J Transl Med. 2023 Jan 25;21(1):48. doi: 10.1186/s12967-023-03876-3.

ABSTRACT

BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy.

METHODS: We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning.

RESULTS: To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF.

CONCLUSIONS: All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

PMID:36698208 | DOI:10.1186/s12967-023-03876-3

BMC Med Inform Decis Mak. 2023 Jan 24;23(1):18. doi: 10.1186/s12911-023-02115-5.

ABSTRACT

BACKGROUND: The lack of interoperability between health information systems reduces the quality of care provided to patients and wastes resources. Accordingly, there is an urgent need to develop integration mechanisms among the various health information systems. The aim of this review was to investigate the interoperability requirements for heterogeneous health information systems and to summarize and present them.

METHODS: In accordance with the PRISMA guideline, a broad electronic search of all literature was conducted on the topic through six databases, including PubMed, Web of science, Scopus, MEDLINE, Cochrane Library and Embase to 25 July 2022. The inclusion criteria were to select English-written articles available in full text with the closest objectives. 36 articles were selected for further analysis.

RESULTS: Interoperability has been raised in the field of health information systems from 2003 and now it is one of the topics of interest to researchers. The projects done in this field are mostly in the national scope and to achieve the electronic health record. HL7 FHIR, CDA, HIPAA and SNOMED-CT, SOA, RIM, XML, API, JAVA and SQL are among the most important requirements for implementing interoperability. In order to guarantee the concept of data exchange, semantic interaction is the best choice because the systems can recognize and process semantically similar information homogeneously.

CONCLUSIONS: The health industry has become more complex and has new needs. Interoperability meets this needs by communicating between the output and input of processor systems and making easier to access the data in the required formats.

PMID:36694161 | DOI:10.1186/s12911-023-02115-5

Psychol Aging. 2023 Mar;38(2):87-102. doi: 10.1037/pag0000721. Epub 2023 Jan 23.

ABSTRACT

Computational research suggests that semantic memory, operationalized as semantic memory networks, undergoes age-related changes. Previous work suggests that concepts in older adults' semantic memory networks are more separated, more segregated, and less connected to each other. However, cognitive network research often relies on group averages (e.g., young vs. older adults), and it remains unclear if individual differences influence age-related disparities in language production abilities. Here, we analyze the properties of younger and older participants' individual-based semantic memory networks based on their semantic relatedness judgments. We related individual-based network measures-clustering coefficient (CC; connectivity), global efficiency, and modularity (structure)-to language production (verbal fluency) and vocabulary knowledge. Similar to previous findings, we found significant age effects: CC and global efficiency were lower, and modularity was higher, for older adults. Furthermore, vocabulary knowledge was significantly related to the semantic memory network measures: corresponding with the age effects, CC and global efficiency had a negative relationship, while modularity had a positive relationship with vocabulary knowledge. More generally, vocabulary knowledge significantly increased with age, which may reflect the critical role that the accumulation of knowledge within semantic memory has on its structure. These results highlight the impact of diverse life experiences on older adults' semantic memory and demonstrate the importance of accounting for individual differences in the aging mental lexicon. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

PMID:36689391 | DOI:10.1037/pag0000721

JMIR Med Inform. 2023 Jan 20;11:e38861. doi: 10.2196/38861.

ABSTRACT

BACKGROUND: There is a variety of libre/free and open-source software (LIFOSS) products for medicine and health care. To support health care and IT professionals select an appropriate software product for given tasks, several comparison studies and web platforms, such as Medfloss.org, are available. However, due to the lack of a uniform terminology for health informatics, ambiguous or imprecise terms are used to describe the functionalities of LIFOSS. This makes comparisons of LIFOSS difficult and may lead to inappropriate software selection decisions. Using Linked Open Data (LOD) promises to address these challenges.

OBJECTIVE: We describe LIFOSS systematically with the help of the underlying Health Information Technology Ontology (HITO). We publish HITO and HITO-based software product descriptions using LOD to obtain the following benefits: (1) linking and reusing existing terminologies and (2) using Semantic Web tools for viewing and querying the LIFOSS data on the World Wide Web.

