common features in data of the remaining groups and weighting the common features based on frequency of occurrence in the remaining groups, wherein the common features comprise principal components represented by coefficients; determining, by the one or more programs, a smallest number of the principal components that yields a pre-defined level of validation accuracy; and generating, by the one or more processors, a predictive model, by utilizing the smallest number of principal components as parameters for a best fit in a logistic regression model, wherein the predictive model provides binary outcomes selected from the group consisting of: likelihood of presence of the condition within a pre-defined threshold or no likelihood of the presence of the condition within the pre-defined threshold.

Shortcomings of the prior art are overcome and additional advantages are provided through the provision of a system for predicting likelihood of a condition. The system includes a memory, one or more processor in communication with the memory, and program instructions executable by the one or more processor via the memory to perform a method. The method includes, for instance: obtaining, by the one or more processors, a raw data set comprising peptide libraries of patients designated as either diagnosed or pre-diagnosed with a condition or not diagnosed with the condition; segmenting, by the one or more processors, the raw data set into a pre-defined number of groups, wherein the segmenting comprises separating out a holdout group of data from the remaining groups; performing, by the one or more processors, a principal component analysis on the remaining groups to identify, based on a frequency of features in the remaining groups, by the one or more processors, common features in data of the remaining groups and weighting the common features based on frequency of occurrence in the remaining groups, wherein the common features comprise principal components represented by coefficients; determining, by the one or more programs, a smallest number of the principal components that yields a pre-defined level of validation accuracy; and generating, by the one or more processors, a predictive model, by utilizing the smallest number of principal components as parameters for a best fit in a logistic regression model, wherein the predictive model provides binary outcomes selected from the group consisting of: likelihood of presence of the condition within a pre-defined threshold or no likelihood of the presence of the condition within the pre-defined threshold.

Methods and systems relating to one or more aspects are also described and claimed herein. Further, services relating to one or more aspects are also described and may be claimed herein.

Additional features and advantages are realized through the techniques described herein. In some embodiments of the present invention, the method includes testing, by the one or more processors, the predictive model, by applying the coefficients to calculate features to use for the holdout group; and tuning, by the one or more processors, the predictive model by comparing the features for the holdout group to the principal components. In some embodiments of the present invention, the peptide libraries comprise 12-mer peptide libraries and/or the condition comprises Stage I breast cancer. In some embodiments of the present invention, segmenting comprises generating random numbers to select which record of the raw data set to assign to which of the groups.

In some embodiments of the present invention, the method includes, prior to performing the principal component analysis, normalizing, by the one or more processors, the peptides comprising the raw data set by adding peptide values of the peptides across all the peptides and dividing each expression value by a resultant sum to compute a ratio.

In some embodiments of the present invention, performing a principal component analysis on the remaining groups comprises: generating, by the one or more processors, sets of training data, wherein each set of training data of the sets of training data comprises data in all but one group of the remaining groups; performing, by the one or more processors, a principal component analysis individually on each set of training data to identify common features in each set of training data; executing, by the one or more programs, a principal component analysis utilizing the remaining groups as a single training set to identify common features in the single training set; and performing a cross-validation of the common features of each set of training data and the common features in the single training set, wherein the cross-validation is of a number of folds equal to a number of the remaining groups, to identify the principal components and determine the coefficients. In some embodiments of the present invention, a pre-defined level of validation accuracy is a highest validation accuracy based on the number of folds.

In some embodiments of the present invention, the method includes: obtaining, by the one or more processors, new raw data comprising a 12-mer peptide library of a given individual; and applying, by the one or more processors, the predictive model to the new raw data to determine a binary outcome for the given individual. In some embodiments of the present invention, applying the predictive model comprises performing a principal component analysis on the new raw data utilizing the coefficients and performing a logistic regression to generate the binary outcome.

