Genomic data is paving the way towards personalized healthcare. By unveiling genetic disease-contributing factors, genomic data can aid in the detection, diagnosis, and treatment of a wide range of complex diseases. Integrating genomic data into healthcare is riddled with a wide range of challenges spanning social, ethical, legal, educational, economic, and technical aspects. Bioinformatics is a core integration aspect presenting an overwhelming number of unaddressed challenges. In this paper we tackle the fundamental bioinformatics integration concerns including: genomic data generation, storage, representation, and utilization in conjunction with clinical data. We divide the bioinformatics challenges into a series of seven intertwined integration aspects spanning the areas of informatics, knowledge management, and communication. For each aspect, we provide a detailed discussion of the current research directions, outstanding challenges, and possible resolutions. This paper seeks to help narrow the gap between the genomic applications, which are being predominantly utilized in research settings, and the clinical adoption of these applications.

Download full-text PDF

Source
http://dx.doi.org/10.1109/JBHI.2017.2778263DOI Listing

Publication Analysis

Top Keywords

genomic data
16
bioinformatics challenges
8
wide range
8
challenges
5
genomic
5
data
5
understanding bioinformatics
4
challenges integrating
4
integrating genomics
4
genomics healthcare
4

Similar Publications

Exploring the Impact of Systemic Inflammatory Regulators on Rosacea Risk: A Bidirectional Mendelian Randomization Analysis.

Clin Cosmet Investig Dermatol

January 2025

Department of Dermatology, Changshu No. 1 People's Hospital, Changshu Hospital Affiliated to Soochow University, Changshu, Jiangsu, 215500, People's Republic of China.

Objective: Rosacea is a common chronic inflammatory disorder primarily affecting the face. While inflammatory factors are known to play a pivotal role in its pathogenesis, their causal relationship with rosacea remains unclear. This study employed a two-sample bidirectional Mendelian randomization (MR) analysis to investigate the causal links between systemic inflammatory regulators and rosacea.

View Article and Find Full Text PDF

The strong correlation between reproductive life cycle type and chromosome numbers in green plants has been a long-standing mystery in evolutionary biology. Within green plants, the derived condition of heterosporous reproduction has emerged from the ancestral condition of homospory in disparate locations on the phylogenetic tree at least 11 times, of which three lineages are extant. In all green plant lineages where heterospory has emerged, there has been a significant downsizing in chromosome numbers.

View Article and Find Full Text PDF

Introduction: The benefits of sharing participant-level data, including clinical or epidemiological data, genomic data, high-dimensional imaging data, or human-derived samples, from biomedical studies have been widely touted and may be taken for granted. As investments in data sharing and reuse efforts continue to grow, understanding the cost and positive and negative effects of data sharing for research participants, the general public, individual researchers, research and development, clinical practice, and public health is of growing importance. In this scoping review, we will identify and summarize existing evidence on the positive and negative impacts and costs of data sharing and how they are measured.

View Article and Find Full Text PDF

Background: Mitochondrial genes are involved in tumor metabolism in ovarian cancer (OC) and affect immune cell infiltration and treatment responses.

Aim: To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.

Methods: Prognosis, immunotherapy efficacy, and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus.

View Article and Find Full Text PDF

Disentangling protein metabolic costs in human cells and tissues.

PNAS Nexus

January 2025

Logic of Genomic Systems Laboratory (CNB-CSIC), Madrid E-28049, Spain.

While more data are becoming available on gene activity at different levels of biological organization, our understanding of the underlying biology remains incomplete. Here, we introduce a metabolic efficiency framework that considers highly expressed proteins (HEPs), their length, and biosynthetic costs in terms of the amino acids (AAs) they contain to address the observed balance of expression costs in cells, tissues, and cancer transformation. Notably, the combined set of HEPs in either cells or tissues shows an abundance of large and costly proteins, yet tissues compensate this with short HEPs comprised of economical AAs, indicating a stronger tendency toward mitigating costs.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!