Molecular signatures from omics data: From chaos to consensus

In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.     

The emergence of molecular profiling and omics techniques in seagrass biology; furthering our understanding of seagrasses

Seagrass meadows are disappearing at alarming rates as a result of increasing coastal development and climate change. The emergence of omics and molecular profiling techniques in seagrass research is timely, providing a new opportunity to address such global issues. Whilst these applications have transformed terrestrial plant research, they have only emerged in seagrass research within the past decade; In this time frame we have observed a significant increase in the number of publications in this nascent field, and as of this year the first genome of a seagrass species has been sequenced. In this review, we focus on the development of omics and molecular profiling and the utilization of molecular markers in the field of seagrass biology. We highlight the advances, merits and pitfalls associated with such technology, and importantly we identify and address the knowledge gaps, which to this day prevent us from understanding seagrasses in a holistic manner. By utilizing the powers of omics and molecular profiling technologies in integrated strategies, we will gain a better understanding of how these unique plants function at the molecular level and how they respond to on-going disturbance and climate change events.     

Novel molecular insights and public omics data in pulmonary hypertension

Pulmonary hypertension is a rare disease with high morbidity and mortality which mainly affects women of reproductive age. Despite recent advances in understanding the pathogenesis of pulmonary hypertension, the high heterogeneity in the presentation of the disease among different patients makes it difficult to make an accurate diagnosis and to apply this knowledge to effective treatments. Therefore, new studies are required to focus on translational and personalized medicine to overcome the lack of specificity and efficacy of current management. Here, we review the majority of public databases storing ‘omics’ data of pulmonary hypertension studies, from animal models to human patients. Moreover, we review some of the new molecular mechanisms involved in the pathogenesis of pulmonary hypertension, including non-coding RNAs and the application of ‘omics’ data to understand this pathology, hoping that these new approaches will provide insights to guide the way to personalized diagnosis and treatment.     

From cancer big data to treatment: Artificial intelligence in cancer research

In recent years, developing the idea of “cancer big data” has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.     

Plant genome sequencing: applications for crop improvement

DNA sequencing technology is undergoing a revolution with the commercialization of second generation technologies capable of sequencing thousands of millions of nucleotide bases in each run. The data explosion resulting from this technology is likely to continue to increase with the further development of second generation sequencing and the introduction of third generation single‐molecule sequencing methods over the coming years. The question is no longer whether we can sequence crop genomes which are often large and complex, but how soon can we sequence them? Even cereal genomes such as wheat and barley which were once considered intractable are coming under the spotlight of the new sequencing technologies and an array of new projects and approaches are being established. The increasing availability of DNA sequence information enables the discovery of genes and molecular markers associated with diverse agronomic traits creating new opportunities for crop improvement. However, the challenge remains to convert this mass of data into knowledge that can be applied in crop breeding programs.     

整合分析多组学数据筛选疾病靶点的精准医学策略

随着高通量测序技术的不断发展与完善,对于不同层次和类型的生物组学数据的获取及分析方法也日趋成熟与完善。基于单组学数据的疾病研究已经发现了诸多新的疾病相关因子,而整合多组学数据研究疾病靶点的工作方兴未艾。生命体是一个复杂的调控系统,疾病的发生与发展涉及基因变异、表观遗传改变、基因表达异常以及信号通路紊乱等诸多层次的复杂调控机制,利用单一组学数据分析致病因子的局限性愈发显著。通过对多种层次和来源的高通量组学数据的整合分析,系统地研究临床发病机理、确定最佳疾病靶点已经成为精准医学研究的重要发展方向,将为疾病研究提供新的思路,并对疾病的早期诊断、个体化治疗和指导用药等提供新的理论依据。本文详细介绍了基因组、转录组和表观组等系统组学研究在疾病靶点筛选方面出现的新技术手段和研究进展,并对它们之间的整合分析新策略和优势进行了讨论。     

Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants

The evolution of “informatics” technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high‐dimensionality of omics‐type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. In this article, we present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the treatment predictive and disease prognostic characteristics of a particular patient's disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers, and hence properly utilizes these complementary sources of information for treatment selection. The methodology is illustrated through a case study of lower grade glioma. Theoretical considerations are explored to demonstrate the manner in which treatment selection is impacted by prognostic features. Additionally, simulations based on an actual leukemia study are provided to ascertain the method's performance with respect to selection rules derived from competing methods.