An integrative and comparative study of pan-cancer transcriptomes reveals distinct cancer common and specific signatures.

To investigate the commonalities and specificities across tumor lineages, we perform a systematic pan-cancer transcriptomic study across 6744 specimens. We find six pan-cancer subnetwork signatures which relate to cell cycle, immune response, Sp1 regulation, collagen, muscle system and angiogenesis. Moreover, four pan-cancer subnetwork signatures demonstrate strong prognostic potential.

IRWRLDA: improved random walk with restart for lncRNA-disease association prediction.

In recent years, accumulating evidences have shown that the dysregulations of lncRNAs are associated with a wide range of human diseases. It is necessary and feasible to analyze known lncRNA-disease associations, predict potential lncRNA-disease associations, and provide the most possible lncRNA-disease pairs for experimental validation. Considering the limitations of traditional Random Walk with Restart (RWR), the model of Improved Random Walk with Restart for LncRNA-Disease Association prediction (IRWRLDA) was developed to predict novel lncRNA-disease associations by integrating known lncRNA-disease associations, disease semantic similarity, and various lncRNA similarity measures.

An effective sequence-alignment-free superpositioning of pairwise or multiple structures with missing data.

Background: Superpositioning is an important problem in structural biology. Determining an optimal superposition requires a one-to-one correspondence between the atoms of two proteins structures. However, in practice, some atoms are missing from their original structures.

DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.

Motivation: Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets.

High-dimensional genomic data bias correction and data integration using MANCIE.

High-dimensional genomic data analysis is challenging due to noises and biases in high-throughput experiments. We present a computational method matrix analysis and normalization by concordant information enhancement (MANCIE) for bias correction and data integration of distinct genomic profiles on the same samples. MANCIE uses a Bayesian-supported principal component analysis-based approach to adjust the data so as to achieve better consistency between sample-wise distances in the different profiles.

ILNCSIM: improved lncRNA functional similarity calculation model.

Increasing observations have indicated that lncRNAs play a significant role in various critical biological processes and the development and progression of various human diseases. Constructing lncRNA functional similarity networks could benefit the development of computational models for inferring lncRNA functions and identifying lncRNA-disease associations. However, little effort has been devoted to quantifying lncRNA functional similarity.

A systematic method to identify modulation of transcriptional regulation via chromatin activity reveals regulatory network during mESC differentiation.

Chromatin regulators (CRs) are crucial for connecting the chromatin level and transcriptome level by modulating chromatin structures, establishing, and maintaining epigenetic modifications. We present a systematic method to identify MOdulation of transcriptional regulation via CHromatin Activity (MOCHA) from gene expression data and demonstrate its advantage in associating CRs to their chromatin localization and understand CRs' function. We first re-construct the CRs modulation network by integrating the correlation and conditional correlation concepts.

WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.

Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Predicting potential miRNA-disease associations by integrating various heterogeneous biological datasets is of great significance to the biomedical research.

Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data.

Motivation: The underlying relationship between genomic factors and the response of diverse cancer drugs still remains unclear. A number of studies showed that the heterogeneous responses to anticancer treatments of patients were partly associated with their specific changes in gene expression and somatic alterations. The emerging large-scale pharmacogenomic data provide us valuable opportunities to improve existing therapies or to guide early-phase clinical trials of compounds under development.

Revealing Potential Biomarkers of Functional Dyspepsia by Combining 1H NMR Metabonomics Techniques and an Integrative Multi-objective Optimization Method.

Metabonomics methods have gradually become important auxiliary tools for screening disease biomarkers. However, recognition of metabolites or potential biomarkers closely related to either particular clinical symptoms or prognosis has been difficult. The current study aims to identify potential biomarkers of functional dyspepsia (FD) by a new strategy that combined hydrogen nuclear magnetic resonance ((1)H NMR)-based metabonomics techniques and an integrative multi-objective optimization (LPIMO) method.

miREFRWR: a novel disease-related microRNA-environmental factor interactions prediction method.

Increasing evidence has indicated that microRNAs (miRNAs) can functionally interact with environmental factors (EFs) to affect and determine human diseases. Uncovering the potential associations between diseases and miRNA-EF interactions could benefit the understanding of the underlying disease mechanism at miRNA and EF levels, miRNA signatures identification, and drug repurposing. In this study, based on the assumption that similar miRNAs (EFs) tend to interact with similar EFs (miRNAs) in the context of a given disease and under the framework of random walk with restart (RWR), a novel method of miREFRWR was developed to uncover the hidden disease-related miRNA-EF interactions by implementing random walks on an miRNA similarity network and EF similarity network, respectively.

