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.