Integrated Data Analysis Research Articles

Integrated analysis of single-cell RNA sequencing and bulk transcriptome data identifies a pyroptosis-associated diagnostic model for Parkinson’s disease

Integrated analysis of single-cell RNA sequencing and bulk transcriptome data identifies a pyroptosis-associated diagnostic model for Parkinson’s disease

Towards Precision Biocatalysis ‐ Leveraging Inline NMR for Autonomous Experimentation in Flow Reactors

AbstractReactor automation is a transformative force for chemical processes, but the potential of reaction monitoring for machine‐assisted autonomous biocatalytic reaction optimization is still largely unexplored. To address this gap, we report on automated reactor optimization for biocatalytic flow‐through microreactors. For this purpose, the inline NMR analysis of an enzymatically catalyzed stereoselective reduction of a prochiral diketone was combined with a self‐developed open‐source analysis and control software. The algorithm is continuously fed with spectra from a benchtop NMR instrument acquired from a reaction solution from a microreactor filled with biocatalytically active materials and adjusts the flow rate of the pumps to achieve predetermined target concentrations of the product. We show that through this automated coupling of data analysis and process parameterization, for example, maximum conversion efficiency can be achieved for a given bioreactor. This work illustrates the potential of inline NMR reaction monitoring for biocatalytic processes and provides a starting point for innovation to develop automated processes for precision biocatalysis through integrated data analysis.

Corrigendum: Integrated analysis of single-cell and bulk RNA sequencing data reveals a cellular senescence-related signature in hepatocellular carcinoma

Corrigendum: Integrated analysis of single-cell and bulk RNA sequencing data reveals a cellular senescence-related signature in hepatocellular carcinoma

Interpretation of the CDISC therapeutic area user guide for traditional Chinese medicine- acupuncture

Improving research data quality is a crucial aspect of high-quality clinical research of acupuncture and moxibustion. The Clinical Data Interchange Standards Consortium (CDISC) has developed a series of standards to support the collection, tabulation, and analysis of clinical research data. To enhance the efficiency and quality of clinical research in acupuncture, as well as to facilitate the integration, sharing, and secondary analysis of data from multiple similar studies, CDISC has developed and released the CDISC therapeutic area user guide for traditional Chinese medicine acupuncture (TAUG-TCM-Acupuncture). This paper introduced the TAUG-TCM-Acupuncture, including relevant concepts, main content, and its application in clinical research of acupuncture, with illustrative examples.

What makes this online course tick?

The Community of Inquiry (CoI) (Garrison, Anderson & Archer, 1999; 2010) is well-established, widely accepted, and practical framework. It highlights social, cognitive and teaching presences in effective online learning communities. 23 Things International (23T) is an online, flexible course for research students and early career researchers. The course is self-directed and promotes the building of academic and research networks, familiarity with resources underpinning research, and establishing professional profiles. It was first implemented in 2020 and has been refined annually to better meet needs of participants. One of the features of the programme is the ‘pod group’ arrangement. Participants are connected to each other in groups (the pods) based on research interests. The size of the pods is kept to about ten, so to facilitate manageable and meaningful communication and discussion of topics within the programme. Each year, 23T participants have reported pleasing satisfaction levels. Using the CoI, this investigation examined possible reasons why. The study aimed to: (a) identify practices and structural mechanisms that seem to underpin the experienced success of 23T; and (b) articulate plans for more targeted evaluation-review-development. Data sources included: annual reports (2020-2023) on the course containing integrated analyses of participant evaluation data; the outline and structure of 23T, showing the fabric of the course, how it is organised and arranged and how it implemented; facilitators’ knowledge and experiences of conducting 23T. Data were organised using a simple matching process (Garrison et al, 1999). CoI elements were matched with practice examples from implementation, and evidence from the data. Positives, negatives, possibilities and conclusions were drawn about gaps and potential. Examples of evidence drawn the from the data sources about the 23T programme include: Teaching Presence: including the use of prompts built into the material presented online and the facilitators’ spoken and written interactions with participants through live activities and through blogs (boosting authentic participant application of their learning); Social Presence: pod groups and how they were arranged and supported; Cognitive Presence: range of topics covered and the participant activity surrounding the topics. To build on theoretical work investigating 23T (Stein, Sim & Rose, 2023), this study is helping us to pinpoint concrete elements that seem to be influencing online course effectiveness and enabling identification of ways to strengthen 23T’s future flexibility and attractiveness. This is pertinent because of the uniqueness of 23T, namely, that it is online, flexible and self-guided and has no formal assessment expectations and does not provide formal credit. Gauging what counts as effectiveness and success is quite different therefore from award courses normally offered by higher education institutions. Similar application of the CoI using our matching approach may be helpful for others in different contexts and settings who are planning and/or implementing online courses.

