Domain expertise–agnostic feature selection for the analysis of breast cancer data*

Abstract

At present, high-dimensional data sets are becoming more and more frequent. The problem of feature selection has already become widespread, owing to the curse of dimensionality. Unfortunately, feature selection is largely based on ground truth and domain expertise. It is possible that ground truth and/or domain expertise will be unavailable, therefore there is a growing need for unsupervised feature selection in multiple fields, such as marketing and proteomics. Now, unlike in past time, it is possible for biologists to measure the amount of protein in a cancer cell. No wonder the data is high-dimensional, the human body is composed of thousands and thousands of proteins. Intuitively, only a handful of proteins cause the onset of the disease. It might be desirable to cluster the cancer sufferers, but at the same time we want to find the proteins that produce good partitions. We hereby propose a methodology designed to find the features able to maximize the clustering performance. After we divided the proteins into different groups, we clustered the patients. Next, we evaluated the clustering performance. We developed a couple of pipelines. Whilst the first focuses its attention on the data provided by the laboratory, the second takes advantage both of the external data on protein complexes and of the internal data. We set the threshold of clustering performance thanks to the biologists at Karolinska Institutet who contributed to the project. In the thesis we show how to make a good selection of features without domain expertise in case of breast cancer data. This experiment illustrates how we can reach a clustering performance up to eight times better than the baseline with the aid of feature selection.

Evolving Trends in Surgical Management of Breast Cancer: An Analysis of 30 Years of Practice Changing Papers

“…Surgical innovation continues to drive advances in the management of breast cancer. Artificial intelligence (AI) technology and machine learning algorithms applied to diagnostic imaging and analysis of large clinical and genomic datasets in predicting response to treatment have been shown to improve patient outcomes (101)(102)(103)(104). Once healthcare practitioners have overcome the fear of the unknown and data scientists and AI experts become more incorporated into healthcare, the future of surgical breast cancer management may change rapidly.…”

A User-Guided Bayesian Framework for Ensemble Feature Selection in Life Science Applications (UBayFS)

“…, θ N ) ∈ [0, 1] N , θ 1 = 1, as the success probabilities of sampling each feature in an individual urn draw. In a statistical sense, we interpret the result from each elementary feature selector ζ m as realization from a multinomial distribution with parameters θ and l. 2 On the one hand, the multinomial setup allows us to define the likelihood p(∆|θ) as joint probability density…”

“…Here, two sources of information are available: large-scale collections of data from multiple sources, and profound knowledge from domain experts. Previous works in data science tend to handle these sources as opposites, see [1], or neglect expert knowledge completely, see [2]. However, a combination of both can be valuable to compensate for underdetermined problem setups from high-dimensional datasets.…”

Artificial intelligence (AI) in breast cancer care - Leveraging multidisciplinary skills to improve care

“…[ 16 ], and Pozzoli et al. [ 17 ] offer significant contributions to the methodological side of the big data topic. Macias-Garcia et al.…”

“…Papers by Macias-Garcia et al [16], and Pozzoli et al [17] offer significant contributions to the methodological side of the big data topic. Macias-Garcia et al summarize DNA methylation data to generate new features from the values of CpG sites of patients, to predict breast cancer recurrence.…”