두 종류의 논문을 읽어 보았다.
<P Sethi et al., Translational Bioinformatics and Healthcare Informatics: Computational and Ethical Challenges (2009), Perspectives in Health Information Management>
The interdisciplinary area of bioinformatics, which involves managing, analyzing, and interpreting information from biological
sequences and structures, has opened doors for sophisticated technology that continues to support the
automation and miniaturization of modern instruments that bear large-scale biomedical data.
The fundamental challenge for genomics is to determine
how gene variations are linked to a certain disease and, on a broader perspective, to determine how the
interactions of genes vary with environment and lifestyle.
New data models and structures that can integrate genomic data in EHRs as well as interface
mechanisms that can link genotype-phenotype data are needed to realize the benefits of new genome-based technologies for the benefits of patients.
We summarize our key conclusions as follows:
- Significant strides have already been achieved in genomic- and proteomic-level data mining
methods. These accomplishments have promoted the understanding of the genes associated with
certain diseases and the identification of new target drugs. - A number of international and national workgroups and committees are promoting the inclusion
of genetic information in EHRs. These efforts will have a broad impact on personalized medicine. - Congress has passed GINA to protect citizens against discrimination based on their genetic information.
- The adoption of sound privacy and security policies is imperative to protect the personalized
nature of genomic data. The current incarnation of GINA has loopholes that may bar citizens
from obtaining their genetic tests and consequently affect biomedical research. - Standards that enable interoperability standards among different formats of health records for
genetic and clinical data are needed to realize the benefits of new genome-based technologies. - A collaborative research agenda for translational bioinformatics and healthcare informatics
should be prioritized. Such a synergistic approach will promote faster and more advanced
breakthroughs in medicine and healthcare.
Approximately 10 percent of pharmacy labels already contain information on how the drug will respond
to an individual‘s genetic variation.
Involving genetic data in addition to clinical data for medical decision making will lead to a paradigm shift toward evidence-based research—a realization for which scientists are hoping.
<V Kuznetsov et al., How bioinformatics influences health informatics: usage of biomolecular sequences, expression profiles and automated microscopic image analyses for clinical needs and public health (2013), Health Information Science and Systems
>
It remains a sad fact that many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known and, thus, most of the molecular and cellular data cannot be interpreted in terms of biomedically relevant conclusions.
The temperate view suggests that biomedical applications that rely either on the comparison of biomolecular sequences and/or on the already known biomolecular mechanisms have much greater chances to enter clinical practice soon.
Since the analysis of heterogeneous biomolecular data in context with clinical data will be increasingly critical, the role of
bioinformatics and computational biology will grow correspondingly in this process.
-> Clinical data 를 biomolecular data와 함께 분석하는 것이 중요할 것 같다.
The current deluge of quantified data is really a game changer and puts theoretical analysis detached from experimentation into general importance for the field for the first time.
In fact, histologically similar cancers do not necessarily represent the same disease due to differences in the biomolecular mechanisms leading finally to similar clinical outcomes. Consequently, among the list of 10 most important human diseases, the pharmacotherapy efficacy of cancer is very low except for a few rare subtypes.
So, it is the task of future bioinformatics projects to develop accurate and flexible solutions for clinical applications.
Worldwide, cancers are responsible for one in eight deaths.
Current clinical oncology needs
(i) improvement of disease classification,
(ii) increased specificity and sensitivity of early detection instruments/molecular diagnostics systems,
(iii) improved disease risk profiling/prediction,
(iv) improvement of cancer therapeutic methods including next generation drugs with higher specificity and lowered toxicity (ideally, inhibitors of the exact biomolecular mechanisms that drive individual cancer growth) and generally more stratified or even personalized therapies,
(v) understanding of the anticancer immune response,
(vi) adequate monitoring and rehabilitation during post-treatment recovery period and
(viii) patients’ social adaptation.
For practical purposes, it is sufficient to show a close correlation between the occurrence of the biomarker and the cancer type and development in model systems and in clinical trials. Yet, the likelihood of the biomarker actually being associated with the cancer subtype considered is dramatically increased if the biomarker plays a role in the biomolecular mechanisms driving cancer and not just in some secondary or tertiary effects of cancer growth. However, discovery of reliable diagnostic, prognostic and drug response cancer biomarkers faces big challenges due to patient heterogeneity, small sample sizes, and high data noises.
Bioinformatics findings can be translated into innovations that are adopted by the healthcare system and biomedical industry in form of diagnostic kits, analysis programs, etc. after the validation in both bench and clinical studies. In this article, we present several examples of how clinically relevant conclusions can be drawn from sequencing, expression profiling or histopathological bioimaging data with computational biology algorithms.
Unfortunately, considerable basic research is still necessary to make full use of the potential opportunities that are associated with the increasing availability of high-throughput technologies such as genome sequencing, mainly since most of the genome’s hidden functional information is not known; the understanding of biomolecular mechanisms that translate genotype into phenotype is limited. But the progress in this field is uneven; pathogen sequencing can already provide important insights in contrast, for example, to sequencing of cancer samples.
Similarly, analyzing the geographic, even better spatio-temporal distribution of disease occurrences can provide hints for environmental influences. Generally, going beyond the patient-centric approach and the linking of biomolecular and clinical data of populations with geographic information, data on food and environment, etc. will be an important source for improving public health, for stopping epidemics, for finding sources of food or environmental poisoning and for improving life styles.