KATZLDA: KATZ measure for the lncRNA-disease association prediction.

Accumulating experimental studies have demonstrated important associations between alterations and dysregulations of lncRNAs and the development and progression of various complex human diseases. Developing effective computational models to integrate vast amount of heterogeneous biological data for the identification of potential disease-lncRNA associations has become a hot topic in the fields of human complex diseases and lncRNAs, which could benefit lncRNA biomarker detection for disease diagnosis, treatment, and prevention. Considering the limitations in previous computational methods, the model of KATZ measure for LncRNA-Disease Association prediction (KATZLDA) was developed to uncover potential lncRNA-disease associations by integrating known lncRNA-disease associations, lncRNA expression profiles, lncRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity.

Data Analysis Strategies for Protein Modification Identification.

Mass spectrometry-based proteomics provides a powerful tool for large-scale analysis of protein modifications. Statistical and computational analysis of mass spectrometry data is a key step in protein modification identification. This chapter presents common and advanced data analysis strategies for modification identification, including variable modification search, unrestrictive approaches for modification discovery, false discovery rate estimation and control methods, and tools for modification site localization.

Computational probing protein-protein interactions targeting small molecules.

Motivation: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein-protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects.

RBMMMDA: predicting multiple types of disease-microRNA associations.

Accumulating evidences have shown that plenty of miRNAs play fundamental and important roles in various biological processes and the deregulations of miRNAs are associated with a broad range of human diseases. However, the mechanisms underlying the dysregulations of miRNAs still have not been fully understood yet. All the previous computational approaches can only predict binary associations between diseases and miRNAs.

Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA.

Accumulating experimental studies have indicated that lncRNAs play important roles in various critical biological process and their alterations and dysregulations have been associated with many important complex diseases. Developing effective computational models to predict potential disease-lncRNA association could benefit not only the understanding of disease mechanism at lncRNA level, but also the detection of disease biomarkers for disease diagnosis, treatment, prognosis and prevention. However, known experimentally confirmed disease-lncRNA associations are still very limited.

Tumor characterization and stratification by integrated molecular profiles reveals essential pan-cancer features.

Background: Identification of tumor heterogeneity and genomic similarities across different cancer types is essential to the design of effective stratified treatments and for the discovery of treatments that can be extended to different types of tumors. However, systematic investigations on comprehensive molecular profiles have not been fully explored to achieve this goal.

Results: Here, we performed a network-based integrative pan-cancer genomic analysis on >3000 samples from 12 cancer types to uncover novel stratifications among tumors.

NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.

Motivation: In prognosis and survival studies, an important goal is to identify multi-biomarker panels with predictive power using molecular characteristics or clinical observations. Such analysis is often challenged by censored, small-sample-size, but high-dimensional genomic profiles or clinical data. Therefore, sophisticated models and algorithms are in pressing need.

A note on the false discovery rate of novel peptides in proteogenomics.

Motivation: Proteogenomics has been well accepted as a tool to discover novel genes. In most conventional proteogenomic studies, a global false discovery rate is used to filter out false positives for identifying credible novel peptides. However, it has been found that the actual level of false positives in novel peptides is often out of control and behaves differently for different genomes.

Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity.

Increasing evidence has indicated that plenty of lncRNAs play important roles in many critical biological processes. Developing powerful computational models to construct lncRNA functional similarity network based on heterogeneous biological datasets is one of the most important and popular topics in the fields of both lncRNAs and complex diseases. Functional similarity network construction could benefit the model development for both lncRNA function inference and lncRNA-disease association identification.

A novel mixed integer programming for multi-biomarker panel identification by distinguishing malignant from benign colorectal tumors.

Multi-biomarker panels can capture the nonlinear synergy among biomarkers and they are important to aid in the early diagnosis and ultimately battle complex diseases. However, identification of these multi-biomarker panels from case and control data is challenging. For example, the exhaustive search method is computationally infeasible when the data dimension is high.

Systematic DNA methylation analysis of multiple cell lines reveals common and specific patterns within and across tissues of origin.

DNA methylation is a key functional regulatory mechanism in human genome, which plays critical roles in development, differentiation and many diseases. With rapid progress of large-scale projects (e.g.

Mapping native disulfide bonds at a proteome scale.

We developed a high-throughput mass spectrometry method, pLink-SS (http://pfind.ict.ac.

A community computational challenge to predict the activity of pairs of compounds.

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing.