Biomedical articles share annotations with their citation neighbors.

BACKGROUND: Numerous efforts have been poured into annotating the wealth of knowledge contained in biomedical articles. Thanks to such efforts, it is now possible to quantitatively explore relations between these annotations and the citation network at large scale. RESULTS: With the aid of several large and small annotation databases, this study shows that articles share annotations with their citation neighborhood to the point that the neighborhood's most common annotations are likely to be those appearing in the article. CONCLUSIONS: These findings posit that an article's citation neighborhood defines to a large extent the article's annotated content. Thus, citations should be considered as a foundation for future knowledge management and annotation of biomedical articles.

Semantic persistence of ambiguous biomedical names in the citation network.

MOTIVATION: Name ambiguity has long been a central problem in biomedical text mining. To tackle it, it has been usually assumed that names present only one meaning within a given text. It is not known whether this assumption applies beyond the scope of single documents. RESULTS: Using a new method that leverages large numbers of biomedical annotations and normalized citations, this study shows that ambiguous biomedical names mentioned in scientific articles tend to present the same meaning in articles that cite them or that they cite, and, to a lesser extent, two steps away in the citation network. Citations, therefore, can be regarded as semantic connections between articles and the citation network should be considered for tasks such as automatic name disambiguation, entity linking and biomedical database annotation. A simple experiment shows the applicability of these findings to name disambiguation. AVAILABILITY AND IMPLEMENTATION: The code used for this analysis is available at: https://github.com/raroes/one-sense-per-citation-network.

A Drug-Centric View of Drug Development: How Drugs Spread from Disease to Disease.

Drugs are often seen as ancillary to the purpose of fighting diseases. Here an alternative view is proposed in which they occupy a spearheading role. In this view, drugs are technologies with an inherent therapeutic potential. Once created, they can spread from disease to disease independently of the drug creator's original intentions. Through the analysis of extensive literature and clinical trial records, it can be observed that successful drugs follow a life cycle in which they are studied at an increasing rate, and for the treatment of an increasing number of diseases, leading to clinical advancement. Such initial growth, following a power law on average, has a degree of momentum, but eventually decelerates, leading to stagnation and decay. A network model can describe the propagation of drugs from disease to disease in which diseases communicate with each other by receiving and sending drugs. Within this model, some diseases appear more prone to influence other diseases than be influenced, and vice versa. Diseases can also be organized into a drug-centric disease taxonomy based on the drugs that each adopts. This taxonomy reflects not only biological similarities across diseases, but also the level of differentiation of existing therapies. In sum, this study shows that drugs can become contagious technologies playing a driving role in the fight against disease. By better understanding such dynamics, pharmaceutical developers may be able to manage drug projects more effectively.

OntoGene web services for biomedical text mining.

Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges,with top ranked results in several of them.

Quantifying the complexity of medical research.

MOTIVATION: A crucial phenomenon of our times is the diminishing marginal returns of investments in pharmaceutical research and development. A potential reason is that research into diseases is becoming increasingly complex, and thus more burdensome, for humans to handle. We sought to investigate whether we could measure research complexity by analyzing the published literature. RESULTS: Through the text mining of the publication record of multiple diseases, we have found that the complexity and novelty of disease research has been increasing over the years. Surprisingly, we have also found that research on diseases with higher publication rate does not possess greater complexity or novelty than that on less-studied diseases. We have also shown that the research produced about a disease can be seen as a differentiated area of knowledge within the wider biomedical research. For our analysis, we have conceptualized disease research as a parallel multi-agent search in which each scientific agent (a scientist) follows a search path based on a model of a disease. We have looked at trends in facts published for diseases, measured their diversity and turnover using the entropy measure and found similar patterns across disease areas. CONTACT: raul.rodriguez-esteban@roche.com.

Generative artificial intelligence empowers digital twins in drug discovery and clinical trials.

INTRODUCTION: The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties. AREAS COVERED: The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials. EXPERT OPINION: The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.

Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 Annual Symposium.

OBJECTIVE: The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. In this paper, we present the annotated corpora, a technical summary of participants' systems, and the performance results. METHODS: The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of 5 tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events). RESULTS: In total, 29 teams registered, representing 17 countries. In general, the top-performing systems used deep neural network architectures based on pre-trained transformer models. In particular, the top-performing systems for the classification tasks were based on single models that were pre-trained on social media corpora. CONCLUSION: To facilitate future work, the datasets-a total of 61 353 posts-will remain available by request, and the CodaLab sites will remain active for a post-evaluation phase.

Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators.

Patients, life science industry and regulatory authorities are united in their goal to reduce the disease burden of patients by closing remaining unmet needs. Patients have, however, not always been systematically and consistently involved in the drug development process. Recognizing this gap, regulatory bodies worldwide have initiated patient-focused drug development (PFDD) initiatives to foster a more systematic involvement of patients in the drug development process and to ensure that outcomes measured in clinical trials are truly relevant to patients and represent significant improvements to their quality of life. As a source of real-world evidence (RWE), social media has been consistently shown to capture the first-hand, spontaneous and unfiltered disease and treatment experience of patients and is acknowledged as a valid method for generating patient experience data by the Food and Drug Administration (FDA). While social media listening (SML) methods are increasingly applied to many diseases and use cases, a significant piece of uncertainty remains on how evidence derived from social media can be used in the drug development process and how it can impact regulatory decision making, including legal and ethical aspects. In this policy paper, we review the perspectives of three key stakeholder groups on the role of SML in drug development, namely patients, life science companies and regulators. We also carry out a systematic review of current practices and use cases for SML and, in particular, highlight benefits and drawbacks for the use of SML as a way to identify unmet needs of patients. While we find that the stakeholders are strongly aligned regarding the potential of social media for PFDD, we identify key areas in which regulatory guidance is needed to reduce uncertainty regarding the impact of SML as a source of patient experience data that has impact on regulatory decision making.

Overview of the 8th Social Media Mining for Health Applications (#SMM4H) Shared Tasks at the AMIA 2023 Annual Symposium.

The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of five tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events). In total, 29 teams registered, representing 18 countries. In this paper, we present the annotated corpora, a technical summary of the systems, and the performance results. In general, the top-performing systems used deep neural network architectures based on pre-trained transformer models. In particular, the top-performing systems for the classification tasks were based on single models that were pre-trained on social media corpora. To facilitate future work, the datasets-a total of 61,353 posts-will remain available by request, and the CodaLab sites will remain active for a post-evaluation phase.

Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition.

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

New reasons for biologists to write with a formal language.

Current biological writing is afflicted by the use of ambiguous names, convoluted sentences, vague statements and narrative-fitted storylines. This represents a challenge for biological research in general and in particular for fields such as biological database curation and text mining, which have been tasked to cope with exponentially growing content. Improving the quality of biological writing by encouraging unambiguity and precision would foster expository discipline and machine reasoning. More specifically, the routine inclusion of formal languages in biological writing would improve our ability to describe, compile and model biology.

The speed of information propagation in the scientific network distorts biomedical research.

Delays in the propagation of scientific discoveries across scientific communities have been an oft-maligned feature of scientific research for introducing a bias towards knowledge that is produced within a scientist's closest community. The vastness of the scientific literature has been commonly blamed for this phenomenon, despite recent improvements in information retrieval and text mining. Its actual negative impact on scientific progress, however, has never been quantified. This analysis attempts to do so by exploring its effects on biomedical discovery, particularly in the discovery of relations between diseases, genes and chemical compounds. Results indicate that the probability that two scientific facts will enable the discovery of a new fact depends on how far apart these two facts were originally within the scientific landscape. In particular, the probability decreases exponentially with the citation distance. Thus, the direction of scientific progress is distorted based on the location in which each scientific fact is published, representing a path-dependent bias in which originally closely-located discoveries drive the sequence of future discoveries. To counter this bias, scientists should open the scope of their scientific work with modern information retrieval and extraction approaches.

