Biomedical Research Literature Research Articles

Visualizing Racism within Medicine

The Tuskegee experiment became the public symbol and face of research medical malfeasance. Numerous scholars, literature, and science frame this study through the lens of distrust, lack of ethics and informed consent. However, Tuskegee foregrounded practices, behaviors, beliefs, and standards that already existed. History and science reveal disconnections from the historical implication of black individuals and disparate experiences within communities. Currently, medical doctors cite challenges to publishing research on race with scholarly medical journals. Biomedical research literature discusses racism within the confines of editorial and commentary features. In 2021, top editor of the Journal of American Medicine stepped down amid controversial comments about race.
 Medical doctors cite challenges to publishing research on race with scholarly medical journals. Racial disparities within medicine, scientific records, scholarly publishing, and medical literature are not silos and are interconnected. This poster is research in design illustrating their intersections and explores the following research questions:
 
 How do existing university course content describe race and ethics?
 Can racism be conceptualized as an ethical issue within medical education?
 

A scoping review of publicly available language tasks in clinical natural language processing.

To provide a scoping review of papers on clinical natural language processing (NLP) shared tasks that use publicly available electronic health record data from a cohort of patients. We searched 6 databases, including biomedical research and computer science literature databases. A round of title/abstract screening and full-text screening were conducted by 2 reviewers. Our method followed the PRISMA-ScR guidelines. A total of 35 papers with 48 clinical NLP tasks met inclusion criteria between 2007 and 2021. We categorized the tasks by the type of NLP problems, including named entity recognition, summarization, and other NLP tasks. Some tasks were introduced as potential clinical decision support applications, such as substance abuse detection, and phenotyping. We summarized the tasks by publication venue and dataset type. The breadth of clinical NLP tasks continues to grow as the field of NLP evolves with advancements in language systems. However, gaps exist with divergent interests between the general domain NLP community and the clinical informatics community for task motivation and design, and in generalizability of the data sources. We also identified issues in data preparation. The existing clinical NLP tasks cover a wide range of topics and the field is expected to grow and attract more attention from both general domain NLP and clinical informatics community. We encourage future work to incorporate multidisciplinary collaboration, reporting transparency, and standardization in data preparation. We provide a listing of all the shared task papers and datasets from this review in a GitLab repository.

FUNCTIONALIZED POLYMERIC NANOPARTICLES: A NOVEL TARGETED APPROACH FOR ONCOLOGY CARE

Popular cancer therapies face extreme disadvantages, including multimedicament tolerance and non-target impact. These issues will lead to poorer patient conformity and poor levels of survival. Successful medical therapies for cancer patients are desperately required. Nano-particulate structures with a pluronic base represent revolutionary platforms for anti-cancer agent provision. These structures provide great potential for the advancement of cancer therapy due to their pharmacological properties and sufficient physicochemical characteristics. This review aims to offer a more detailed description of the pluronic drug delivery mechani sms that are currently available and explains pluronic as a medicinal polymer. Hydrophobic payload formulations and updated, targeted distribution mechanisms are explained based on pluronic formulations. This analysis offers a rundown of the current situation art related to the theranostic application of polymer micelles targeting the microenvironment of cancer cells. Some guidelines for the future scope and possible opportunities are also been addressed. Search criteria: Primary sources such as Medline a principal component of PubMed, an online, searchable, and biomedical and life science research literature database has been used. It brings readers to almost any area of interest with research and journal articles. One of the internet resources of importance to get scientific publications is specialized scientific search engines known as Google Scholar a database of research material that can be searched for. I have used the online electronic access portal of Elsevier, such as Science Direct to its publications. Scopus is the biggest abstract and peer-reviewed literature database for scientific journals, books, and conference work. Keywords like Cancer, Pluronic, Nanoparticles, Chemotherapy, Cancer, Theranostic, Targeted, Micelles, and Core-shell are crucial as they notify search engines of the content of the site. Range of years: 1992-2020.

A systematic review of automatic text summarization for biomedical literature and EHRs.

