An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules

Due to the high heterogeneity and complexity of cancer, it is still a challenge to predict the prognosis of cancer patients. In this work, we used a clustering algorithm to divide patients into different subtypes in order to reduce the heterogeneity of the cancer patients in each subtype. Based on the hypothesis that the gene co-expression network may reveal relationships among genes, some communities in the network could influence the prognosis of cancer patients and all the prognosis-related communities could fully reveal the prognosis of cancer patients. To predict the prognosis for cancer patients in each subtype, we adopted an ensemble classifier based on the gene co-expression network of the corresponding subtype. Using the gene expression data of ovarian cancer patients in TCGA (The Cancer Genome Atlas), three subtypes were identified. Survival analysis showed that patients in different subtypes had different survival risks. Three ensemble classifiers were constructed for each subtype. Leave-one-out and independent validation showed that our method outperformed control and literature methods. Furthermore, the function annotation of the communities in each subtype showed that some communities were cancer-related. Finally, we found that the current drug targets can partially support our method

Network-Based Biomarker Discovery : Development of Prognostic Biomarkers for Personalized Medicine by Integrating Data and Prior Knowledge

Advances in genome science and technology offer a deeper understanding of biology while at the same time improving the practice of medicine. The expression profiling of some diseases, such as cancer, allows for identifying marker genes, which could be able to diagnose a disease or predict future disease outcomes. Marker genes (biomarkers) are selected by scoring how well their expression levels can discriminate between different classes of disease or between groups of patients with different clinical outcome (e.g. therapy response, survival time, etc.). A current challenge is to identify new markers that are directly related to the underlying disease mechanism

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates

Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction.

Abnormal miRNA functions are widely involved in many diseases recorded in the database of experimentally supported human miRNA-disease associations (HMDD). Some of the associations are complicated: There can be up to five heterogeneous association types of miRNA with the same disease, including genetics type, epigenetics type, circulating miRNAs type, miRNA tissue expression type and miRNA-target interaction type. When one type of association is known for an miRNA-disease pair, it is important to predict any other types of the association for a better understanding of the disease mechanism. It is even more important to reveal associations for currently unassociated miRNAs and diseases. Methods have been recently proposed to make predictions on the association types of miRNA-disease pairs through restricted Boltzman machines, label propagation theories and tensor completion algorithms. None of them has exploited the non-linear characteristics in the miRNA-disease association network to improve the performance. We propose to use attributed multi-layer heterogeneous network embedding to learn the latent representations of miRNAs and diseases from each association type and then to predict the existence of the association type for all the miRNA-disease pairs. The performance of our method is compared with two newest methods via 10-fold cross-validation on the database HMDD v3.2 to demonstrate the superior prediction achieved by our method under different settings. Moreover, our real predictions made beyond the HMDD database can be all validated by NCBI literatures, confirming that our method is capable of accurately predicting new associations of miRNAs with diseases and their association types as well

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

EMT Network-based Lung Cancer Prognosis Prediction

Network-based feature selection methods on omics data have been developed in recent years. Their performance gain, however, is shown to be affected by the datasets, networks, and evaluation metrics. The reproducibility and robustness of biomarkers await to be improved. In this endeavor, one of the major challenges is the curse of dimensionality. To mitigate this issue, we proposed the Phenotype Relevant Network-based Feature Selection (PRNFS) framework. By employing a much smaller but phenotype relevant network, we could avoid irrelevant information and select robust molecular signatures. The advantages of PRNFS were demonstrated with the application of lung cancer prognosis prediction. Specifically, we constructed epithelial mesenchymal transition (EMT) networks and employed them for feature selection. We mapped multiple types of omics data on it alternatively to select single-omics signatures and further integrated them into multi-omics signatures. Then we introduced a multiplex network-based feature selection method to directly select multi-omics signatures. Both single-omics and multi-omics EMT signatures were evaluated on TCGA data as well as an independent multi-omics dataset. The results showed that EMT signatures achieved significant performance gain, although EMT networks covered less than 2.5% of the original data dimensions. Frequently selected EMT features achieved average AUC values of 0.83 on TCGA data. Employing EMT signatures on the independent dataset stratified the patients into significantly different prognostic groups. Multi-omics features showed superior performance over single-omics features on both TCGA data and the independent data. Additionally, we tested the performance of a few relational and non-relational databases for storing and retrieving omics data. Since biological data have large volume, high velocity, and wide varieties, it is necessary to have database systems that meet the need of integrative omics data analysis. Based on the results, we provided a few advices on building scalable omics data infrastructures