Understanding the genetics of viral drug resistance by integrating clinical data and mining of the scientific literature.

Drug resistance caused by mutations is a public health threat for existing and emerging viral diseases. A wealth of evidence about these mutations and their clinically associated phenotypes is scattered across the literature, but a comprehensive perspective is usually lacking. This work aimed to produce a clinically relevant view for the case of Hepatitis B virus (HBV) mutations by combining a chronic HBV clinical study with a compendium of genetic mutations systematically gathered from the scientific literature. We enriched clinical mutation data by systematically mining 2,472,725 scientific articles from PubMed Central in order to gather information about the HBV mutational landscape. By performing this analysis, we were able to identify mutational hotspots for each HBV genotype (A-E) and gene (C, X, P, S), as well as the location of disulfide bonds associated with these mutations. Through a modelling study, we also identified a mutation position common in both the clinical data and the literature that is located at the binding pocket for a known anti-HBV drug, namely entecavir. The results of this novel approach show the potential of integrated analyses to assist in the development of new drugs for viral diseases that are more robust to resistance. Such analyses should be of particular interest due to the increasing importance of viral resistance in established and emerging viruses, such as for newly developed drugs against SARS-CoV-2.

Applications of quantitative social media listening to patient-centric drug development.

Social media listening has been increasingly acknowledged as a tool with applications in many stages of the drug development process. These applications were created to meet the need for patient-centric therapies that are fit-for-purpose and meaningful to patients. Such applications, however, require the leverage of new quantitative approaches and analytical methods that draw from developments in artificial intelligence and real-world data (RWD) analysis. Here, we review the state-of-the-art in quantitative social media listening (QSML) methods applied to drug discovery from the perspective of the pharmaceutical industry.

Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers.

Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore 'Challenges in Mining Drug Adverse Reactions'. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.

Prediction of standard cell types and functional markers from textual descriptions of flow cytometry gating definitions using machine learning.

BACKGROUND: A key step in clinical flow cytometry data analysis is gating, which involves the identification of cell populations. The process of gating produces a set of reportable results, which are typically described by gating definitions. The non-standardized, non-interpreted nature of gating definitions represents a hurdle for data interpretation and data sharing across and within organizations. Interpreting and standardizing gating definitions for subsequent analysis of gating results requires a curation effort from experts. Machine learning approaches have the potential to help in this process by predicting expert annotations associated with gating definitions. METHODS: We created a gold-standard dataset by manually annotating thousands of gating definitions with cell type and functional marker annotations. We used this dataset to train and test a machine learning pipeline able to predict standard cell types and functional marker genes associated with gating definitions. RESULTS: The machine learning pipeline predicted annotations with high accuracy for both cell types and functional marker genes. Accuracy was lower for gating definitions from assays belonging to laboratories from which limited or no prior data was available in the training. Manual error review ensured that resulting predicted annotations could be reused subsequently as additional gold-standard training data. CONCLUSIONS: Machine learning methods are able to consistently predict annotations associated with gating definitions from flow cytometry assays. However, a hybrid automatic and manual annotation workflow would be recommended to achieve optimal results.

A Patient-Centered Conceptual Model of Symptoms and Their Impact in Early Parkinson's Disease: A Qualitative Study.

BACKGROUND: Individuals with Parkinson's disease (PD) develop a significant disease burden over time that contributes to a progressive decline in health-related quality of life. There is a paucity of qualitative research to understand symptoms and impacts in individuals with early-stage PD (i.e., Hoehn and Yahr stage 1-2 and ≤2 years since diagnosis). OBJECTIVE: The collection of qualitative data to inform the selection of clinical outcome assessments for clinical trials is advocated by regulators. This patient-centered, multistage study sought to create a conceptual model of symptoms and their impact for individuals with early-stage PD. METHODS: Symptoms and impacts of PD were gathered from a literature review of qualitative research, a quantitative social media listening analysis, and qualitative patient concept elicitation interviews (n = 35). Clinical experts provided input to validate and finalize the concepts. RESULTS: The final conceptual model consisted of 27 symptoms categorized into 'motor' or 'non-motor' domains, and 39 impacts divided into five domains. Most frequently reported symptoms in early-stage PD were 'tremors' (89%), 'stiffness and rigidity', and 'fatigue' (69%, both). Most frequently reported impacts included 'anxiety' (74%), 'eating and drinking' (71%), followed by 'exercise/sport' and 'relationship with family/family life' (66%, both). CONCLUSION: This study provides initial insights relating to the symptom and impact burden of early-stage PD patients. The conceptual model can be used to help researchers to develop and select optimal patient-centered outcomes to measure treatment benefit in clinical trials. These findings could inform future qualitative research and the development of outcomes specifically for early-stage PD patients.

