Dataset Information

Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts.

ABSTRACT: About 29.8 million people worldwide had been diagnosed with Alzheimer's disease (AD) in 2015, and the number is projected to triple by 2050. In 2018, AD was the fifth leading cause of death in Americans with 65 years of age or older, but the progress of AD drug research is very limited. It is helpful to identify the key factors and research trends of AD for guiding further more effective studies. We proposed a framework named as LDAP, which combined the latent Dirichlet allocation model and affinity propagation algorithm to extract research topics from 95,876 AD-related papers published from 2007 to 2016. Trends and hotspots analyses were performed on LDAP results. We found that the focus points of AD research for the past 10 years include 15 diseases, 15 amino acids, peptides, and proteins, 9 enzymes and coenzymes, 7 hormones, 7 carbohydrates, 5 lipids, 2 organophosphonates, 18 chemicals, 11 compounds, 13 symptoms, and 20 phenomena. Our LDAP framework allowed us to trace the evolution of research trends and the most popular areas of interest (hotspots) on disease, protein, symptom, and phenomena. Meanwhile, 556 AD related-genes were identified, which are enriched in 12 KEGG pathways including the AD pathway and nitrogen metabolism pathway. Our results are freely available at https://www.keaml.cn/Alzheimer.

SUBMITTER: Guan R

PROVIDER: S-EPMC6775293 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts.

Guan Renchu R Wen Xiaojing X Liang Yanchun Y Xu Dong D He Baorun B Feng Xiaoyue X

International journal of biological sciences 20190806 10

About 29.8 million people worldwide had been diagnosed with Alzheimer's disease (AD) in 2015, and the number is projected to triple by 2050. In 2018, AD was the fifth leading cause of death in Americans with 65 years of age or older, but the progress of AD drug research is very limited. It is helpful to identify the key factors and research trends of AD for guiding further more effective studies. We proposed a framework named as LDAP, which combined the latent Dirichlet allo ...[more]

PMID: 31592230

Similar Datasets

Project description:BackgroundThe field of epidemiological criminology (or justice health research) has emerged in the past decade, studying the intersection between the public health and justice systems. To ensure research efforts are focused and equitable, it is important to reflect on the outputs in this area and address knowledge gaps.ObjectiveThis study aimed to examine the characteristics of populations researched in a large sample of published outputs and identify research gaps and biases.MethodsA rule-based, text mining method was applied to 34,481 PubMed abstracts published from 1963 to 2023 to identify 4 population characteristics (sex, age, offender type, and nationality).ResultsWe evaluated our method in a random sample of 100 PubMed abstracts. Microprecision was 94.3%, with microrecall at 85.9% and micro-F1-score at 89.9% across the 4 characteristics. Half (n=17,039, 49.4%) of the 34,481 abstracts did not have any characteristic mentions and only 1.3% (n=443) reported sex, age, offender type, and nationality. From the 5170 (14.9%) abstracts that reported age, 3581 (69.3%) mentioned young people (younger than 18 years) and 3037 (58.7%) mentioned adults. Since 1990, studies reporting female-only populations increased, and in 2023, these accounted for almost half (105/216, 48.6%) of the research outputs, as opposed to 33.3% (72/216) for male-only populations. Nordic countries (Sweden, Norway, Finland, and Denmark) had the highest number of abstracts proportional to their incarcerated populations. Offenders with mental illness were the most common group of interest (840/4814, 17.4%), with an increase from 1990 onward.ConclusionsResearch reporting on female populations increased, surpassing that involving male individuals, despite female individuals representing 5% of the incarcerated population; this suggests that male prisoners are underresearched. Although calls have been made for the justice health area to focus more on young people, our results showed that among the abstracts reporting age, most mentioned a population aged <18 years, reflecting a rise of youth involvement in the youth justice system. Those convicted of sex offenses and crimes relating to children were not as researched as the existing literature suggests, with a focus instead on populations with mental illness, whose rates rose steadily in the last 30 years. After adjusting for the size of the incarcerated population, Nordic countries have conducted proportionately the most research. Our findings highlight that despite the presence of several research reporting guidelines, justice health abstracts still do not adequately describe the investigated populations. Our study offers new insights in the field of justice health with implications for promoting diversity in the selection of research participants.

