BioTextQuest v2.0: An evolved tool for biomedical literature mining and concept discovery.

The process of navigating through the landscape of biomedical literature and performing searches or combining them with bioinformatics analyses can be daunting, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related repositories. Herein, we present BioTextQuest v2.0, a tool for biomedical literature mining. BioTextQuest v2.0 is an open-source online web portal for document clustering based on sets of selected biomedical terms, offering efficient management of information derived from PubMed abstracts. Employing established machine learning algorithms, the tool facilitates document clustering while allowing users to customize the analysis by selecting terms of interest. BioTextQuest v2.0 streamlines the process of uncovering valuable insights from biomedical research articles, serving as an agent that connects the identification of key terms like genes/proteins, diseases, chemicals, Gene Ontology (GO) terms, functions, and others through named entity recognition, and their application in biological research. Instead of manually sifting through articles, researchers can enter their PubMed-like query and receive extracted information in two user-friendly formats, tables and word clouds, simplifying the comprehension of key findings. The latest update of BioTextQuest leverages the EXTRACT named entity recognition tagger, enhancing its ability to pinpoint various biological entities within text. BioTextQuest v2.0 acts as a research assistant, significantly reducing the time and effort required for researchers to identify and present relevant information from the biomedical literature.

A machine learning framework for extracting information from biological pathway images in the literature.

There have been significant advances in literature mining, allowing for the extraction of target information from the literature. However, biological literature often includes biological pathway images that are difficult to extract in an easily editable format. To address this challenge, this study aims to develop a machine learning framework called the "Extraction of Biological Pathway Information" (EBPI). The framework automates the search for relevant publications, extracts biological pathway information from images within the literature, including genes, enzymes, and metabolites, and generates the output in a tabular format. For this, this framework determines the direction of biochemical reactions, and detects and classifies texts within biological pathway images. Performance of EBPI was evaluated by comparing the extracted pathway information with manually curated pathway maps. EBPI will be useful for extracting biological pathway information from the literature in a high-throughput manner, and can be used for pathway studies, including metabolic engineering.

CORACLE (COVID-19 liteRAture CompiLEr): A platform for efficient tracking and extraction of SARS-CoV-2 and COVID-19 literature, with examples from post-COVID with respiratory involvement.

Background: During the COVID-19 pandemic a need to process large volumes of publications emerged. As the pandemic is winding down, the clinicians encountered a novel syndrome - Post-acute Sequelae of COVID-19 (PASC) - that affects over 10 % of those who contract SARS-CoV-2 and presents a significant challenge in the medical field. The continuous influx of publications underscores a need for efficient tools for navigating the literature. Objectives: We aimed to develop an application which will allow monitoring and categorizing COVID-19-related literature through building publication networks and medical subject headings (MeSH) maps to identify key publications and networks. Methods: We introduce CORACLE (COVID-19 liteRAture CompiLEr), an innovative web application designed to analyse COVID-19-related scientific articles and to identify research trends. CORACLE features three primary interfaces: The "Search" interface, which displays research trends and citation links; the "Citation Map" interface, allowing users to create tailored citation networks from PubMed Identifiers (PMIDs) to uncover common references among selected articles; and the "MeSH" interface, highlighting current MeSH trends and their associations. Results: CORACLE leverages PubMed data to categorize literature on COVID-19 and PASC, aiding in the identification of relevant research publication hubs. Using lung function in PASC patients as a search example, we demonstrate how to identify and visualize the interactions between the relevant publications. Conclusion: CORACLE is an effective tool for the extraction and analysis of literature. Its functionalities, including the MeSH trends and customizable citation mapping, facilitate the discovery of emerging trends in COVID-19 and PASC research.

Unravelling Phyto-Compound Therapeutics against SRC Protein via FGF Pathway Targeting-A Comprehensive Approach Integrating Omics Data Analysis, Network Pharmacology, Virtual Screening, and Molecular Dynamics.

INTRODUCTION: Colorectal cancer is a complex condition influenced by genetic mutations and environmental factors. Due to its intricate nature, the diagnosis and treatment of this condition require a comprehensive approach that considers individual circumstances. The study aimed to identify genes linked with colorectal cancer and their therapeutic agents from natural bioactive compounds. METHODS: The significantly prognostic differentially expressed genes (DEGs) were screened out from NCBI Gene Expression Omnibus (GEO) datasets. A protein-protein interaction network was constructed using STRING Database, and key genes were identified using Network Analyzer and CytoNCA plugins within Cytoscape. Further analysis involved functional annotations, and biological pathways analysis, SRC mechanism to uncover the role of SRC in CRC. Additionally, we performed virtual screening and molecular docking, Physiochemical property analysis along with MD simulation study to propose suitable natural compounds for promising therapeutic targets. RESULTS: The study conducted differential gene expression analysis, identifying 3621 statistically significant genes, with 1467 upregulated and 2154 downregulated. The top ten genes with the highest degree, betweenness centrality, and closeness centrality in the PPI network were selected as key genes. The SRC gene was found to have the highest degree and closeness centrality. Functional annotation and pathway analysis of key genes with a specific focus on the SRC mechanism revealed that the SRC's role in activating the RAS-RAF-MEK-ERK and Wnt/ß-catenin pathways in CRC cells, promoting proliferation and invasion. Molecular modelling of SRC led to the screening of phyto-compounds from tropical fruits, with Rutinexhibiting a higher docking score compared to FDA-approved anticancer drugs. MD simulations over 100 ns and the post-MD analysis i.e. RMSD, SASA, RMSF, FEL, RG, Hydrogen bond, PCA, and MMPBSA, comprehended the stable and robust interactions of a protein-ligand complex. These findings suggest Rutin's potential as a potent natural molecule for treating CRC. The study concludes that SRC plays a pivotal role in CRC, influencing cellular processes critical to cancer development and Rutin has been found to be a promising SRC inhibitor, suggesting a potential alternative therapeutic strategy for CRC. The consistent molecular interactions of Rutin necessitate further validation through wet lab experiments, offering hope for individuals affected by CRC.

Identification of hub genes and molecular pathways in keratoconus by integrating bioinformatics and literature mining at the RNA level.

OBJECTIVE: Keratoconus (KC) is a condition characterized by progressive corneal steepening and thinning. However, its pathophysiological mechanism remains vague. We mainly performed literature mining to extract bioinformatic and related data on KC at the RNA level. The objective of this study was to explore the potential pathological mechanisms of KC by identifying hub genes and key molecular pathways at the RNA level. METHODS: We performed an exhaustive search of the PubMed database and identified studies that pertained to gene transcripts derived from diverse corneal layers in patients with KC. The identified differentially expressed genes were intersected, and overlapping genes were extracted for further analyses. Significantly enriched genes were screened using "Gene Ontology" (GO) and "Kyoto Encyclopedia of Genes and Genomes" (KEGG) analysis with the "Database for Annotation, Visualization, and Integrated Discovery" (DAVID) database. A protein-protein interaction (PPI) network was constructed for the significantly enriched genes using the STRING database. The PPI network was visualized using the Cytoscape software, and hub genes were screened via betweenness centrality values. Pathways that play a critical role in the pathophysiology of KC were discovered using the GO and KEGG analyses of the hub genes. RESULTS: 68 overlapping genes were obtained. Fifty genes were significantly enriched in 67 biological processes, and 16 genes were identified in 7 KEGG pathways. Moreover, 14 nodes and 32 edges were identified via the PPI network constructed using the STRING database. Multiple analyses identified 4 hub genes, 12 enriched biological processes, and 6 KEGG pathways. GO enrichment analysis showed that the hub genes are mainly involved in the positive regulation of apoptotic process, and KEGG analysis showed that the hub genes are primarily associated with the interleukin-17 (IL-17) and tumor necrosis factor (TNF) pathways. Overall, the matrix metalloproteinase 9, IL-6, estrogen receptor 1, and prostaglandin-endoperoxide synthase 2 were the potential important genes associated with KC. CONCLUSION: Four genes, matrix metalloproteinase 9, IL-6, estrogen receptor 1, and prostaglandin endoperoxide synthase 2, as well as IL-17 and TNF pathways, are critical in the development of KC. Inflammation and apoptosis may contribute to the pathogenesis of KC.

Transcriptomic signature of cancer cachexia by integration of machine learning, literature mining and meta-analysis.