METHODS: HITO was incrementally developed and implemented. First, classes for the description of software products in health IT evaluation studies were identified. Second, requirements for describing LIFOSS were elicited by interviewing domain experts. Third, to describe domain-specific functionalities of software products, existing catalogues of features and enterprise functions were analyzed and integrated into the HITO knowledge base. As a proof of concept, HITO was used to describe 25 LIFOSS products.

RESULTS: HITO provides a defined set of classes and their relationships to describe LIFOSS in medicine and health care. With the help of linked or integrated catalogues for languages, programming languages, licenses, features, and enterprise functions, the functionalities of LIFOSS can be precisely described and compared. We publish HITO and the LIFOSS descriptions as LOD; they can be queried and viewed using different Semantic Web tools, such as Resource Description Framework (RDF) browsers, SPARQL Protocol and RDF Query Language (SPARQL) queries, and faceted searches. The advantages of providing HITO as LOD are demonstrated by practical examples.

CONCLUSIONS: HITO is a building block to achieving unambiguous communication among health IT professionals and researchers. Providing LIFOSS product information as LOD enables barrier-free and easy access to data that are often hidden in user manuals of software products or are not available at all. Efforts to establish a unique terminology of medical and health informatics should be further supported and continued.

PMID:36662569 | DOI:10.2196/38861

JMIR Form Res. 2023 Jan 19;7:e38831. doi: 10.2196/38831.

ABSTRACT

BACKGROUND: Recommender systems have great potential in mental health care to personalize self-guided content for patients, allowing them to supplement their mental health treatment in a scalable way.

OBJECTIVE: In this paper, we describe and evaluate 2 knowledge-based content recommendation systems as parts of Ginger, an on-demand mental health platform, to bolster engagement in self-guided mental health content.

METHODS: We developed two algorithms to provide content recommendations in the Ginger mental health smartphone app: (1) one that uses users' responses to app onboarding questions to recommend content cards and (2) one that uses the semantic similarity between the transcript of a coaching conversation and the description of content cards to make recommendations after every session. As a measure of success for these recommendation algorithms, we examined the relevance of content cards to users' conversations with their coach and completion rates of selected content within the app measured over 14,018 users.

RESULTS: In a real-world setting, content consumed in the recommendations section (or "Explore" in the app) had the highest completion rates (3353/7871, 42.6%) compared to other sections of the app, which had an average completion rate of 37.35% (21,982/58,614; P<.001). Within the app's recommendations section, conversation-based content recommendations had 11.4% (1108/2364) higher completion rates per card than onboarding response-based recommendations (1712/4067; P=.003) and 26.1% higher than random recommendations (534/1440; P=.005). Studied via subject matter experts' annotations, conversation-based recommendations had a 16.1% higher relevance rate for the top 5 recommended cards, averaged across sessions of varying lengths, compared to a random control (110 conversational sessions). Finally, it was observed that both age and gender variables were sensitive to different recommendation methods, with responsiveness to personalized recommendations being higher if the users were older than 35 years or identified as male.

CONCLUSIONS: Recommender systems can help scale and supplement digital mental health care with personalized content and self-care recommendations. Onboarding-based recommendations are ideal for "cold starting" the process of recommending content for new users and users that tend to use the app just for content but not for therapy or coaching. The conversation-based recommendation algorithm allows for dynamic recommendations based on information gathered during coaching sessions, which is a critical capability, given the changing nature of mental health needs during treatment. The proposed algorithms are just one step toward the direction of outcome-driven personalization in mental health. Our future work will involve a robust causal evaluation of these algorithms using randomized controlled trials, along with consumer feedback-driven improvement of these algorithms, to drive better clinical outcomes.

PMID:36656628 | DOI:10.2196/38831

Knowl Inf Syst. 2023 Jan 7:1-28. doi: 10.1007/s10115-022-01826-x. Online ahead of print.

