A Graphical Model of Smoking-Induced Global Instability in Lung Cancer

Abstract

Smoking is the major cause of lung cancer and the leading cause of cancer-related death in the world. The most current view about lung cancer is no longer limited to individual genes being mutated by any carcinogenic insults from smoking. Instead, tumorigenesis is a phenotype conferred by many systematic and global alterations, leading to extensive heterogeneity and variation for both the genotypes and phenotypes of individual cancer cells. Thus, strategically it is foremost important to develop a methodology to capture any consistent and global alterations presumably shared by most of the cancerous cells for a given population. This is particularly true that almost all of the data collected from solid cancers (including lung cancers) are usually distant apart over a large span of temporal or even spatial contexts. Here, we report a multiple non-Gaussian graphical model to reconstruct the gene interaction network using two previously published gene expression datasets. Our graphical model aims to selectively detect gross structural changes at the level of gene interaction networks. Our methodology is extensively validated, demonstrating good robustness, as well as the selectivity and specificity expected based on our biological insights. In summary, gene regulatory networks are still relatively stable during presumably the early stage of neoplastic transformation. But drastic structural differences can be found between lung cancer and its normal control, including the gain of functional modules for cellular proliferations such as EGFR and PDGFRA, as well as the lost of the important IL6 module, supporting their roles as potential drug targets. Interestingly, our method can also detect early modular changes, with the ALDH3A1 and its associated interactions being strongly implicated as a potential early marker, whose activations appear to alter LCN2 module as well as its interactions with the important TP53-MDM2 circuitry. Our strategy using the graphical model to reconstruct gene interaction work with biologically-inspired constraints exemplifies the importance and beauty of biology in developing any bio-computational approach.

Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites

“…For drug discovery and development, it is one of most important things to find protein targets of drugs. Although a large number of approaches have been proposed to detect new targets of drugs in the past decades [ 5 , 6 , 7 , 10 , 11 , 14 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ], few have dealt with specific proteins such as post-translationally modified proteins which play key regulating roles in cellular activities [ 79 , 80 ]. Protein SNO is involved in the pathological progression of some diseases, especially neurodegenerative diseases.…”

Deep learning for predicting disease status using genomic data

“…Recently, the authors [29] improved their results using deep transfer learning [57]. Moreover, semi-supervised learning and reinforcement learning are receiving a lot of attention to image recognition, gaming, and robotics [58–60].…”

Leaf identification using radial basis function neural networks and SSA based support vector machine

“…In addition, the study presented in [13] used SVM for segmenting and classifying legume leaves based on the analysis of their veins. Tan et al used a classifier, namely CNN for plant identification from leaf vein features [14]. They used the CNN classifier to discriminate between three types of legume species named white, red, and soybean.…”