GutMetaNet: an integrated database for exploring horizontal gene transfer and functional redundancy in the human gut microbiome.

Metagenomic studies have revealed the critical roles of complex microbial interactions, including horizontal gene transfer (HGT) and functional redundancy (FR), in shaping the gut microbiome's functional capacity and resilience. However, the lack of comprehensive data integration and systematic analysis approaches has limited the in-depth exploration of HGT and FR dynamics across large-scale gut microbiome datasets. To address this gap, we present GutMetaNet (https://gutmetanet.deepomics.org/), a first-of-its-kind database integrating extensive human gut microbiome data with comprehensive HGT and FR analyses. GutMetaNet contains 21567 human gut metagenome samples with whole-genome shotgun sequencing data related to various health conditions. Through systematic analysis, we have characterized the taxonomic profiles andFR profiles, and identified 14636 HGT events using a shared reference genome database across the collected samples. These HGT events have been curated into 8049 clusters, which are annotated with categorized mobile genetic elements, including transposons, prophages, integrative mobilizable elements, genomic islands, integrative conjugative elements and group II introns. Additionally, GutMetaNet incorporates automated analyses and visualizations for the HGT events and FR, serving as an efficient platform for in-depth exploration of the interactions among gut microbiome taxa and their implications for human health.

Ythdf2 facilitates precursor miR-378/miR-378-5p maturation to support myogenic differentiation.

Ythdf2 is known to mediate mRNA degradation in an m6A-dependent manner, and it has been shown to play a role in skeletal muscle differentiation. Recently, Ythdf2 was also found to bind to m6A-modified precursor miRNAs and regulate their maturation. However, it remains unknown whether this mechanism is related to the regulation of myogenesis by Ythdf2. Here, we observed that Ythdf2 knockdown significantly suppressed myotube formation and impacted miRNAs expression during myogenic differentiation. Through integrated analysis of miRNA and mRNA sequencing data, miR-378 and miR-378-5p were identified as important targets of Ythdf2 in myogenesis. Mechanically, Ythdf2 was found to interact with core components of the pre-miRNA processor complex, namely DICER1 and TARBP2, thereby facilitating the maturation of pre-miR-378/miR-378-5p in an m6A-dependent manner and resulting in an increase in the expression levels of mature miR-378 and miR-378-5p. Moreover, the downregulation of either miR-378 or miR-378-5p significantly inhibited myotube formation, while the forced expression of miR-378 or miR-378-5p could partially rescued Ythdf2 knockdown-induced suppression of myogenic differentiation by activating the mTOR pathway. Collectively, our results for the first time suggest that Ythdf2 regulates myogenic differentiation via mediating pre-miR-378/miR-378-5p maturation, which might provide new insights into the molecular mechanisms underlying m6A modification in the regulation of myogenesis.

MIMR: Development of a Web-Based System for miRNA and mRNA Integrated Analysis.

