Deep Learning Causal Attributions of Breast Cancer

In this paper, a deep learning-based approach is applied to high dimensional, high-volume, and high-sparsity medical data to identify critical casual attributions that might affect the survival of a breast cancer patient. The Surveillance Epidemiology and End Results (SEER) breast cancer data is explored in this study. The SEER data set contains accumulated patient-level and treatment-level information, such as cancer site, cancer stage, treatment received, and cause of death. Restricted Boltzmann machines (RBMs) are proposed for dimensionality reduction in the analysis. RBM is a popular paradigm of deep learning networks and can be used to extract features from a given data set and transform data in a non-linear manner into a lower dimensional space for further modelling. In this study, a group of RBMs has been trained to sequentially transform the original data into a very low dimensional space, and then the k-means clustering is conducted in this space. Furthermore, the results obtained about the cluster membership of the data samples are mapped back to the original sample space for interpretation and insight creation. The analysis has demonstrated that essential features relating to breast cancer survival can be effectively extracted and brought forward into a much lower dimensional space formed by RBMs

Literature-Assisted Validation of a Novel Causal Inference Graph in a Sparsely Sampled Multi-Regimen Exercise Data

Background. Causal mechanisms supporting the cardio-metabolic benefits of exercise can be identified for individuals who cannot exercise. With the use of appropriate causal discovery algorithms, the causal pathways can be found for even sparsely sampled data which will help direct drug discovery and pharmaceutical industries to create the appropriate drug to maintain muscles. Objective. The purpose of this study was to infer novel causal source-target interactions active in sparsely sampled data and embed these in a broader causal network extracted from the literature to test their alignment with community-wide prior knowledge and their mechanistic validity in the context of regulatory feedback dynamics. Methods. To this goal, emphasis was placed on the female STRRIDE1/PD dataset to see how the observed data predicts a Causal Directed Acyclic Graph (C-DAG). The analytes in the dataset with greater than 5 missing values were dropped from further analysis to retain a higher confidence among the graphs. The PC, named after its authors Peter and Clark, algorithm was executed for ten thousand iterations on randomly sampled columns of the modified dataset keeping intensity and amount constant as the first two columns to see their effect on the resultant DAG. Out of the 10,000 iterations, interactions that appeared more than 45%, 50%, 65%, 75% and 100% were observed. The interactions that appeared more than 50% of the times were then compared to the literature mined dataset using MedScan Natural Language Processing (NLP) techniques as a part of Pathway Studio. Results. Full consensus across all sub-sampled networks produced 136 interactions that were fully conserved. Of these 136 interactions, 64 were resolved as direct causal interactions, 5 were not direct causal interactions and 67 could only be described as associative. It was found that about 17% of the interactions were recovered from the text mining of the 285 peer-reviewed journals from a total of 64 that were predicted at a 50% consensus. Out of these 11, 4 were completely recovered whereas 7 were only partially recovered. A completely recovered interaction was LDL → ApoB and a partially recovered interaction was HDL → insulin sensitivity. Conclusion. Only 17% of the predicted interactions were found through literature mining, remaining 83% were a mix of novel interactions and self-interactions that need to be worked on further. Of the remaining interactions, 53 remain novel and give insight into how different clinical parameters interact with the cholesterol molecules, biological markers and how they interact with each other