Biomedical text summarization helps biomedical information seekers avoid information overload by reducing the length of a document while preserving the contents' essence. Our systematic review investigates the most recent biomedical text summarization researches on biomedical literature and electronic health records by analyzing their techniques, areas of application, and evaluation methods. We identify gaps and propose potential directions for future research. This review followed the PRISMA methodology and replicated the approaches adopted by the previous systematic review published on the same topic. We searched 4 databases (PubMed, ACM Digital Library, Scopus, and Web of Science) from January 1, 2013 to April 8, 2021. Two reviewers independently screened title, abstract, and full-text for all retrieved articles. The conflicts were resolved by the third reviewer. The data extraction of the included articles was in 5 dimensions: input, purpose, output, method, and evaluation. Fifty-eight out of 7235 retrieved articles met the inclusion criteria. Thirty-nine systems used single-document biomedical research literature as their input, 17 systems were explicitly designed for clinical support, 47 systems generated extractive summaries, and 53 systems adopted hybrid methods combining computational linguistics, machine learning, and statistical approaches. As for the assessment, 51 studies conducted an intrinsic evaluation using predefined metrics. This study found that current biomedical text summarization systems have achieved good performance using hybrid methods. Studies on electronic health records summarization have been increasing compared to a previous survey. However, the majority of the works still focus on summarizing literature.

#Menopause on Instagram: a mixed-methods study.

The aims of this study were to: (1) characterize the menopause transition (MT) on social media and (2) determine if concordance or discordance exists when comparing MT-focused social media posts and biomedical research literature. We analyzed 440 sequential Instagram posts with the hashtag #menopause over 2 weeks from January to February 2019. Posts were composed of 299 unique accounts, resulting in an average of 1.7 posts per account (standard deviation [SD] 1; range 1-9; median 1 and interquartile range [IQR] 1-2). Each account had an average of 2,616 followers (SD 11,271; range 3-129,000; median 421.5 and IQR 177-1,101). Content and thematic analyses were completed for posts, images, and videos to identify codes related to the MT. The top 15 codes were then searched along with the key term "menopause" in PubMed to ascertain the level of concordance between Instagram content and peer-reviewed literature on the MT. We identified 69 codes in our corpus of Instagram content, resulting in 9 categories: physical health, mental health, complementary and integrative health, advertising, social, advice, self-care, nature, and self-expression (kappa 0.95-1.00). The most prevalent codes were related to weight loss/fitness (20.5%) and hormones (18.4%). The majority of frequent codes identified in Instagram posts were infrequently listed in biomedical literature related to menopause. However, there were two codes, Weight loss/Fitness and Hot flashes, that were frequently discussed in Instagram posts and the biomedical literature. The examination of #menopause on Instagram provides novel insights for researchers and clinicians. Our findings provide a better understanding of the experiences and support needs of individuals experiencing menopause. Furthermore, codes related to menopause have low prominence in the biomedical literature, suggesting key topics that could be explored in the future.

Co-occurrence of Cell Lines, Basal Media and Supplementation in the Biomedical Research Literature

Abstract Purpose The use of in vitro cell culture and experimentation is a cornerstone of biomedical research, however, more attention has recently been given to the potential consequences of using such artificial basal medias and undefined supplements. As a first step towards better understanding and measuring the impact these systems have on experimental results, we use text mining to capture typical research practices and trends around cell culture. Design/methodology/approach To measure the scale of in vitro cell culture use, we have analyzed a corpus of 94,695 research articles that appear in biomedical research journals published in ScienceDirect from 2000–2018. Central to our investigation is the observation that studies using cell culture describe conditions using the typical sentence structure of cell line, basal media, and supplemented compounds. Here we tag our corpus with a curated list of basal medias and the Cellosaurus ontology using the Aho-Corasick algorithm. We also processed the corpus with Stanford CoreNLP to find nouns that follow the basal media, in an attempt to identify supplements used. Findings Interestingly, we find that researchers frequently use DMEM even if a cell line's vendor recommends less concentrated media. We see long-tailed distributions for the usage of media and cell lines, with DMEM and RPMI dominating the media, and HEK293, HEK293T, and HeLa dominating cell lines used. Research limitations Our analysis was restricted to documents in ScienceDirect, and our text mining method achieved high recall but low precision and mandated manual inspection of many tokens. Practical implications Our findings document current cell culture practices in the biomedical research community, which can be used as a resource for future experimental design. Originality/value No other work has taken a text mining approach to surveying cell culture practices in biomedical research.

Disaster Lit: Database for Disaster Medicine and Public Health

Disaster Lit: Database for Disaster Medicine and Public Health is created and maintained by the U.S. National Library of Medicine. It is an Open Access database of research literature in disaster medicine and public health. This database provides access to biomedical research, journal research, and grey literature on disaster medicine and public health. It has a well-developed search engine and a good user interface. Disaster Lit is an important database for research in emergency management and disaster preparedness and public health.