ABSTRACT

In the last decade, a large number of knowledge graph (KG) completion approaches were proposed. Albeit effective, these efforts are disjoint, and their collective strengths and weaknesses in effective KG completion have not been studied in the literature. We extend Plumber, a framework that brings together the research community's disjoint efforts on KG completion. We include more components into the architecture of Plumber to comprise 40 reusable components for various KG completion subtasks, such as coreference resolution, entity linking, and relation extraction. Using these components, Plumber dynamically generates suitable knowledge extraction pipelines and offers overall 432 distinct pipelines. We study the optimization problem of choosing optimal pipelines based on input sentences. To do so, we train a transformer-based classification model that extracts contextual embeddings from the input and finds an appropriate pipeline. We study the efficacy of Plumber for extracting the KG triples using standard datasets over three KGs: DBpedia, Wikidata, and Open Research Knowledge Graph. Our results demonstrate the effectiveness of Plumber in dynamically generating KG completion pipelines, outperforming all baselines agnostic of the underlying KG. Furthermore, we provide an analysis of collective failure cases, study the similarities and synergies among integrated components and discuss their limitations.

PMID:36643405 | PMC:PMC9823264 | DOI:10.1007/s10115-022-01826-x

Brief Bioinform. 2023 Jan 11:bbac606. doi: 10.1093/bib/bbac606. Online ahead of print.

ABSTRACT

Recently, peptide-based drugs have gained unprecedented interest in discovering and developing antifungal drugs due to their high efficacy, broad-spectrum activity, low toxicity and few side effects. However, it is time-consuming and expensive to identify antifungal peptides (AFPs) experimentally. Therefore, computational methods for accurately predicting AFPs are highly required. In this work, we develop AFP-MFL, a novel deep learning model that predicts AFPs only relying on peptide sequences without using any structural information. AFP-MFL first constructs comprehensive feature profiles of AFPs, including contextual semantic information derived from a pre-trained protein language model, evolutionary information, and physicochemical properties. Subsequently, the co-attention mechanism is utilized to integrate contextual semantic information with evolutionary information and physicochemical properties separately. Extensive experiments show that AFP-MFL outperforms state-of-the-art models on four independent test datasets. Furthermore, the SHAP method is employed to explore each feature contribution to the AFPs prediction. Finally, a user-friendly web server of the proposed AFP-MFL is developed and freely accessible at http://inner.wei-group.net/AFPMFL/, which can be considered as a powerful tool for the rapid screening and identification of novel AFPs.

PMID:36631407 | DOI:10.1093/bib/bbac606

J Alzheimers Dis. 2023 Jan 3. doi: 10.3233/JAD-220832. Online ahead of print.

ABSTRACT

BACKGROUND: Loneliness has been highlighted as a risk factor for dementia. However, the nature of the relationship between loneliness and cognitive function prior to onset of dementia is unclear.

OBJECTIVE: The aim of this systematic review and meta-analysis was to examine the relationship between loneliness and cognitive function in samples screened for dementia at study commencement.

METHODS: Five electronic databases (PubMed, PsycNET, Web of Science, EBSCOhost, Scopus) were searched from inception to August 31, 2021. A narrative review and random-effects meta-analysis were conducted on studies meeting search criteria. PROSPERO registration number: CRD42020155539.

RESULTS: The sixteen studies that met inclusion criteria involved 30,267 individuals, with mean age ranging from 63.0 to 84.9 years. Studies varied in dementia screening criteria, measurement of loneliness and cognitive function, and statistical modeling approach. The narrative review indicated that loneliness was associated with poorer global cognition, episodic memory, working memory, visuospatial function, processing speed, and semantic verbal fluency. Results of the meta-analysis indicated that loneliness was negatively associated with global cognitive function (overall r = -0.08; 95% CI = -0.14, -0.02; n = 6). Due to lack of sufficient data and heterogeneity between studies, we were unable to explore associations with other cognitive domains or longitudinal associations.

CONCLUSION: Loneliness is associated with subtle impairment across multiple cognitive domains in older adults who were screened for dementia. Better characterization of this relationship will provide important information about how loneliness contributes to the clinical and pathological sequalae of AD and be informative for risk reduction and early detection strategies.

PMID:36617781 | DOI:10.3233/JAD-220832

Brain Commun. 2022 Dec 8;5(1):fcac322. doi: 10.1093/braincomms/fcac322. eCollection 2023.

