BACKGROUND: Identification of differentially expressed genes, i.e., genes whose transcript abundance level differs across different biological or physiological conditions, was indeed a challenging task. However, the inception of transcriptome sequenc...
We introduce the CDRP (Concatenated Diagnostic-Relapse Prognostic) architecture for multi-task deep learning that incorporates a clinical algorithm, e.g., a risk stratification schema to improve prognostic profiling. We present the first application ...
Although modern methods of whole genome DNA methylation analysis have a wide range of applications, they are not suitable for clinical diagnostics due to their high cost and complexity and due to the large amount of sample DNA required for the analys...
BACKGROUND: The high growth of Next Generation Sequencing data currently demands new knowledge extraction methods. In particular, the RNA sequencing gene expression experimental technique stands out for case-control studies on cancer, which can be ad...
Computer methods and programs in biomedicine
Oct 5, 2018
BACKGROUND AND OBJECTIVE: Cancer has become a complex health problem due to its high mortality. Over the past few decades, with the rapid development of the high-throughput sequencing technology and the application of various machine learning methods...
Journal of chemical information and modeling
Oct 3, 2018
In this study, we developed two cancer-specific machine learning classifiers for prediction of driver mutations in cancer-associated genes that were validated on canonical data sets of functionally validated mutations and applied to a large cancer ge...
We propose an effective machine learning approach to identify group of interacting single nucleotide polymorphisms (SNPs), which contribute most to the breast cancer (BC) risk by assuming dependencies among BCAC iCOGS SNPs. We adopt a gradient tree b...
Cancer is a complex disease that is caused by rapid alteration of genes. Prediction of the state of cancer in advance contributes to a better understanding of its mechanism and improves the cancer therapy process. For example, predicting the malignan...
Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier was subsequently established to predict the recurrence of OC. Three datasets (GSE17260, GSE44104 an...
This paper demonstrates the ability of mach- ine learning approaches to identify a few genes among the 23,398 genes of the human genome to experiment on in the laboratory to establish new drug mechanisms. As a case study, this paper uses MDA-MB-231 b...