The human body is a complex network of systems that is harmonized with multiple biological components. To understand these interactions is very challenging. With rapid development of advanced sequencing technologies, massive amounts of data such as mRNA, miRNA are rapidly accumulated. The integrated analysis of mRNA-miRNA has brought an extensive understanding of complex biological systems and pathological mechanisms. MicroRNAs (miRNAs) are small non-coding RNAs that intricately regulate target gene products, resulting in the inhibition of gene expression. While these miRNAs play crucial roles in essential biological processes-ranging from immunity and metabolism to cell death-their specific impacts on diseases remain unknown. Recent studies have been focused on the integration of miRNA and mRNA expression to reveal the underlying biological pathways and mechanisms responsible for disease manifestation. We proposed a novel approach for integrative analysis of miRNA and mRNA expression data and developed MIMR (Integrative Analysis of miRNA and mRNA), a web-based application that leverages the Random Walk with Restart (RWR) algorithm. MIMR incorporates both direct and indirect interactions by utilizing protein-protein interaction (PPI) networks and experimentally validated mRNA-miRNA target interactions. MIMR provides comprehensive results, including novel pathological pathways associated with a specific disease and interactive network diagrams representing the mRNAs and miRNAs. We applied it to Alzheimer and breast cancer data and successfully identified the novel biological pathways related to these diseases. In summary, MIMR will offer a deeper insight into the hidden mechanisms of diseases and identify potential therapeutic strategies through integrated analysis of miRNAs and mRNAs.

A Data Integration Method for New Advances in Development Cognitive Neuroscience

Combining existing datasets to investigate key questions in developmental cognitive neuroscience brings exciting opportunities and unique challenges. However, many data pooling methods require identical or harmonized methodologies that are often not feasible. We propose Integrative Data Analysis (IDA) as a promising framework to advance developmental cognitive neuroscience with secondary data analysis. IDA serves to test hypotheses by combining data of the same construct from commensurate (but not identical) measures. To overcome idiosyncrasies of neuroimaging data, IDA explicitly evaluates if measures across studies assess the same construct. Moreover, IDA allows investigators to examine meaningful individual variability by de-confounding source-specific differences. To demonstrate IDA’s potential, we explain foundational concepts, outline necessary steps, and apply IDA to volumetric measures of hippocampal subfields from 443 4- to 17-year-olds across three independent studies. We identified commensurate measures of Cornu Ammonis (CA) 1, dentate gyrus (DG)/CA3, and Subiculum (Sub). Model testing supported use of IDA to create IDA factor scores. We found age-related differences in DG/CA3, not but CA1 and Sub volume in the integrated dataset. By successfully demonstrating IDA, our hope is that future innovations come from the combination of existing neuroimaging data to create representative integrated samples when testing critical developmental questions.

Multi-omics Data Integration Analysis Identified Therapeutic Targets and Potential Reuse Drugs for Osteoporosis.

To facilitate drug discovery and development for the treatment of osteoporosis. With global aging, osteoporosis has become a common problem threatening the health of the elderly. It is of important clinical value to explore new targets for drug intervention and develop promising drugs for the treatment of osteoporosis. To understand the major molecules that mediate the communication between the cell populations of bone marrow-derived mesenchymal stem cells (BM-MSCs) in osteoporosis and osteoarthritis patients and identify potential reusable drugs for the treatment of osteoporosis. Single-cell RNA sequencing (scRNA-seq) data of BM-MSCs in GSE147287 dataset were classified using the Seurat package. CellChat was devoted to analyzing the ligand-receptor pairs (LR pairs) contributing to the communication between BM-MSCs subsets. The LR pairs that were differentially expressed between osteoporosis samples and control samples and significantly correlated with immune score were screened in the GSE35959 dataset, and the differentially expressed gene in both GSE35959 and GSE13850 data sets were identified as targets from a single ligand or receptor. The therapeutic drugs for osteoporosis were screened by network proximity method, and the top-ranked drugs were selected for molecular docking and molecular dynamics simulation with the target targets. Twelve subsets of BM-MSCs were identified, of which CD45-BM-MSCS_4, CD45-BM- MSCS_5, and CD45+ BM-MSCs_5 subsets showed significantly different distributions between osteoporosis samples and osteoarthritis samples. Six LR pairs were identified in the bidirectional communication between these three BM-MSCs subsets and other BM-MSCs subsets. Among them, MIF-CD74 and ITGB2-ICAM2 were significantly correlated with the immune score. CD74 was identified as the target, and a total of 48 drugs targeting CD47 protein were identified. Among them, DB01940 had the lowest free energy binding score with CD74 protein and the binding state was very stable. This study provided a new network-based framework for drug reuse and identified initial insights into therapeutic agents targeting CD74 in osteoporosis, which may be meaningful for promoting the development of osteoporosis treatment.