The Confounding of Race in High School Biology Textbooks, 2014–2019

Following on earlier examinations of biology high school textbooks’ handling of race, this study examines eleven biology textbooks, intended for US high school use and published between 2014 and 2019. It offers a close reading of these textbooks’ treatment of four topics that have been associated with race: genetic disorders, human origins, skin color, and eugenics, as well as the topic of race itself. While only one textbook has a section on “human races,” another nine refer directly or indirectly to race across these five topics. The textbooks’ treatment of genetic disorders gives a marked, if uneven, prominence to race and ethnicity. Their coverage of human origins and skin color largely undermines concepts of race essentialism and racial classification. At other points, the same textbooks introduce studies, questions, and concepts that reinforce the biological reality of race, much as was done by the textbooks of two and three generations ago. The study draws parallels between these textbooks’ decidedly mixed message on race and a similar stance present in the current biomedical research literature. In the face of this mixed message, three sets of pedagogical strategies—fact-checking, historical, and meta-scientific—are proposed that teachers might use with current biology textbooks to raise awareness of the varied positions that scientists have taken in the past and continue to take on questions of race and biology.

Towards a characterization of apparent contradictions in the biomedical literature using context analysis.

With the substantial growth in the biomedical research literature, a larger number of claims are published daily, some of which seemingly disagree with or contradict prior claims on the same topics. Resolving such contradictions is critical to advancing our understanding of human disease and developing effective treatments. Automated text analysis techniques can facilitate such analysis by extracting claims from the literature, flagging those that are potentially contradictory, and identifying any study characteristics that may explain such contradictions. Using SemMedDB, our own PubMed-scale repository of semantic predications (subject-relation-object triples), we identified apparent contradictions in the biomedical research literature and developed a categorization of contextual characteristics that explain such contradictions. Clinically relevant semantic predications relating to 20 diseases and involving opposing predicate pairs (e.g., an intervention treats or causes a disease) were retrieved from SemMedDB. After addressing inference, uncertainty, generic concepts, and NLP errors through automatic and manual filtering steps, a set of apparent contradictions were identified and characterized. We retrieved 117,676 predication instances from 62,360 PubMed abstracts (Jan 1980-Dec 2016). From these instances, automatic filtering steps generated 2236 candidate contradictory pairs. Through manual analysis, we determined that 58 of these pairs (2.6%) were apparent contradictions. We identified five main categories of contextual characteristics that explain these contradictions: (a) internal to the patient, (b) external to the patient, (c) endogenous/exogenous, (d) known controversy, and (e) contradictions in literature. Categories (a) and (b) were subcategorized further (e.g., species, dosage) and accounted for the bulk of the contradictory information. Semantic predications, by accounting for lexical variability, and SemMedDB, owing to its literature scale, can support identification and elucidation of potentially contradictory claims across the biomedical domain. Further filtering and classification steps are needed to distinguish among them the true contradictory claims. The ability to detect contradictions automatically can facilitate important biomedical knowledge management tasks, such as tracking and verifying scientific claims, summarizing research on a given topic, identifying knowledge gaps, and assessing evidence for systematic reviews, with potential benefits to the scientific community. Future work will focus on automating these steps for fully automatic recognition of contradictions from the biomedical research literature.

Low income countries have the highest percentages of open access publication: A systematic computational analysis of the biomedical literature.

Open access publication rates have been steadily increasing over time. In spite of this growth, academics in low income settings struggle to gain access to the full canon of research literature. While the vast majority of open access repositories and funding organizations with open access policies are based in high income countries, the geographic patterns of open access publication itself are not well characterized. In this study, we developed a computational approach to better understand the topical and geographical landscape of open access publications in the biomedical research literature. Surprisingly, we found a strong negative correlation between country per capita income and the percentage of open access publication. Open access publication rates were particularly high in sub-Saharan Africa, but vastly lower in the Middle East and North Africa, South Asia, and East Asia and the Pacific. These effects persisted when considering papers only bearing authors from within each region and income group. However, papers resulting from international collaborations did have a higher percentage of OA than single-country papers, and inter-regional collaboration increased OA publication for all world regions. There was no clear relationship between the number of open access policies in a region and the percentage of open access publications in that region. To understand the distribution of open access across topics of biomedical research, we examined keywords that were most enriched and depleted in open access papers. Keywords related to genomics, computational biology, animal models, and infectious disease were enriched in open access publications, while keywords related to the environment, nursing, and surgery were depleted in open access publications. This work identifies geographic regions and fields of research that could be priority areas for open access advocacy. The finding that open access publication rates are highest in sub-Saharan Africa and low income countries suggests that factors other than open access policy strongly influence authors’ decisions to make their work openly accessible. The high proportion of OA resulting from international collaborations indicates yet another benefit of collaborative research. Certain applied fields of medical research, notably nursing, surgery, and environmental fields, appear to have a greater proportion of fee-for-access publications, which presumably creates barriers that prevent researchers and practitioners in low income settings from accessing the literature in those fields.

Reagent Validation to Facilitate Experimental Reproducibility.

Many results reported in the biomedical research literature cannot be independently reproduced, undermining the basic foundations of science. This overview is intended for researchers who are committed to improving the quality and integrity of biomedical science by raising awareness of both the sources of irreproducibility, and activities specifically targeted to address the issue. The irreproducibility of biomedical research is due to a variety of factors, known and unknown, that markedly influence experimental outcomes. Among the known factors are inadequate training or mentoring, or experimenter incompetence. These may result in flawed experimental design and execution that reflect a lack of planning, leading to an underpowering of a study, an absence of appropriate controls, selective data analysis, inappropriate statistical analysis, and/or investigator bias or fraud. Another frequently overlooked source of irreproducibility is failure to ensure that the equipment used is performing up to manufacturer specifications. Failure to validate/authenticate the experimental reagents is another source of irreproducibility. These include compounds, cell lines, antibodies, and the animal models used in a study. This overview discusses how a lack of validation of research reagents negatively impact experimental reproducibility, and provides guidelines to avoid these pitfalls. © 2018 by John Wiley & Sons, Inc.

Improving the biomedical research literature: insights from authors’ editors can help journal editors define and refine their core competencies

A team of stakeholders in biomedical publishing recently proposed a set of core competencies for journal editors, as a resource that can inform training programs for editors and ultimately improve the quality of the biomedical research literature. This initiative, still in its early stages, would benefit from additional sources of expert information. Based on our experiences as authors’ editors, we offer two suggestions on how to strengthen these competencies so that they better respond to the needs of readers and authors – the main users of and contributors to research journals. First, journal editors should be able to ensure that authors are given useful feedback on the language and writing in submitted manuscripts, beyond a (possibly incorrect) blanket judgement of whether the English is “acceptable” or not. Second, journal editors should be able to deal effectively with inappropriate text re-use and plagiarism. These additional competencies would, we believe, be valued by other stakeholders in biomedical research publication as markers of editorial quality.

Improving the biomedical research literature: insights from authors' editors can help journal editors define and refine their core competencies.

A team of stakeholders in biomedical publishing recently proposed a set of core competencies for journal editors, as a resource that can inform training programs for editors and ultimately improve the quality of the biomedical research literature. This initiative, still in its early stages, would benefit from additional sources of expert information. Based on our experiences as authors' editors, we offer two suggestions on how to strengthen these competencies so that they better respond to the needs of readers and authors - the main users of and contributors to research journals. First, journal editors should be able to ensure that authors are given useful feedback on the language and writing in submitted manuscripts, beyond a (possibly incorrect) blanket judgement of whether the English is "acceptable" or not. Second, journal editors should be able to deal effectively with inappropriate text re-use and plagiarism. These additional competencies would, we believe, be valued by other stakeholders in biomedical research publication as markers of editorial quality.

Unit of analysis issues in laboratory-based research.

Many studies in the biomedical research literature report analyses that fail to recognise important data dependencies from multilevel or complex experimental designs. Statistical inferences resulting from such analyses are unlikely to be valid and are often potentially highly misleading. Failure to recognise this as a problem is often referred to in the statistical literature as a unit of analysis (UoA) issue. Here, by analysing two example datasets in a simulation study, we demonstrate the impact of UoA issues on study efficiency and estimation bias, and highlight where errors in analysis can occur. We also provide code (written in R) as a resource to help researchers undertake their own statistical analyses.