Computational methods for personalized cancer genomics

In recent years, cancer treatment strategies have moved towards personalized approaches, specifically tailoring cancer treatments on a single-patient basis using molecular profiles from the patients’ tumor genomes. Knowledge of a patient’s molecular profile can be used to 1) identify the disease mechanisms and underlying cause of a single patient’s cancer, 2) assign patients into treatment groups based on the molecular prognosis, and 3) recommend potential treatments for individual patients based on the patient’s molecular signature data. However, the bottleneck of the personalized medicine approach lies in the challenge of translating the vast amount of sequencing data to meaningful clinical insights. This dissertation explores several computational methods that utilize molecular signature data to understand disease mechanisms of cancer, categorize patients into biologically relevant subtypes, and recommend drug treatments to patients. In the dissertation, we present a method, DawnRank, a patient-specific method that determines the potential driving genomic alterations (the drivers) of cancer. We expand on DawnRank’s capabilities by using the DawnRank scores in key driver mutations and copy number variants (CNVs) to identify breast cancer subtypes. We found 5 alternative subtypes based on potentially clinically relevant driver genes, each with unique defining target features and pathways. These subtypes correspond to and build upon our previous knowledge of breast cancer subtypes. We also identify disease mechanisms in identifying key novel cancer pathways in which driver genes interact. We developed a method, C3, which pinpoints patterns of cancer mutations in a pathway context from a patient population to detect novel cancer pathways that consist of significant driver genes. C3 improves on current methods in driver pathway detection both on a technical aspect and a results-oriented aspect. C3 can detect larger and more consistent pathways than previous methods as well as discovering more biologically relevant drivers. Finally, we address the issue of drug recommendation in the wake of molecular signature data. We develop a method, Scattershot, which combines genomic information along with biological insights on cancer disease mechanisms to predict drug response and prioritize drug treatments. Scattershot outperforms previous methods in predicting drug response and produces recommendations that largely comply with known medical treatment protocols.Scattershot recommends drugs to cancer patients that are in line with the actual drugs prescribed by the physician

Statistical learning methods for multi-omics data integration in dimension reduction, supervised and unsupervised machine learning

Over the decades, many statistical learning techniques such as supervised learning, unsupervised learning, dimension reduction technique have played ground breaking roles for important tasks in biomedical research. More recently, multi-omics data integration analysis has become increasingly popular to answer to many intractable biomedical questions, to improve statistical power by exploiting large size samples and different types omics data, and to replicate individual experiments for validation. This dissertation covers the several analytic methods and frameworks to tackle with practical problems in multi-omics data integration analysis. Supervised prediction rules have been widely applied to high-throughput omics data to predict disease diagnosis, prognosis or survival risk. The top scoring pair (TSP) algorithm is a supervised discriminant rule that applies a robust simple rank-based algorithm to identify rank-altered gene pairs in case/control classes. TSP usually generates greatly reduced accuracy in inter-study prediction (i.e., the prediction model is established in the training study and applied to an independent test study). In the first part, we introduce a MetaTSP algorithm that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies. One important objective of omics data analysis is clustering unlabeled patients in order to identify meaningful disease subtypes. In the second part, we propose a group structured integrative clustering method to incorporate a sparse overlapping group lasso technique and a tight clustering via regularization to integrate inter-omics regulation flow, and to encourage outlier samples scattering away from tight clusters. We show by two real examples and simulated data that our proposed methods improve the existing integrative clustering in clustering accuracy, biological interpretation, and are able to generate coherent tight clusters. Principal component analysis (PCA) is commonly used for projection to low-dimensional space for visualization. In the third part, we introduce two meta-analysis frameworks of PCA (Meta-PCA) for analyzing multiple high-dimensional studies in common principal component space. Theoretically, Meta-PCA specializes to identify meta principal component (Meta-PC) space; (1) by decomposing the sum of variances and (2) by minimizing the sum of squared cosines. Applications to various simulated data shows that Meta-PCAs outstandingly identify true principal component space, and retain robustness to noise features and outlier samples. We also propose sparse Meta-PCAs that penalize principal components in order to selectively accommodate significant principal component projections. With several simulated and real data applications, we found Meta-PCA efficient to detect significant transcriptomic features, and to recognize visual patterns for multi-omics data sets. In the future, the success of data integration analysis will play an important role in revealing the molecular and cellular process inside multiple data, and will facilitate disease subtype discovery and characterization that improve hypothesis generation towards precision medicine, and potentially advance public health research