Literature mining in support of drug discovery

The drug discovery enterprise provides strong drivers for data integration. While attention in this arena has tended to focus on integration of primary data from omics and other large platform technologies contributing to drug discovery and development, the scientific literature remains a major source of information valuable to pharmaceutical enterprises, and therefore tools for mining such data and integrating it with other sources are of vital interest and economic impact. This review provides a brief overview of approaches to literature mining as they relate to drug discovery, and offers an illustrative case study of a 'lightweight' approach we have implemented within an industrial context.     

Conceptual biology,hypothesis discovery,and text mining: Swanson's legacy

Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians.     

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper.     

Biomedical applications of protein chips

The development of microchips involving proteins has accelerated within the past few years. Although DNA chip technologies formed the precedent, many different strategies and technologies have been used because proteins are inherently a more complex type of molecule. This review covers the various biomedical applications of protein chips in diagnostics, drug screening and testing, disease monitoring, drug discovery (proteomics), and medical research. The proteomics and drug discovery section is further subdivided to cover drug discovery tools (on-chip separations, expression profiling, and antibody arrays), molecular interactions and signaling pathways, the identification of protein function, and the identification of novel therapeutic compounds. Although largely focused on protein chips, this review includes chips involving cells and tissues as a logical extension of the type of data that can be generated from these microchips.     

New strategies and approaches for engineering biosynthetic gene clusters of microbial natural products

With the rapidly growing number of sequenced microbial (meta)genomes, enormous cryptic natural product (NP) biosynthetic gene clusters (BGCs) have been identified, which are regarded as a rich reservoir for novel drug discovery. A series of powerful tools for engineering BGCs has accelerated the discovery and development of pharmaceutically active NPs. Here, we describe recent advances in the strategies for BGCs manipulation, which are driven by emerging technologies, including efficient DNA recombination systems, versatile CRISPR/Cas9 genome editing tools and diverse DNA assembly methods. We further discuss how these approaches could be used for genome mining studies and industrial strain improvement.     

Literature mining for the biologist: from information retrieval to biological discovery

For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.     

Biotechnology match making: screening orphan ligands and receptors

To date there has been a considerable amount of interest and success in the pharmaceutical industry in the discovery of drug targets and diagnostics via genomic technologies, namely DNA sequencing, mutation/polymorphism detection and expression monitoring of mRNA. As the ultimate targets for the majority of these methods are actually proteins, more and more emphasis has been placed upon protein-based methods in an effort to define the function of proteins discovered by genomic technologies. One of the most challenging areas of drug target discovery facing researchers today is the search for novel receptor-ligand pairs. Database mining techniques in conjunction with other computational methods are able to identify many novel sequences of putative receptors, but the ability to similarly identify the receptor's natural ligand is not possible by these methods. The past few years have seen an increase in methodology and instrumentation focused on the ability to discover and characterize protein-protein interactions, as well as receptor-ligand pairs. Significant advances have been made in the areas of instrumentation (biosensors and fluorescent plate readers) as well as methodologies relating to phage/ribosome display and library construction.     

Life Sciences and the web: a new era for collaboration

The World Wide Web has revolutionized how researchers from various disciplines collaborate over long distances. This is nowhere more important than in the Life Sciences, where interdisciplinary approaches are becoming increasingly powerful as a driver of both integration and discovery. Data access, data quality, identity, and provenance are all critical ingredients to facilitate and accelerate these collaborative enterprises and it is here where Semantic Web technologies promise to have a profound impact. This paper reviews the need for, and explores advantages of as well as challenges with these novel Internet information tools as illustrated with examples from the biomedical community.     