Project description:ObjectiveAcupuncture has become popular and widely practiced in many countries around the world. Despite the large amount of acupuncture-related literature that has been published, broader trends in the prevalence and scope of acupuncture research remain underexplored. The current study quantitatively analyzes trends in acupuncture research publications in the past 20 years.MethodsA bibliometric approach was used to search PubMed for all acupuncture-related research articles including clinical and animal studies. Inclusion criteria were articles published between 1995 and 2014 with sufficient information for bibliometric analyses. Rates and patterns of acupuncture publication within the 20 year observational period were estimated, and compared with broader publication rates in biomedicine. Identified eligible publications were further analyzed with respect to study type/design, clinical condition addressed, country of origin, and journal impact factor.ResultsA total of 13,320 acupuncture-related publications were identified using our search strategy and eligibility criteria. Regression analyses indicated an exponential growth in publications over the past two decades, with a mean annual growth rate of 10.7%. This compares to a mean annual growth rate of 4.5% in biomedicine. A striking trend was an observed increase in the proportion of randomized clinical trials (RCTs), from 7.4% in 1995 to 20.3% in 2014, exceeding the 4.5% proportional growth of RCTs in biomedicine. Over the 20 year period, pain was consistently the most common focus of acupuncture research (37.9% of publications). Other top rankings with respect to medical focus were arthritis, neoplasms/cancer, pregnancy or labor, mood disorders, stroke, nausea/vomiting, sleep, and paralysis/palsy. Acupuncture research was conducted in 60 countries, with the top 3 contributors being China (47.4%), United States (17.5%), and United Kingdom (8.2%). Retrieved articles were published mostly in complementary and alternative medicine (CAM) journals with impact factors ranging between 0.7 and 2.8 in the top 20 journals, followed by journals specializing in neuroscience, pain, anesthesia/analgesia, internal medicine and comprehensive fields.ConclusionAcupuncture research has grown markedly in the past two decades, with a 2-fold higher growth rate than for biomedical research overall. Both the increases in the proportion of RCTs and the impact factor of journals support that the quality of published research has improved. While pain was a consistently dominant research focus, other topics gained more attention during this time period. These findings provide a context for analyzing strengths and gaps in the current state of acupuncture research, and for informing a comprehensive strategy for further advancing the field.

Project description:BackgroundIn a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method.MethodsWe recruited 143 patients with FTD, 50 patients with Alzheimer's disease (AD) dementia, and 146 cognitively normal subjects. All subjects underwent a three-dimensional volumetric brain magnetic resonance imaging (MRI) scan, and cortical thickness was measured using FreeSurfer. We applied the Laplace Beltrami operator to reduce noise in the cortical thickness data and to reduce the dimension of the feature vector. Classifiers were constructed by applying both principal component analysis and linear discriminant analysis to the cortical thickness data. For the hierarchical classification, we trained four classifiers using different pairs of groups: Step 1 - CN vs. FTD + AD, Step 2 - FTD vs. AD, Step 3 - bvFTD vs. PPA, Step 4 - svPPA vs. nfvPPA. To evaluate the classification performance for each step, we used a10-fold cross-validation approach, performed 1000 times for reliability.ResultsThe classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%). The classification accuracies of steps 1-4 were 86.1%, 90.8%, 86.9%, and 92.1%, respectively. Changes in the right frontotemporal area were critical for discriminating behavioral variant FTD from PPA. The left frontal lobe discriminated nfvPPA from svPPA, while the bilateral anterior temporal regions were critical for identifying svPPA.ConclusionsIn the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions.

Dataset Information

Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts.

Publications

Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets