Computational advances in cancer informatics (a).

Supplement Aims and Scope

Cancer Informatics represents a hybrid discipline encompassing the fields of oncology, computer science, bioinformatics, statistics, computational biology, genomics, proteomics, metabolomics, pharmacology, and quantitative epidemiology. The common bond or challenge that unifies the various disciplines is the need to bring order to the massive amounts of data generated by researchers and clinicians attempting to find the underlying causes and effective means of treating cancer.

The future cancer informatician will need to be well-versed in each of these fields and have the appropriate background to leverage the computational, clinical, and basic science resources necessary to understand their data and separate signal from noise. Knowledge of and the communication among these specialty disciplines, acting in unison, will be the key to success as we strive to find answers underlying the complex and often puzzling diseases known as cancer.

Authors of articles in this supplement were asked to focus on computational advances, including one or more of the following topics:

▪ Gene Set Enrichment Analysis

▪ Hybrid Computing

▪ Efficient Cloud Storage and Retrieval

▪ Matching of Expression Patterns

▪ Multi-Modal Analysis

▪ Splice Variations and Chip Seq System Algorithms

▪ Rapid High-Throughput Analysis

▪ Computational Molecular Profiling

▪ Digital Gene Expression Analysis

▪ De Novo Genome Assembly and Re-Sequencing Computational Methods

▪ Computational Drug Repurposing

▪ Accelerated Next Generation Sequencing Technologies and Search Engines

▪ Post-Transcriptional Pattern Recognition

▪ Computational Advances in High-Content Platform Analysis

▪ Data visualization Software Development

▪ Machine Learning and Integrative Computer Approaches for Cancer Omics Research

Computational method is playing an increasingly more important role in cancer research. The advances of text mining1–3, microRNA4,5, pathway analysis6,7, and whole genome sequencing8 shed light on improving medical practices using fine-grained information of individual patients but there is a long way to go towards personalized medical practices. We still cannot provide optimal treatment for many cancer patients9 and we have not accurately identified relationships between molecular subtypes and prognosis and treatment response. To improve treatment, we need to learn which pathways are altered in a given cancer, determine how they are changed, identify therapeutic targets on the pathways, and discover therapies that can reverse the damage.

Biomedical science is entering a “big data” era, and the catalogue of genomic variants in the human population is expanding rapidly in the decades to come. There has been an explosion of new genomic and proteomic datasets, which provide us with unprecedented and rich resources to discover the underlying mechanisms. There are also abundant data concerning SNPs, somatic mutations, copy number, methylation levels, and expression levels in cancerous and noncancerous tissue. To fully exploit these data, we need advanced biomedical informatics methodology that can extract useful knowledge efficiently. The main objective of this special issue is to bring researchers together from different areas of cancer informatics to exchange ideas, disseminate novel research methodologies, and promote cross-disciplinary collaborations. The issue is therefore broad to cover various aspects of informatics and medical analyses, a unique combination that is appreciated by researchers in the field. Just to highlight a few articles, Kim et al systematically compare different feature selection and predictive models to identify a set of highly predictive features to predict novel pre-miRNAsin renal cancer study. Neapolitan et al use Bayesian networks to infer aberrant signaling pathways in ovarian cancer using The Cancer Genome Atlas (TCGA) data. Hua et al evaluate gene set enrichment analysis via a hybrid model. Lu et al integrate protein phosphorylation and gene expression data to infer signaling pathways.

It is more imperative than ever to work together in cancer informatics to reveal insightful biological functions and their underlying mechanisms. We appreciate the opportunity to lead and contribute to this special issue.

Lead Guest Editor Dr Xiaoqian Jiang

Dr. Xiaoqian Jiang is an assistant professor of biomedical informatics at the University of California San Diego. He completed his PhD in Computer Science at Carnegie Mellon University and has previously worked at Mitsubishi Electrical Research Laboratory. He is an associate editor of BMC Medical Informatics and Decision Making. He now works primarily in health data privacy and predictive models in biomedicine. Dr. Jiang is the author or co-author of 57 published papers and has presented at 17 conferences.

Guest Editors

RUI CHEN

Dr. Rui Chen is a research assistant professor of computer science at Hong Kong Baptist University. He completed his PhD at Concordia University and has previously worked at the University of British Columbia and INRIA Grenoble Rhone-Alpes. His primary research interests lie in databases, data mining and data privacy. Dr. Chen is the author or co-author of eight published papers and has 15 conference papers. Dr. Chen has been committee members for five conferences and served as external reviewers for numerous leading journals and conferences.

SAMUEL CHENG

Dr. Samuel Cheng is an associate professor of electrical and computing engineering at the University of Oklahoma. He completed his PhD at Texas A&M University and has previously worked at Microsoft Asia, Panasonic Technologies Company and Advanced Digital Imaging Research. He now works primarily in signal and image processing, and information theory. Dr. Cheng is the author or co-author of 39 published papers and has 92 conference papers. Dr. Cheng has several patent submissions and has been awarded five US patents.

XIA JIANG

Dr. Xia Jiang is an assistant professor of biomedical informatics at the University of Pittsburgh. She received her PhD from School of Medicine at the University of Pittsburgh. She is now a principal investigator of NIH/NLM funded projects on the development of a clinical decision support system in breast cancer, and epistasis learning of cancer genome data. Dr. Jiang is the author or co-author of 34 peer-reviewed scientific publications, and has given over 10 conference presentations and invited talks. She is the co-author of the books “Probabilistic Methods for Financial and Marketing Informatics” and “Contemporary Artificial Intelligence.”

BAIRONG SHEN

Dr. Bairong Shen is a professor of systems biology at Soochow University. He completed his PhD at Fudan University and has previously worked at Suzhou Medical College, Fudan University, and the University of Tampere. He now works primarily in bioinformatics. Dr. Shen is the author or co-author of 73 published papers and has presented at more than 10 conferences.

RONG XU

Dr. Rong Xu is an assistant professor in the Division of Medical Informatics at Case Western Reserve University. She completed her PhD at Stanford University. Her current research focusses on facilitating biomedical discovery and promoting efficient communication between biomedical researchers, physicians and patients to improve health care delivery. Dr. Xu is the author or co-author of 14 published papers and has 27 conference papers.

SONG YI

Dr. Song Yi is a research fellow of genetics at Harvard Medical School. He completed his PhD at the University of Iowa. He now works primarily in systems biology. Dr. Yi is the author or co-author of 15 high-impact published scientific papers in his field and has presented his work at over 20 conferences. Notably, his findings have been highlighted two times by the world-renowned journal Nature as a significant scientific breakthrough in biology. Dr. Yi’s work has been collectively cited more than 400 times, and is internationally recognized. He holds editorial appointments at the Universal Journal of Microbiology Research.