Integrated Metabolome and Transcriptome Analysis Reveals the Key Bitter Substances of Pleioblastus amarus Shoots at Different Developmental Stages

Pleioblastus amarus (bitter bamboo) shoots are a traditional forest vegetable in China, renowned for its nutritional composition and associated health benefits. This study aimed to investigate the dynamic changes in nutritional quality and bitterness-related metabolites, as well as corresponding gene expression in P. amarus shoots at three distinct growth stages, specifically at heights of 10 cm (BT1), 20 cm (BT2), and 40 cm (BT3). The results showed that the content of soluble sugars decreased while the content of total phenols and flavonoids, which contribute to bitterness, increased during the growth of P. amarus shoots. In addition, comparative analysis of BT2 vs. BT1, BT3 vs. BT1, and BT3 vs. BT2 revealed 14, 43, and 36, respectively, differentially accumulated metabolites (DAMs). Notably, BT2 exhibited upregulation of flavonoids, such as apigenin, while bitter amino acids, including phenylalanine and arginine, showed a marked increase in BT3. Moreover, differential gene expression analysis revealed 3161, 13268, and 3742 differentially expressed genes (DEGs) in the BT2 vs. BT1, BT3 vs. BT1, and BT3 vs. BT2 comparisons, respectively. An integrated analysis of metabolomic and transcriptomic data indicated that the metabolites and genes associated with the biosynthesis pathways of phenylalanine, tyrosine, tryptophan, arginine, and the branched-chain amino acids valine, leucine, and isoleucine were significantly enriched during the growth of P. amarus shoots. The study indicates that early-stage (≤20 cm) P. amarus shoots are mainly bitter due to flavonoids like apigenin, while the bitterness in later-stage (around 40 cm) shoots is largely due to bitter amino acids such as phenylalanine, arginine, valine, and isoleucine. This research highlights key compounds and genes related to bitterness, providing a basis for future research on enhancing the nutritional value and flavor of P. amarus shoots.

Differences in Drug Shortages in the US and Canada

Drug shortages are a persistent public health issue that increased during the COVID-19 pandemic. Both the US and Canada follow similar regulatory standards and require reporting of drug-related supply chain issues that may result in shortages. However, it is unknown what proportion are associated with meaningful shortages (defined by a significant decrease in drug supply) and whether differences exist between Canada and the US. To compare how frequently reports of drug-related supply chain issues in the US vs Canada were associated with drug shortages. Longitudinal cross-sectional study conducted from January 2023 to March 2024 using drug-related reports of supply chain issues from 2017 to 2021 that were less than 180 days apart in Canada and the US. Shortages were assessed using data from the IQVIA Multinational Integrated Data Analysis database, comprising 89% of US and 100% of Canadian drug purchases. Country (Canada vs US), timing of report issuance (before vs after the COVID-19 pandemic), and characteristics of the supply chain prior to the reports of drug-related supply chain issues (including World Health Organization essential medicine status, Health Canada tier 3 medicine [moderate risk classification], whether there was sole-source manufacturing of the drug, the formulation, the price per unit, ≥20 years since drug approval, and the number of therapeutic alternatives). A drug shortage (a decrease of ≥33% in monthly purchased standardized drug units) within 12 months, relative to the average units purchased during the 6 months prior to the report of supply chain issues to a US or Canadian reporting system. Among the 104 drug-related reports of supply chain issues in both countries, 49.0% (95% CI, 39.3%-59.7%) were associated with drug shortages in the US vs 34.0% (95% CI, 25.0%-45.0%) in Canada (adjusted hazard ratio [HR], 0.53 [95% CI, 0.36-0.79]). The lower risk of drug shortages in Canada vs the US was consistent before the COVID-19 pandemic (adjusted HR, 0.47 [95% CI, 0.30-0.75]) and after the pandemic (adjusted HR, 0.31 [95% CI, 0.15-0.66]). After combining reports of supply chain issues in both countries, the shortage risk was double for sole-sourced drugs (adjusted HR, 2.58 [95% CI, 1.57-4.24]) and nearly half for Canadian tier 3 medicines (moderate risk) (adjusted HR, 0.56 [95% CI, 0.32-0.98]). Drug-related reports of supply chain issues were 40% less likely to result in meaningful drug shortages in Canada compared with the US. These findings highlight the need for international cooperation between countries to curb the effects of drug shortages and improve resiliency of the supply chain for drugs.

