This review paper provides an extensive analysis of the rapidly evolving convergence of deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements in deep learning and the increasing recognition of lncRNAs as crucial components in various biological processes, this review aims to offer a comprehensive examination of these intertwined research areas. The remarkable progress in deep learning necessitates thoroughly exploring its latest applications in the study of lncRNAs. Therefore, this review provides insights into the growing significance of incorporating deep learning methodologies to unravel the intricate roles of lncRNAs. By scrutinizing the most recent research spanning from 2021 to 2023, this paper provides a comprehensive understanding of how deep learning techniques are employed in investigating lncRNAs, thereby contributing valuable insights to this rapidly evolving field. The review is aimed at researchers and practitioners looking to integrate deep learning advancements into their lncRNA studies.

In deep reinforcement learning (RL), learning stochastic and multimodal policies are crucial for various tasks, but most continuous control algorithms model the policy using a deterministic or unimodal Gaussian distribution. Despite being designed to inherently learn a stochastic policy under the maximum entropy RL framework, soft actor–critic (SAC) also uses a factorized Gaussian policy for tractable optimization, which not only restricts the expressiveness of the policy but also ignores correlations among the components of the action vector. In this article, we revisit the approach of employing normalizing flow for SAC policy, justified by the change of variable theorem, and then propose a state-dependent nonvolume preserving (SD-NVP) architecture suitable for SAC learning. In addition, we introduce a generative adversarial SAC (GASAC) that implicitly defines and optimizes various divergences without calculating the normalization constant through a generative adversarial loss. Experimental results on multigoal environment and MuJoCo continuous control tasks suite demonstrate that GASAC model multimodal policy and learn policy more stably in terms of cumulative return.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The fact that medical terms require special expertise and are becoming increasingly complex makes it difficult to employ natural language processing techniques in medical informatics. Several human-validated reference standards for medical terms have been developed to evaluate word embedding models using the semantic similarity and relatedness of medical word pairs. However, there are very few reference standards in non-English languages. In addition, because the existing reference standards were developed a long time ago, there is a need to develop an updated standard to represent recent findings in medical sciences. Objective: We propose a new Korean word pair reference set to verify embedding models. Methods: From January 2010 to December 2020, 518 medical textbooks, 72,844 health information news, and 15,698 medical research articles were collected, and the top 10,000 medical terms were selected to develop medical word pairs. Sixteen attending physicians participated in the verification of the developed set with 607 word pairs. Results: The proportion of word pairs answered by all participants was 90.8% (551/607) for the similarity task and 86.5% (525/605) for the relatedness task. The similarity and relatedness of the word pair showed a high correlation (ρ=0.70, P<.001). The intraclass correlation coefficients to assess the inter-rater agreements of the word pair sets were 0.47 on the similarity task and 0.53 on the relatedness task. The final reference standard was 604 word pairs for the similarity task and 599 word pairs for relatedness, excluding word pairs with answers corresponding to outliers and word pairs that were answered by less than 50% of all the respondents. When FastText models were applied to the final reference standard word pair sets, the embedding models learning medical documents had a higher correlation between the calculated cosine similarity scores compared to human-judged similarity and relatedness scores (ρ=0.12, namu vs. ρ=0.47, with medical text for the similarity task and ρ=0.02, with namu vs. ρ=0.30, with medical text for the relatedness task). Conclusions: Korean medical word pair reference standard sets for semantic similarity and relatedness were developed based on medical documents from the past 10 years. It is expected that our word pair reference sets will be actively utilized in the development of medical and multilingual natural language processing technology in the future.

In general survival analysis, multiple studies have considered a single failure time corresponding to the time to the event of interest or to the occurrence of multiple events under the assumption that each event is independent. However, in real-world events, one event may impact others. Essentially, the potential structure of the occurrence of multiple events can be observed in several survival datasets. The interrelations between the times to the occurrences of events are immensely challenging to analyze because of the presence of censoring. Censoring commonly arises in longitudinal studies in which some events are often not observed for some of the subjects within the duration of research. Although this problem presents the obstacle of distortion caused by censoring, the advanced multivariate survival analysis methods that handle multiple events with censoring make it possible to measure a bivariate probability density function for a pair of events. Considering this improvement, this paper proposes a method called censored network estimation to discover partially correlated relationships and construct the corresponding network composed of edges representing non-zero partial correlations on multiple censored events. To demonstrate its superior performance compared to conventional methods, the selecting power for the partially correlated events was evaluated in two types of networks with iterative simulation experiments. Additionally, the correlation structure was investigated on the electronic health records dataset of the times to the first diagnosis for newborn babies in South Korea. The results show significantly improved performance as compared to edge measurement with competitive methods and reliability in terms of the interrelations of real-life diseases.

Objectives: Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differences may occur. Previous public databases can be used for clinical studies, but there is no common standard that would allow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases. Recent commercial ECG machines offer diagnoses similar to those of a physician. Therefore, the purpose of this study was to construct a standardized ECG database using computerized diagnoses.Methods: The constructed database was standardized using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Observational Medical Outcomes Partnership–common data model (OMOP-CDM), and data were then categorized into 10 groups based on the Minnesota classification. In addition, to extract high-quality waveforms, poor-quality ECGs were removed, and database bias was minimized by extracting at least 2,000 cases for each group. To check database quality, the difference in baseline displacement according to whether poor ECGs were removed was analyzed, and the usefulness of the database was verified with seven classification models using waveforms.Results: The standardized KURIAS-ECG database consists of high-quality ECGs from 13,862 patients, with about 20,000 data points, making it possible to obtain more than 2,000 for each Minnesota classification. An artificial intelligence classification model using the data extracted through SNOMED-CT showed an average accuracy of 88.03%.Conclusions: The KURIAS-ECG database contains standardized ECG data extracted from various machines. The proposed protocol should promote cardiovascular disease research using big data and artificial intelligence.

Supply chain mapping is crucial for global companies to identify and mitigate potential risks. Although natural language processing techniques are analyzed to extract supply chain maps from textual sources to automate the process, they require extensive manual annotation of training data, limiting the scalability and efficiency of these approaches. This study explores novel methods using large language models to fully automate supply chain mapping, focusing on synthetic data generation and improving relation classification techniques. This study investigates the use of large language models for relation classification in supply chain contexts and explores their potential for generating synthetic datasets. The performance of these synthetic datasets is compared with those of manually annotated datasets. The experimental results demonstrate that synthetic data outperform manual data if manual data are insufficient. To demonstrate its practical applicability, this study applies the developed method to generate a supply chain map from mining-related news articles. The subsequent supply chain map visualization for mining-related industries indicates the effectiveness of the method in automated, real-time mapping using publicly available information, enabling the rapid identification of supply network changes. This study contributes to the supply chain management and natural language processing fields by advancing sophisticated, AI-driven supply chain analysis tools.