ABSTRACT

The replication crisis poses important challenges to modern science. Central to this challenge is re-establishing ground truths or the most fundamental theories that serve as the bedrock to a scientific community. However, the goal to identify hypotheses with the greatest support is non-trivial given the unprecedented rate of scientific publishing. In this era of high-volume science, the goal of this study is to sample from one research community within clinical neuroscience (traumatic brain injury) and track major trends that have shaped this literature over the past 50 years. To do so, we first conduct a decade-wise (1980-2019) network analysis to examine the scientific communities that shape this literature. To establish the robustness of our findings, we utilized searches from separate search engines (Web of Science; Semantic Scholar). As a second goal, we sought to determine the most highly cited hypotheses influencing the literature in each decade. In a third goal, we then searched for any papers referring to 'replication' or efforts to reproduce findings within our >50 000 paper dataset. From this search, 550 papers were analysed to determine the frequency and nature of formal replication studies over time. Finally, to maximize transparency, we provide a detailed procedure for the creation and analysis of our dataset, including a discussion of each of our major decision points, to facilitate similar efforts in other areas of neuroscience. We found that the unparalleled rate of scientific publishing within the brain injury literature combined with the scarcity of clear hypotheses in individual publications is a challenge to both evaluating accepted findings and determining paths forward to accelerate science. Additionally, while the conversation about reproducibility has increased over the past decade, the rate of published replication studies continues to be a negligible proportion of the research. Meta-science and computational methods offer the critical opportunity to assess the state of the science and illuminate pathways forward, but ultimately there is structural change needed in the brain injury literature and perhaps others.

PMID:36601624 | PMC:PMC9806718 | DOI:10.1093/braincomms/fcac322

J Asthma. 2023 Aug;60(8):1584-1591. doi: 10.1080/02770903.2022.2164200. Epub 2023 Feb 3.

ABSTRACT

OBJECTIVES: To (1) describe primary health care utilization and (2) estimate the effect of primary care early follow-up, continuity, regularity, frequency, and long consultations on asthma hospital readmission, including secondary outcomes of emergency (ED) presentations, asthma preventer adherence, and use of rescue oral corticosteroids within 12 months.

METHODS: An Australian multi-site cohort study of 767 children aged 3-18 years admitted with asthma between 2017 and 2018, followed up for at least 12 months with outcome and primary care exposure data obtained through linked administrative datasets. We estimated the effect of primary care utilization through a modified Poisson regression adjusting for child age, asthma severity, socioeconomic status and self-reported GP characteristics.

RESULTS: The median number of general practitioner (GP) consultations, unique GPs and clinics visited was 9, 5, and 4, respectively. GP care was irregular and lacked continuity, only 152 (19.8%) children visited their usual GP on more than 60% of occasions. After adjusting for confounders, there was overall weak indication of effects due to any of the exposures. Increased frequency of GP visits was associated with reduced readmissions (4-14 visits associated with risk ratio of 0.71, 95% CI 0.50-1.00, p = 0.05) and ED presentations (>14 visits associated risk ratio 0.62, 95% CI 0.42-0.91, p = 0.02).

CONCLUSIONS: Our study demonstrates that primary care use by children with asthma is often irregular and lacking in continuity. This highlights the importance of improving accessibility, consistency in care, and streamlining discharge communication from acute health services.

PMID:36594684 | DOI:10.1080/02770903.2022.2164200

JMIR Public Health Surveill. 2022 Dec 23;8(12):e24938. doi: 10.2196/24938.

ABSTRACT

BACKGROUND: Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends.

OBJECTIVE: The key aims were to describe the development and application of the drug abuse ontology (DAO) as a framework for analyzing web-based and social media data to inform public health and substance use research in the following areas: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web forum data Prescription Drug Abuse Online Surveillance; analyzing patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the United States through analysis of Twitter and web forum data (eDrugTrends); assessing trends in the availability of novel synthetic opioids through the analysis of cryptomarket data (eDarkTrends); and analyzing COVID-19 pandemic trends in social media data related to 13 states in the United States as per Mental Health America reports.

METHODS: The domain and scope of the DAO were defined using competency questions from popular ontology methodology (101 ontology development). The 101 method includes determining the domain and scope of ontology, reusing existing knowledge, enumerating important terms in ontology, defining the classes, their properties and creating instances of the classes. The quality of the ontology was evaluated using a set of tools and best practices recognized by the semantic web community and the artificial intelligence community that engage in natural language processing.