BACKGROUND: Cancer cachexia is a severe metabolic syndrome marked by skeletal muscle atrophy. A successful clinical intervention for cancer cachexia is currently lacking. The study of cachexia mechanisms is largely based on preclinical animal models and the availability of high-throughput transcriptomic datasets of cachectic mouse muscles is increasing through the extensive use of next generation sequencing technologies. METHODS: Cachectic mouse muscle transcriptomic datasets of ten different studies were combined and mined by seven attribute weighting models, which analysed both categorical variables and numerical variables. The transcriptomic signature of cancer cachexia was identified by attribute weighting algorithms and was used to evaluate the performance of eleven pattern discovery models. The signature was employed to find the best combination of drugs (drug repurposing) for developing cancer cachexia treatment strategies, as well as to evaluate currently used cachexia drugs by literature mining. RESULTS: Attribute weighting algorithms ranked 26 genes as the transcriptomic signature of muscle from mice with cancer cachexia. Deep Learning and Random Forest models performed better in differentiating cancer cachexia cases based on muscle transcriptomic data. Literature mining revealed that a combination of melatonin and infliximab has negative interactions with 2 key genes (Rorc and Fbxo32) upregulated in the transcriptomic signature of cancer cachexia in muscle. CONCLUSIONS: The integration of machine learning, meta-analysis and literature mining was found to be an efficient approach to identifying a robust transcriptomic signature for cancer cachexia, with implications for improving clinical diagnosis and management of this condition.

Comparison of literature mining tools for variant classification: Through the lens of 50 RYR1 variants.

PURPOSE: The American College of Medical Genetics and Genomics and the Association for Molecular Pathology have outlined a schema that allows for systematic classification of variant pathogenicity. Although gnomAD is generally accepted as a reliable source of population frequency data and ClinGen has provided guidance on the utility of specific bioinformatic predictors, there is no consensus source for identifying publications relevant to a variant. Multiple tools are available to aid in the identification of relevant variant literature, including manually curated databases and literature search engines. We set out to determine the utility of 4 literature mining tools used for ascertainment to inform the discussion of the use of these tools. METHODS: Four literature mining tools including the Human Gene Mutation Database, Mastermind, ClinVar, and LitVar 2.0 were used to identify relevant variant literature for 50 RYR1 variants. Sensitivity and precision were determined for each tool. RESULTS: Sensitivity among the 4 tools ranged from 0.332 to 0.687. Precision ranged from 0.389 to 0.906. No single tool retrieved all relevant publications. CONCLUSION: At the current time, the use of multiple tools is necessary to completely identify the literature relevant to curate a variant.

Knowledge-Driven Experimental Discovery of Ce-Based Metal Oxide Composites for Selective Catalytic Reduction of NOx with NH3 through Interpretable Machine Learning.

Mining the scientific literature, combined with data-driven methods, may assist in the identification of optimized catalysts. In this paper, we employed interpretable machine learning to discover ternary metal oxides capable of selective catalytic reduction of nitrogen oxides with ammonia (NH3-SCR). Specifically, we devised a machine learning framework utilizing extreme gradient boosting (XGB), identified for its optimal performance, and SHapley Additive exPlanations (SHAP) to evaluate a curated database of 5654 distinct metal oxide composite catalytic systems containing cerium (Ce) element, with records of catalyst composition and preparation and reaction conditions. By virtual screening, this framework precisely pinpointed a CeO2-MoO3-Fe2O3 catalyst with superior NOx conversion, N2 selectivity, and resistance to H2O and SO2, as confirmed by empirical evaluations. Subsequent characterization affirmed its favorable structural, chemical bulk properties and reaction mechanism. Demonstrating the efficacy of combining knowledge-driven techniques with experimental validation and analysis, our strategy charts a course for analogous catalyst discoveries.

Quantitative Topic Analysis of Materials Science Literature Using Natural Language Processing.

Materials science research has garnered extensive attention from industry, society, policy, and academia. However, understanding the research landscape and extracting strategic insights are challenging due to the increasing diversity and volume of publications. This study proposes a natural language processing-based protocol for extracting text-encoded topics from a large volume of scientific literature, uncovering research interests of scientific communities, as well as convergence trends. We report a topic map, representing the materials science research landscape with text-mined 257 topics regarding biocompatible materials, structural materials, electrochemistry, or photonics. We analyze the topic map in terms of national research interests in materials science, revealing competitive positions and strategies of active nations. For example, it is found that the increasing trend of research interest in machine learning topic was captured in the United States earlier than other nations. Similarly, our journal-level analyses serve as reference information for journal recommendations and trend guidance, showing that the main topics and research interests of materials science journals slightly changed over time. Moreover, we build the topic association network which can highlight the status and future potential of interdisciplinary research, revealing research fields with high centrality in the network such as machine learning-enabled composite modeling, energy policy, or wearable electronics. This study offers insightful results on current and near-future materials science research landscapes, facilitating the understanding of stakeholders, amidst the fast-evolving and diverse knowledge of materials science.

pathCLIP: Detection of Genes and Gene Relations from Biological Pathway Figures through Image-Text Contrastive Learning.

In biomedical literature, biological pathways are commonly described through a combination of images and text. These pathways contain valuable information, including genes and their relationships, which provide insight into biological mechanisms and precision medicine. Curating pathway information across the literature enables the integration of this information to build a comprehensive knowledge base. While some studies have extracted pathway information from images and text independently, they often overlook the correspondence between the two modalities. In this paper, we present a pathway figure curation system named pathCLIP for identifying genes and gene relations from pathway figures. Our key innovation is the use of an image-text contrastive learning model to learn coordinated embeddings of image snippets and text descriptions of genes and gene relations, thereby improving curation. Our validation results, using pathway figures from PubMed, showed that our multimodal model outperforms models using only a single modality. Additionally, our system effectively curates genes and gene relations from multiple literature sources. A case study on extracting pathway information from non-small cell lung cancer literature further demonstrates the usefulness of our curated pathway information in enhancing related pathways in the KEGG database.

Applications of cutting-edge artificial intelligence technologies in biomedical literature and document mining.

The biomedical literature is a vast and invaluable resource for biomedical research. Integrating knowledge from the literature with biomedical data can help biological studies and the clinical decision-making process. Efforts have been made to gather information from the biomedical literature and create biomedical knowledge bases, such as KEGG and Reactome. However, manual curation remains the primary method to retrieve accurate biomedical entities and relationships. Manual curation becomes increasingly challenging and costly as the volume of biomedical publications quickly grows. Fortunately, recent advancements in Artificial Intelligence (AI) technologies offer the potential to automate the process of curating, updating, and integrating knowledge from the literature. Herein, we highlight the AI capabilities to aid in mining knowledge and building the knowledge base from the biomedical literature.

[Prescription and medication rules of traditional Chinese medicine for prevention and treatment of diabetic microangiopathy based on literature mining].

This study explored the prescription and medication rules of traditional Chinese medicine(TCM) in the prevention and treatment of diabetic microangiopathy based on literature mining. Relevant literature on TCM against diabetic microangiopathy was searched and prescriptions were collected. Microsoft Excel 2021 software was used to establish a prescription database, and an analysis was conducted on the frequency, properties, flavors, meridian tropism, and efficacy classifications of drugs. Association rule analysis, cluster analysis, and factor analysis were performed using SPSS Modeler 18.0 and SPSS Statistics 26.0 software. The characteristic active components and mechanisms of action of medium-high frequency drugs in the analysis of medication rules were explored through li-terature mining. A total of 1 327 prescriptions were included in this study, involving 411 drugs, with a total frequency reaching 19 154 times. The top five high-frequency drugs were Astragali Radix, Angelicae Sinensis Radix, Poria, Salviae Miltiorrhizae Radix et Rhizoma, and Rehmanniae Radix. The cold and warm drugs were used in combination. Drugs were mainly sweet, followed by bitter and pungent, and acted on the liver meridian. The majority of drugs were effective in tonifying deficiency, clearing heat, activating blood, and resolving stasis. Association rule analysis identified the highly supported drug pair of Astragali Radix-Angelicae Sinensis Radix and the highly confident drug combination of Poria-Alismatis Rhizoma-Corni Fructus. The strongest correlation was found among Astragali Radix, Angelicae Sinensis Radix, Poria, and Salviae Miltiorrhizae Radix et Rhizoma through the complex network analysis. Cluster analysis identified nine categories of drug combinations, while factor analysis identified 16 common factors. The analysis of active components in high-frequency drugs for the treatment of diabetic microangiopathy revealed that these effective components mainly exerted their effects by inhibiting oxidative stress and suppressing inflammatory reactions. The study found that the pathogenesis of diabetic microangiopathy was primarily characterized by deficiency in origin, with a combination of deficiency and excess. Deficiency was manifested as Qi deficiency and blood deficiency, while excess as phlegm-heat and blood stasis. The key organ involved in the pathological changes was the liver. The treatment mainly focused on supplementing Qi and nourishing blood, supplemented by clearing heat, coo-ling blood, activating blood, and dredging collaterals. Commonly used formulas included Danggui Buxue Decoction, Liuwei Dihuang Pills, Erzhi Pills, and Buyang Huanwu Decoction. The mechanisms of action of high-frequency drugs in the treatment of diabetic microangiopathy were often related to the inhibition of oxidative stress and suppression of inflammatory reactions. These findings can provide references for the clinical treatment of diabetic microangiopathy and the development of targeted drugs.