Identifying genotype-phenotype relationships in biomedical text

BackgroundOne important type of information contained in biomedical research literature is the newly discovered relationships between phenotypes and genotypes. Because of the large quantity of literature, a reliable automatic system to identify this information for future curation is essential. Such a system provides important and up to date data for database construction and updating, and even text summarization. In this paper we present a machine learning method to identify these genotype-phenotype relationships. No large human-annotated corpus of genotype-phenotype relationships currently exists. So, a semi-automatic approach has been used to annotate a small labelled training set and a self-training method is proposed to annotate more sentences and enlarge the training set.ResultsThe resulting machine-learned model was evaluated using a separate test set annotated by an expert. The results show that using only the small training set in a supervised learning method achieves good results (precision: 76.47, recall: 77.61, F-measure: 77.03) which are improved by applying a self-training method (precision: 77.70, recall: 77.84, F-measure: 77.77).ConclusionsRelationships between genotypes and phenotypes is biomedical information pivotal to the understanding of a patient’s situation. Our proposed method is the first attempt to make a specialized system to identify genotype-phenotype relationships in biomedical literature. We achieve good results using a small training set. To improve the results other linguistic contexts need to be explored and an appropriately enlarged training set is required.

Quantitative Perspectives on Fifty Years of the Journal of the History of Biology.

Journal of the History of Biology provides a fifty-year long record for examining the evolution of the history of biology as a scholarly discipline. In this paper, we present a new dataset and preliminary quantitative analysis of the thematic content of JHB from the perspectives of geography, organisms, and thematic fields. The geographic diversity of authors whose work appears in JHB has increased steadily since 1968, but the geographic coverage of the content of JHB articles remains strongly lopsided toward the United States, United Kingdom, and western Europe and has diversified much less dramatically over time. The taxonomic diversity of organisms discussed in JHB increased steadily between 1968 and the late 1990s but declined in later years, mirroring broader patterns of diversification previously reported in the biomedical research literature. Finally, we used a combination of topic modeling and nonlinear dimensionality reduction techniques to develop a model of multi-article fields within JHB. We found evidence for directional changes in the representation of fields on multiple scales. The diversity of JHB with regard to the representation of thematic fields has increased overall, with most of that diversification occurring in recent years. Drawing on the dataset generated in the course of this analysis, as well as web services in the emerging digital history and philosophy of science ecosystem, we have developed an interactive web platform for exploring the content of JHB, and we provide a brief overview of the platform in this article. As a whole, the data and analyses presented here provide a starting-place for further critical reflection on the evolution of the history of biology over the past half-century.

Under-reporting of Adverse Events in the Biomedical Literature

AbstractPurposeTo address the under-reporting of research results, with emphasis on the under-reporting/distorted reporting of adverse events in the biomedical research literature.Design/methodology/approachA four-step approach is used: (1) To identify the characteristics of literature that make it adequate to support policy; (2) to show how each of these characteristics becomes degraded to make inadequate literature; (3) to identify incentives to prevent inadequate literature; and (4) to show policy implications of inadequate literature.FindingsThis review has provided reasons for, and examples of, adverse health effects of myriad substances (1) being under-reported in the premiere biomedical literature, or (2) entering this literature in distorted form. Since there is no way to gauge the extent of this under/distorted-reporting, the quality and credibility of the ‘premiere’ biomedical literature is unknown. Therefore, any types of meta-analyses or scientometric analyses of this literature will have unknown quality and credibility. The most sophisticated scientometric analysis cannot compensate for a highly flawed database.Research limitationsThe main limitation is in identifying examples of under-reporting. There are many incentives for under-reporting and few dis-incentives.Practical implicationsAlmost all research publications, addressing causes of disease, treatments for disease, diagnoses for disease, scientometrics of disease and health issues, and other aspects of healthcare, build upon previous healthcare-related research published. Many researchers will not have laboratories or other capabilities to replicate or validate the published research, and depend almost completely on the integrity of this literature. If the literature is distorted, then future research can be misguided, and health policy recommendations can be ineffective or worse.Originality/valueThis review has examined a much wider range of technical and non-technical causes for under-reporting of adverse events in the biomedical literature than previous studies.

Assigning factuality values to semantic relations extracted from biomedical research literature.