Prediction of grain shoal reservoirs via seismic forward modeling and waveform classification: Application to the northern slope of the central Sichuan Uplift, Sichuan Basin, SW China

Grain shoals are one of the principal reservoir facies of carbonate rocks in oil and gas exploration domains. With the discovery of oil and gas in the grain shoal reservoirs of the Longwangmiao Formation in the central Sichuan paleo-uplift, the characteristics and distribution of grain shoal reservoirs are still unclear in the northern slope of the central Sichuan paleo-uplift. Through integrated analysis of wireline logs and high-quality 3D seismic data, combined with seismic forward modeling and waveform classification, reservoir seismic facies, reservoir classification, and the reservoir distribution of different grades are investigated. The results show that (1) under the thin thickness (i.e., the thickness is around 80 m) of the Longwangmiao Formation (i) when reservoirs are not developed, the seismic waveform is manifested as a single peak, and the top interface of the Longwangmiao Formation corresponds to 1/8 [Formula: see text] above the single peak. (ii) When a single set of reservoirs is merely developed in the second member of the Longwangmiao Formation (SMLF), the waveform displays a single peak, and the top peak moves downward. In addition, when multiple sets of reservoirs are developed in the SMLF, the seismic reflections of multiple reservoirs are consistent with those of a single reservoir with the same cumulative thickness. (iii) When the reservoirs are developed in the first member of the Longwangmiao Formation (FMLF) and SMLF, the single peak moves downward, and either the reservoir thickness of the SMLF is greater than that of the FMLF or the reservoir velocity of the SMLF is lower than that of the FMLF. (2) Under the thick thickness (i.e., the thickness is approximately 110 m) of the Longwangmiao Formation, (i) when reservoirs are not developed, the waveform of the Longwangmiao Formation exhibits a double peak, and the top interface of the Longwangmiao Formation corresponds to the extreme value of the upper peak. (ii) When only a single set of reservoirs or multiple sets of reservoirs are developed in the SMLF, the Longwangmiao Formation exhibits a single peak, and the top interface of the Longwangmiao Formation corresponds to the trough. (iii) With the reservoir development in the first and second members of the Longwangmiao Formation, the waveform of the Longwangmiao Formation shows an asymmetric low-frequency peak, and either the reservoir thickness of the SMLF is greater than that of the FMLF or the reservoir velocity of the SMLF is lower than that of the FMLF. (3) Using the waveform classification method, seismic waveforms corresponding to the Longwangmiao Formation could be summarized into four types (i.e., two types of waveforms associated with reservoir development and two types of waveforms associated with reservoir un-development). Two reservoir waveforms are distributed as two strips oriented approximately east–west and spread in the central study area, according to the planar study results that combine seismic attributes and waveform classification. The reservoir stripes near well PS1 represent the better-developed reservoir zones in the study area.

Unveiling the novel regulatory roles of RpoD-family sigma factors in Salmonella Typhimurium heat shock response through systems biology approaches.

Three RpoD-family sigma factors, RpoD, RpoS, and RpoH, play critical roles in transcriptional regulation in Salmonella enterica serovar Typhimurium under heat shock conditions. However, the genome-wide regulatory mechanisms of these sigma factors in response to heat stress have remained elusive. In this study, we comprehensively identified 2,319, 2,226, and 213 genome-wide binding sites for RpoD, RpoS, and RpoH, respectively, under sublethal heat shock conditions (42°C). Machine learning-based transcriptome analysis was employed to infer the relative activity of iModulons, providing valuable insights into the transcriptional impact of heat shock. Integrative data analysis enabled the reconstruction of the transcriptional regulatory network of sigma factors, revealing how they modulate gene expression to adapt to heat stress, including responses to anaerobic and oxidative stresses. Notably, we observed a significant expansion of the RpoS sigmulon from 97 to 301 genes in response to heat shock, underscoring the crucial role of RpoS in regulating various metabolic processes. Moreover, we uncovered a competition mechanism between RpoD and RpoS within RpoS sigmulons, where RpoS significantly increases its binding within promoter regions shared with RpoD under heat shock conditions. These findings illuminate how three RpoD-family sigma factors coordinate multiple cellular processes to orchestrate the overall response of S. Typhimurium to heat stress.