OTTM: an automated classification tool for translational drug discovery from omics data.

Omics data from clinical samples are the predominant source of target discovery and drug development. Typically, hundreds or thousands of differentially expressed genes or proteins can be identified from omics data. This scale of possibilities is overwhelming for target discovery and validation using biochemical or cellular experiments. Most of these proteins and genes have no corresponding drugs or even active compounds. Moreover, a proportion of them may have been previously reported as being relevant to the disease of interest. To facilitate translational drug discovery from omics data, we have developed a new classification tool named Omics and Text driven Translational Medicine (OTTM). This tool can markedly narrow the range of proteins or genes that merit further validation via drug availability assessment and literature mining. For the 4489 candidate proteins identified in our previous proteomics study, OTTM recommended 40 FDA-approved or clinical trial drugs. Of these, 15 are available commercially and were tested on hepatocellular carcinoma Hep-G2 cells. Two drugs-tafenoquine succinate (an FDA-approved antimalarial drug targeting CYC1) and branaplam (a Phase 3 clinical drug targeting SMN1 for the treatment of spinal muscular atrophy)-showed potent inhibitory activity against Hep-G2 cell viability, suggesting that CYC1 and SMN1 may be potential therapeutic target proteins for hepatocellular carcinoma. In summary, OTTM is an efficient classification tool that can accelerate the discovery of effective drugs and targets using thousands of candidate proteins identified from omics data. The online and local versions of OTTM are available at http://otter-simm.com/ottm.html.

Insights into Changing Dermatophyte Spectrum in India Through Analysis of Cumulative 161,245 Cases Between 1939 and 2021.

Dermatophytosis is one of the most common superficial infections of the skin affecting nearly one-fifth of the world population at any given time. With nearly 30% of worldwide terbinafine-resistance cases in Trichophyton mentagrophytes/Trichophyton interdigitale and Trichophyton rubrum reported from India in recent years, there is a significant burden of the emerging drug resistance epidemic on India. Here, we carry out a comprehensive retrospective analysis of dermatophytosis in India using 1038 research articles pertaining to 161,245 cases reported from 1939 to 2021. We find that dermatophytosis is prevalent in all parts of the country despite variable climatic conditions in different regions. Our results show T. rubrum as the most prevalent until 2015, with a sudden change in dermatophyte spectrum towards T. mentagrophytes/T. interdigitale complex since then. We also carried out an 18S rRNA-based phylogenetics and an average nucleotide identity-and single nucleotide polymorphism-based analysis of available whole genomes and find very high relatedness among the prevalent dermatophytes, suggesting geographic specificity. The comprehensive epidemiological and phylogenomics analysis of dermatophytosis in India over the last 80 years, presented here, would help in region-specific prevention, control and treatment of dermatophyte infections, especially considering the large number of emerging resistance cases.