Biomedical knowledge claims are often expressed as hypotheses, speculations, or opinions, rather than explicit facts (propositions). Much biomedical text mining has focused on extracting propositions from biomedical literature. One such system is SemRep, which extracts propositional content in the form of subject-predicate-object triples called predications. In this study, we investigated the feasibility of assessing the factuality level of SemRep predications to provide more nuanced distinctions between predications for downstream applications. We annotated semantic predications extracted from 500 PubMed abstracts with seven factuality values (fact, probable, possible, doubtful, counterfact, uncommitted, and conditional). We extended a rule-based, compositional approach that uses lexical and syntactic information to predict factuality levels. We compared this approach to a supervised machine learning method that uses a rich feature set based on the annotated corpus. Our results indicate that the compositional approach is more effective than the machine learning method in recognizing the factuality values of predications. The annotated corpus as well as the source code and binaries for factuality assignment are publicly available. We will also incorporate the results of the better performing compositional approach into SemMedDB, a PubMed-scale repository of semantic predications extracted using SemRep.

Abstract 3589: A systematic approach toward gene annotation of the hallmarks of cancer

Abstract The hallmarks of cancer consist of ten categories that serve as an organizing principle and framework for understanding neoplastic disease. The ten hallmarks include activating invasion and metastasis, avoiding immune destruction, deregulating cellular energetics, enabling replicative immortality, evading growth suppressors, genome instability and mutation, inducing angiogenesis, resisting cell death, sustaining proliferative signaling, and tumor-promoting inflammation. In order to facilitate the use of the hallmarks of cancer in genomic studies, we have undertaken a systematic methodology to map genes to each individual hallmark of cancer. Assignment of genes to the individual hallmarks was performed leveraging gene ontology (GO) annotations. Specifically, for each hallmark, a term list was generated by cancer research experts, with biological terms associated with each hallmark. This term list was used to search and identify matching GO terms. Matched terms were then mapped to the highest, most specific, representative, non-redundant GO term in the GO hierarchy. Genes for each term were restricted by selecting only human taxon, and high confidence experimental evidence codes. For specific GO terms, where present, the Regulation of ‘GO term’ category was included. The genes for each GO term for each hallmark were combined in a non-redundant list of genes for each hallmark. In the case of the evading growth suppressors category, a different strategy was employed. In this instance, genes were identified through ‘tumor suppressor’ gene/protein annotation in the DGIdb resource and UniProt. Additionally, text-mining methods on biomedical research literature were used to defined a high confidence list of genes for this hallmark. As an example use case of the hallmarks we applied them to the analysis of the TCGA Pan-Cancer RNAseq data set. Clustering across the individual hallmarks led to interesting differences within particular tumor tissue types, as well as shared hallmarks for other tumor types. For example, use of the genome instability and mutation hallmark for Pan-Cancer clustering, revealed a tight tissue-based cluster of low-grade gliomas and GBMs. This result suggests the importance of this hallmark expression in this tumor type. This high confidence mapping of genes to hallmarks of cancer provides a unique dataset to facilitate analyses of cancer genome data. Citation Format: Jeff Kiefer, Sara Nasser, John Graf, Chinnappa Kodira, Fiona Ginty, Lee Newberg, Anup Sood, Michael E. Berens. A systematic approach toward gene annotation of the hallmarks of cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3589. doi:10.1158/1538-7445.AM2017-3589

Informatics Support for Basic Research in Biomedicine.

Informatics methodologies exploit computer-assisted techniques to help biomedical researchers manage large amounts of information. In this paper, we focus on the biomedical research literature (MEDLINE). We first provide an overview of some text mining techniques that offer assistance in research by identifying biomedical entities (e.g., genes, substances, and diseases) and relations between them in text.We then discuss Semantic MEDLINE, an application that integrates PubMed document retrieval, concept and relation identification, and visualization, thus enabling a user to explore concepts and relations from within a set of retrieved citations. Semantic MEDLINE provides a roadmap through content and helps users discern patterns in large numbers of retrieved citations. We illustrate its use with an informatics method we call "discovery browsing," which provides a principled way of navigating through selected aspects of some biomedical research area. The method supports an iterative process that accommodates learning and hypothesis formation in which a user is provided with high level connections before delving into details.As a use case, we examine current developments in basic research on mechanisms of Alzheimer's disease. Out of the nearly 90 000 citations returned by the PubMed query "Alzheimer's disease," discovery browsing led us to 73 citations on sortilin and that disorder. We provide a synopsis of the basic research reported in 15 of these. There is wide-spread consensus among researchers working with a range of animal models and human cells that increased sortilin expression and decreased receptor expression are associated with amyloid beta and/or amyloid precursor protein.