Identifying and classifying biomedical perturbations in text.

Molecular perturbations provide a powerful toolset for biomedical researchers to scrutinize the contributions of individual molecules in biological systems. Perturbations qualify the context of experimental results and, despite their diversity, share properties in different dimensions in ways that can be formalized. We propose a formal framework to describe and classify perturbations that allows accumulation of knowledge in order to inform the process of biomedical scientific experimentation and target analysis. We apply this framework to develop a novel algorithm for automatic detection and characterization of perturbations in text and show its relevance in the study of gene-phenotype associations and protein-protein interactions in diabetes and cancer. Analyzing perturbations introduces a novel view of the multivariate landscape of biological systems.

A Neural Network Approach for Understanding Patient Experiences of Chronic Obstructive Pulmonary Disease (COPD): Retrospective, Cross-sectional Study of Social Media Content.

BACKGROUND: The abundance of online content contributed by patients is a rich source of insight about the lived experience of disease. Patients share disease experiences with other members of the patient and caregiver community and do so using their own lexicon of words and phrases. This lexicon and the topics that are communicated using words and phrases belonging to the lexicon help us better understand disease burden. Insights from social media may ultimately guide clinical development in ways that ensure that future treatments are fit for purpose from the patient's perspective. OBJECTIVE: We sought insights into the patient experience of chronic obstructive pulmonary disease (COPD) by analyzing a substantial corpus of social media content. The corpus was sufficiently large to make manual review and manual coding all but impossible to perform in a consistent and systematic fashion. Advanced analytics were applied to the corpus content in the search for associations between symptoms and impacts across the entire text corpus. METHODS: We conducted a retrospective, cross-sectional study of 5663 posts sourced from open blogs and online forum posts published by COPD patients between February 2016 and August 2019. We applied a novel neural network approach to identify a lexicon of community words and phrases used by patients to describe their symptoms. We used this lexicon to explore the relationship between COPD symptoms and disease-related impacts. RESULTS: We identified a diverse lexicon of community words and phrases for COPD symptoms, including gasping, wheezy, mucus-y, and muck. These symptoms were mentioned in association with specific words and phrases for disease impact such as frightening, breathing discomfort, and difficulty exercising. Furthermore, we found an association between mucus hypersecretion and moderate disease severity, which distinguished mucus from the other main COPD symptoms, namely breathlessness and cough. CONCLUSIONS: We demonstrated the potential of neural networks and advanced analytics to gain patient-focused insights about how each distinct COPD symptom contributes to the burden of chronic and acute respiratory illness. Using a neural network approach, we identified words and phrases for COPD symptoms that were specific to the patient community. Identifying patterns in the association between symptoms and impacts deepened our understanding of the patient experience of COPD. This approach can be readily applied to other disease areas.

Figure mining for biomedical research.

MOTIVATION: Figures from biomedical articles contain valuable information difficult to reach without specialized tools. Currently, there is no search engine that can retrieve specific figure types. RESULTS: This study describes a retrieval method that takes advantage of principles in image understanding, text mining and optical character recognition (OCR) to retrieve figure types defined conceptually. A search engine was developed to retrieve tables and figure types to aid computational and experimental research. AVAILABILITY: http://iossifovlab.cshl.edu/figurome/.