Literature Analysis of Clinical Application of Sniffing Therapy / 世界科学技术-中医药现代化

Objective To analyze the characteristics of sniffing therapy in China and abroad,to provide data support and reference for the establishment of clinical application guidelines for sniffing therapy,and to promote the multidimensional development of sniffing therapy.Methods Using the literature on sniffing therapy to retrieve clinical research,which were retrieved from the databases of CNKI,Wanfang,VIP,PubMed,WOS core database,SD as the data sources,we summarized the type of treatment,treatment route,primary disease and Indications,drug composition,treatment mode,treatment dose,duration,frequency and periodicity.Results A total of 158 papers were screened,of which the treatment types were mainly plant essential oils;direct sniffing was the main treatment route in sniffing therapy;auxiliary tools were mostly cotton(balls)or diffusers,etc.;main treatment for neurological disorders,brain disorders and affective disorders were the most common;lavender was the most frequently used drug;drug forms were widely used in single prescriptions,often in combination with western drugs;the duration of drug use was commonly 30 min.The ambiguous dose of clinical trials is mostly 2 drops;its frequency of administration is mostly twice a day;the duration of treatment is more common in 15-30 day.Conclusion Sniffing therapy has been widely studied,with the particular advantages of being non-invasive,free of side effects,simple to operate and cost effective.But the strong volatility of aromatic substances and the subjective nature of odour recognition,coupled with the lack of scientific and unified objective treatment standards,make a slight difference in efficacy.Therefore,based on the clarification of the characteristics of sniffing therapy,standardization and innovation are of great significance to its efficient development.

Prescription and medication rules of traditional Chinese medicine for prevention and treatment of diabetic microangiopathy based on literature mining / 中国中药杂志

This study explored the prescription and medication rules of traditional Chinese medicine(TCM) in the prevention and treatment of diabetic microangiopathy based on literature mining. Relevant literature on TCM against diabetic microangiopathy was searched and prescriptions were collected. Microsoft Excel 2021 software was used to establish a prescription database, and an analysis was conducted on the frequency, properties, flavors, meridian tropism, and efficacy classifications of drugs. Association rule analysis, cluster analysis, and factor analysis were performed using SPSS Modeler 18.0 and SPSS Statistics 26.0 software. The characteristic active components and mechanisms of action of medium-high frequency drugs in the analysis of medication rules were explored through li-terature mining. A total of 1 327 prescriptions were included in this study, involving 411 drugs, with a total frequency reaching 19 154 times. The top five high-frequency drugs were Astragali Radix, Angelicae Sinensis Radix, Poria, Salviae Miltiorrhizae Radix et Rhizoma, and Rehmanniae Radix. The cold and warm drugs were used in combination. Drugs were mainly sweet, followed by bitter and pungent, and acted on the liver meridian. The majority of drugs were effective in tonifying deficiency, clearing heat, activating blood, and resolving stasis. Association rule analysis identified the highly supported drug pair of Astragali Radix-Angelicae Sinensis Radix and the highly confident drug combination of Poria-Alismatis Rhizoma-Corni Fructus. The strongest correlation was found among Astragali Radix, Angelicae Sinensis Radix, Poria, and Salviae Miltiorrhizae Radix et Rhizoma through the complex network analysis. Cluster analysis identified nine categories of drug combinations, while factor analysis identified 16 common factors. The analysis of active components in high-frequency drugs for the treatment of diabetic microangiopathy revealed that these effective components mainly exerted their effects by inhibiting oxidative stress and suppressing inflammatory reactions. The study found that the pathogenesis of diabetic microangiopathy was primarily characterized by deficiency in origin, with a combination of deficiency and excess. Deficiency was manifested as Qi deficiency and blood deficiency, while excess as phlegm-heat and blood stasis. The key organ involved in the pathological changes was the liver. The treatment mainly focused on supplementing Qi and nourishing blood, supplemented by clearing heat, coo-ling blood, activating blood, and dredging collaterals. Commonly used formulas included Danggui Buxue Decoction, Liuwei Dihuang Pills, Erzhi Pills, and Buyang Huanwu Decoction. The mechanisms of action of high-frequency drugs in the treatment of diabetic microangiopathy were often related to the inhibition of oxidative stress and suppression of inflammatory reactions. These findings can provide references for the clinical treatment of diabetic microangiopathy and the development of targeted drugs.

The LCNetWork: An electronic representation of the mRNA-lncRNA-miRNA regulatory network underlying mechanisms of non-small cell lung cancer in humans, and its explorative analysis.