Perspectives with cryogenic RF probes in biomedical MRI

Since discovery of high-temperature superconductive (HTS) ceramics by Bednorz and Muller in 1986, there has been an accelerated development of cold technologies in industry, including the domain of NMR detection. The purpose of this paper is to fix ideas about the stage that cryogenic radio frequency (RF) probe techniques have reached in biomedical magnetic resonance imaging (MRI). Readers confronted to the literature about this emerging topic have to understand a large range of motivations with somewhat unclearly defined technical limitations and actual outlets. An overview of sensitivity issues in the general context of biomedical MRI is provided here and the contribution of RF coil techniques to recent advances is identified. The domains where cooled coil materials such as copper, low- or high-temperature superconductors, could actually increase the RF coil sensitivity are delimited by a quantitative analysis of noise mechanisms. Technical keys, cryogenic means and cold RF coil technologies are considered, and first achievements in different fields of biomedical MRI are reviewed. This survey provides a basis for discussing about the future impact of cryogenic probes for MRI investigations.     

The re-emerging role of microbial natural products in antibiotic discovery

New classes of antibacterial compounds are urgently needed to respond to the high frequency of occurrence of resistances to all major classes of known antibiotics. Microbial natural products have been for decades one of the most successful sources of drugs to treat infectious diseases but today, the emerging unmet clinical need poses completely new challenges to the discovery of novel candidates with the desired properties to be developed as antibiotics. While natural products discovery programs have been gradually abandoned by the big pharma, smaller biotechnology companies and research organizations are taking over the lead in the discovery of novel antibacterials. Recent years have seen new approaches and technologies being developed and integrated in a multidisciplinary effort to further exploit microbial resources and their biosynthetic potential as an untapped source of novel molecules. New strategies to isolate novel species thought to be uncultivable, and synthetic biology approaches ranging from genome mining of microbial strains for cryptic biosynthetic pathways to their heterologous expression have been emerging in combination with high throughput sequencing platforms, integrated bioinformatic analysis, and on-site analytical detection and dereplication tools for novel compounds. These different innovative approaches are defining a completely new framework that is setting the bases for the future discovery of novel chemical scaffolds that should foster a renewed interest in the identification of novel classes of natural product antibiotics from the microbial world.     

蛋白质组学技术在网络药理学中的应用

蛋白质组学发展至今已日趋成熟,在生物医药相关领域研究中的应用显著增加,与之相关的样品制备技术、蛋白定量方法及先进的质谱仪器也得到了快速发展。网络药理学是近年来提出的新药发现新策略,是药理学的新兴分支学科,它从整体的角度探索药物与疾病的关联性,发现药物靶标,指导新药研发。将蛋白质组学技术应用于网络药理学研究,能使研究人员系统地预测和解释药物的作用,加速药物靶点的确认,从而设计多靶点药物或药物组合。综述了蛋白质组学技术的新近研究进展,并简单概述了其在网络药理学中的应用。     

BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation

We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information. The platform offers prioritizations of putative disease genes, supported by functional hypotheses. We show that BioGraph can retrospectively confirm recently discovered disease genes and identify potential susceptibility genes, outperforming existing technologies, without requiring prior domain knowledge. Additionally, BioGraph allows for generic biomedical applications beyond gene discovery. BioGraph is accessible at .     

Online tools to support literature-based discovery in the life sciences

In biomedical research, the amount of experimental data and published scientific information is overwhelming and ever increasing, which may inhibit rather than stimulate scientific progress. Not only are text-mining and information extraction tools needed to render the biomedical literature accessible but the results of these tools can also assist researchers in the formulation and evaluation of novel hypotheses. This requires an additional set of technological approaches that are defined here as literature-based discovery (LBD) tools. Recently, several LBD tools have been developed for this purpose and a few well-motivated, specific and directly testable hypotheses have been published, some of which have even been validated experimentally. This paper presents an overview of recent LBD research and discusses methodology, results and online tools that are available to the scientific community.     