RESULTS: The current version of the DAO comprises 315 classes, 31 relationships, and 814 instances among the classes. The ontology is flexible and can easily accommodate new concepts. The integration of the ontology with machine learning algorithms dramatically decreased the false alarm rate by adding external knowledge to the machine learning process. The ontology is recurrently updated to capture evolving concepts in different contexts and applied to analyze data related to social media and dark web marketplaces.

CONCLUSIONS: The DAO provides a powerful framework and a useful resource that can be expanded and adapted to a wide range of substance use and mental health domains to help advance big data analytics of web-based data for substance use epidemiology research.

PMID:36563032 | DOI:10.2196/24938

Health Informatics J. 2022 Oct-Dec;28(4):14604582221146720. doi: 10.1177/14604582221146720.

NO ABSTRACT

PMID:36548199 | DOI:10.1177/14604582221146720

Front Hum Neurosci. 2022 Dec 5;16:1027446. doi: 10.3389/fnhum.2022.1027446. eCollection 2022.

NO ABSTRACT

PMID:36545349 | PMC:PMC9760723 | DOI:10.3389/fnhum.2022.1027446

Schizophr Bull. 2023 Mar 15;49(2):498-506. doi: 10.1093/schbul/sbac157.

ABSTRACT

OBJECTIVES: Schizophrenia is a mental illness that presents with thought disorders including delusions and disorganized speech. Thought disorders have been regarded as a consequence of the loosening of associations between semantic concepts since the term "schizophrenia" was first coined by Bleuler. However, a mechanistic account of this cardinal disturbance in terms of functional dysconnection has been lacking. To evaluate how aberrant semantic connections are expressed through brain activity, we characterized large-scale network structures of concept representations using functional magnetic resonance imaging (fMRI).

STUDY DESIGN: We quantified various concept representations in patients' brains from fMRI activity evoked by movie scenes using encoding modeling. We then constructed semantic brain networks by evaluating the similarity of these semantic representations and conducted graph theory-based network analyses.

STUDY RESULTS: Neurotypical networks had small-world properties similar to those of natural languages, suggesting small-worldness as a universal property in semantic knowledge networks. Conversely, small-worldness was significantly reduced in networks of schizophrenia patients and was correlated with psychological measures of delusions. Patients' semantic networks were partitioned into more distinct categories and had more random within-category structures than those of controls.

CONCLUSIONS: The differences in conceptual representations manifest altered semantic clustering and associative intrusions that underlie thought disorders. This is the first study to provide pathophysiological evidence for the loosening of associations as reflected in randomization of semantic networks in schizophrenia. Our method provides a promising approach for understanding the neural basis of altered or creative inner experiences of individuals with mental illness or exceptional abilities, respectively.

PMID:36542452 | PMC:PMC10016409 | DOI:10.1093/schbul/sbac157

JMIR Med Inform. 2022 Dec 18. doi: 10.2196/43053. Online ahead of print.

NO ABSTRACT

PMID:36534739 | DOI:10.2196/43053

JMIR Med Inform. 2022 Dec 19;10(12):e42664. doi: 10.2196/42664.

NO ABSTRACT

PMID:36534448 | DOI:10.2196/42664

Biophys Rev. 2022 Dec 9:1-6. doi: 10.1007/s12551-022-01021-w. Online ahead of print.

NO ABSTRACT

PMID:36532871 | PMC:PMC9734456 | DOI:10.1007/s12551-022-01021-w

PeerJ Comput Sci. 2022 Dec 8;8:e1163. doi: 10.7717/peerj-cs.1163. eCollection 2022.

NO ABSTRACT

PMID:36532807 | PMC:PMC9748814 | DOI:10.7717/peerj-cs.1163

Front Public Health. 2022 Dec 2;10:1056575. doi: 10.3389/fpubh.2022.1056575. eCollection 2022.

NO ABSTRACT

PMID:36530722 | PMC:PMC9755165 | DOI:10.3389/fpubh.2022.1056575

Comput Biol Med. 2022 Dec 6;152:106415. doi: 10.1016/j.compbiomed.2022.106415. Online ahead of print.

NO ABSTRACT

PMID:36527782 | DOI:10.1016/j.compbiomed.2022.106415