Advancement in genomics technologies have made it possible to identify the genes and their interaction/regulation involved in NSCLC, but the interaction information are scattered over the literature. Thus, there is a need of assembling all the available interaction/regulation information in a single platform which will provide a complete view of NSCLC mechanisms. Further, analysis of the mechanisms underlying NSCLC in humans would benefit substantially from easy access to an electronic network. We, therefore, used manual literature curation and integrated all the existing knowledge of NSCLC biomarkers (mRNA, lncRNA and miRNA) into a single conceptual platform represented through a biological network, termed the LCNetWork which represents 345 genes (195 mRNA, 46 lncRNA and 104 miRNA) and 500 direct interactions that are crucial to the regulation of NSCLC in humans. Furthermore, through exploratory data analysis, we have reported four mRNAs (PLK1, ZNF300, NKX2-1, and EGR1), one lncRNA (UCA1) and five miRNAs (MIR133B, MIR326, MIR429, MIR451A, and MIR944) and MIR193A/UCA1/EGFR axis crucial to NSCLC mechanisms. The GO results suggested their role in post-transcriptional gene silencing and RNA polymerase II activities as causes leading to cancer metastasis. The LCNetWork provides up-to-date knowledge of the genes and their interaction/regulation information in NSCLC and is capable of revealing multiple cancer-gene landscapes. Additionally, the LCNetWork has been provided in the Network Data Exchange portal as an electronic circuit for growth by community-level effort.

Screening of traditional Chinese medicine monomers as ribonucleotide reductase M2 inhibitors for tumor treatment.

BACKGROUND: Ribonucleotide reductase (RR) is a key enzyme in tumor proliferation, especially its subunit-RRM2. Although there are multiple therapeutics for tumors, they all have certain limitations. Given their advantages, traditional Chinese medicine (TCM) monomers have become an important source of anti-tumor drugs. Therefore, screening and analysis of TCM monomers with RRM2 inhibition can provide a reference for further anti-tumor drug development. AIM: To screen and analyze potential anti-tumor TCM monomers with a good binding capacity to RRM2. METHODS: The Gene Expression Profiling Interactive Analysis database was used to analyze the level of RRM2 gene expression in normal and tumor tissues as well as RRM2's effect on the overall survival rate of tumor patients. TCM monomers that potentially act on RRM2 were screened via literature mining. Using AutoDock software, the screened monomers were docked with the RRM2 protein. RESULTS: The expression of RRM2 mRNA in multiple tumor tissues was significantly higher than that in normal tissues, and it was negatively correlated with the overall survival rate of patients with the majority of tumor types. Through literature mining, we discovered that berberine, ursolic acid, gambogic acid, cinobufagin, quercetin, daphnetin, and osalmide have inhibitory effects on RRM2. The results of molecular docking identified that the above TCM monomers have a strong binding capacity with RRM2 protein, which mainly interacted through hydrogen bonds and hydrophobic force. The main binding sites were Arg330, Tyr323, Ser263, and Met350. CONCLUSION: RRM2 is an important tumor therapeutic target. The TCM monomers screened have a good binding capacity with the RRM2 protein.

Mining on Alzheimer's diseases related knowledge graph to identity potential AD-related semantic triples for drug repurposing.

BACKGROUND: To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study the relations between Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention. RESULTS: Among three knowledge graph completion models, TransE outperformed the other two (MR = 10.53, Hits@1 = 0.28). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge. CONCLUSION: This paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.

Literature Mining of Disease Associated Noncoding RNA in the Omics Era.

Noncoding RNAs (ncRNA) are transcripts without protein-coding potential that play fundamental regulatory roles in diverse cellular processes and diseases. The application of deep sequencing experiments in ncRNA research have generated massive omics datasets, which require rapid examination, interpretation and validation based on exiting knowledge resources. Thus, text-mining methods have been increasingly adapted for automatic extraction of relations between an ncRNA and its target or a disease condition from biomedical literature. These bioinformatics tools can also assist in more complex research, such as database curation of candidate ncRNAs and hypothesis generation with respect to pathophysiological mechanisms. In this concise review, we first introduced basic concepts and workflow of literature mining systems. Then, we compared available bioinformatics tools tailored for ncRNA studies, including the tasks, applicability, and limitations. Their powerful utilities and flexibility are demonstrated by examples in a variety of diseases, such as Alzheimer's disease, atherosclerosis and cancers. Finally, we outlined several challenges from the viewpoints of both system developers and end users. We concluded that the application of text-mining techniques will booster disease-associated ncRNA discoveries in the biomedical literature and enable integrative biology in the current omics era.