New approaches to antibiotic discovery

New antibiotics are urgently required by human medicine as pathogens emerge with developed resistance to almost all antibiotic classes. Pioneering approaches, methodologies and technologies have facilitated a new era in antimicrobial discovery. Innovative culturing techniques such as iChip and co-culturing methods which use ‘helper’ strains to produce bioactive molecules have had notable success. Exploiting antibiotic resistance to identify antibacterial producers performed in tandem with diagnostic PCR based identification approaches has identified novel candidates. Employing powerful metagenomic mining and metabolomic tools has identified the antibiotic’ome, highlighting new antibiotics from underexplored environments and silent gene clusters enabling researchers to mine for scaffolds with both a novel mechanism of action and also few clinically established resistance determinants. Modern biotechnological approaches are delivering but will require support from government initiatives together with changes in regulation to pave the way for valuable, efficacious, highly targeted, pathogen specific antimicrobial therapies.     

文本挖掘在生物医学领域中的应用文献计量学分析

目的:近年来,随着生物医学领域文献数量的急骤增长,大量隐含的规律和新知被掩埋在浩如烟海的文献之中,而将文本挖掘技术应用于生物医学领域则可以对海量生物医学文献数据进行整合、分析,从而获得有价值的信息,提高人们对生物医学现象的认识。本文就我国近十年来文本挖掘技术在生物医学领域的应用现状进行文献计量学分析,旨在为我国科研工作者对该领域的进一步研究提供参考。方法:对国内正式发表的生物医学领域文本挖掘相关文献进行检索和筛选,分别从年度变化、地区分布、研究机构、期刊来源、研究领域等方面进行分析。结果:国内生物医学文本挖掘文献总量呈上升趋势,主要集中在挖掘算法的研究和文本挖掘技术在中医药及系统生物学领域的应用方面;北京、上海、广东等地的研究处于领先地位。结论:相比其他较为成熟的研究课题来说,目前文本挖掘技术在生物医学中的应用在国内还属于一个比较新的研究领域,但国内对该领域的认识正不断提高、研究正不断深入,初步形成了一批在该领域的核心研究地区、核心研究机构和核心研究领域,而对其进一步的研究,必将为生物医学领域的发展注入新的活力。     

Text mining and ontologies in biomedicine: making sense of raw text

The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.     

Microbial genome mining for accelerated natural products discovery: is a renaissance in the making?

Microbial genome mining is a rapidly developing approach to discover new and novel secondary metabolites for drug discovery. Many advances have been made in the past decade to facilitate genome mining, and these are reviewed in this Special Issue of the Journal of Industrial Microbiology and Biotechnology. In this Introductory Review, we discuss the concept of genome mining and why it is important for the revitalization of natural product discovery; what microbes show the most promise for focused genome mining; how microbial genomes can be mined; how genome mining can be leveraged with other technologies; how progress on genome mining can be accelerated; and who should fund future progress in this promising field. We direct interested readers to more focused reviews on the individual topics in this Special Issue for more detailed summaries on the current state-of-the-art.     

Terminology-driven mining of biomedical literature

MOTIVATION: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective literature mining techniques that can help biologists to gather and make use of the knowledge encoded in text documents. Although the knowledge is organized around sets of domain-specific terms, few literature mining systems incorporate deep and dynamic terminology processing. RESULTS: In this paper, we present an overview of an integrated framework for terminology-driven mining from biomedical literature. The framework integrates the following components: automatic term recognition, term variation handling, acronym acquisition, automatic discovery of term similarities and term clustering. The term variant recognition is incorporated into terminology recognition process by taking into account orthographical, morphological, syntactic, lexico-semantic and pragmatic term variations. In particular, we address acronyms as a common way of introducing term variants in biomedical papers. Term clustering is based on the automatic discovery of term similarities. We use a hybrid similarity measure, where terms are compared by using both internal and external evidence. The measure combines lexical, syntactical and contextual similarity. Experiments on terminology recognition and clustering performed on a corpus of MEDLINE abstracts recorded the precision of 98 and 71% respectively. AVAILABILITY: software for the terminology management is available upon request.