Systematic identification of therapeutic targets for coronary artery calcification: an integrated transcriptomic and proteomic Mendelian randomization.

Coronary artery calcification (CAC) is associated with an increased risk of mortality and cardiovascular events. However, none therapeutic drugs have been proven effective for CAC treatment. The objective of this study was to identify potential therapeutic targets for CAC through the utilization of Mendelian randomization (MR) and colocalization analysis. The expression quantitative trait loci (eQTLs) of 16,943 genes from the eQTLGen consortium and protein quantitative trait loci (pQTLs) of 4,412 proteins from a plasma proteome were utilized as genetic instruments. Genetic associations with CAC were derived from a GWAS meta-analysis of 26,909 individuals. The MR and colocalization analysis were utilized to identify potential target genes. A total of 671 genes were found to be significantly associated with the risk of CAC based on transcriptomic MR analysis at a false discovery rate <0.05, while proteomic MR analysis identified 15 genes with significant associations with CAC at the same threshold. With robust evidence from colocalization analysis, we observed positive associations between CWF19L2, JARID2, and MANBA and the risk of CAC, while KLB exhibited an inverse association. In summary, our study identified 23 potential therapeutic targets for CAC. Further downstream analysis revealed IGFBP3, ABCC6, ULK3, DOT1L, KLB and AMH as promising candidates for repurposing in the treatment of CAC. The integrated MR analysis of transcriptomic and proteomic data identified multiple potential drug targets for the treatment of CAC. ULK3, DOT1L, and AMH were recognized as novel targets for drug repurposing for CAC and deserve further investigation.

Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is associated with high mortality rate. This study investigated the status of lipid metabolism-related genes in HCC. Bulk transcriptomic and single-cell sequencing data for HCC were retrieved from public databases. The single-cell sequencing data was subjected to dimensionality reduction, which facilitated the annotation of distinct cell subpopulations and marker gene expression analysis within each subpopulation. Genes associated with lipid metabolism in liver cells were identified, and a machine-learning model was developed using the bulk transcriptomic data randomly partitioned into training and validation sets. The efficacy of the model was validated using these two sets. A multifactorial Cox analysis on the model genes combined with clinical features, led to the identification of age, HMGCS2, HNRNPU, and RAN as independent prognostic factors, which were included in the nomogram model construction and validation. A weighted gene co-expression analysis of all genes of the bulk transcriptome samples revealed the correlation between gene modules and risk score. Genes with cor > 0.4 in the highest-expressing module were selected for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis. Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. For the genes in the nomogram model, the expression in clinical pathological factors was also analyzed. The drug sensitivity analysis offered a reference for the selection of targeting drugs. This investigation provides novel insights and a theoretical basis for the prognosis, treatment, and pharmaceutical advancements for patients diagnosed with HCC.

Using data fusion with multiple imputation to correct for misclassification in self-reported exposure: a case-control study of cannabis use and homicide victimization

BackgroundCannabis use has been causally linked to violent behaviors in experimental and case studies, but its association with homicide victimization has not been rigorously assessed through epidemiologic research.MethodsWe performed a case-control analysis using two national data systems. Cases were homicide victims from the National Violent Death Reporting System (NVDRS), and controls were participants from the National Survey on Drug Use and Health (NSDUH). While the NVDRS contained toxicological testing data on cannabis use, the NSDUH only collected self-reported data, and thus the potential misclassification in the self-reported data needed to be corrected. We took a data fusion approach by concatenating the NSDUH with a third data system, the National Roadside Survey of Alcohol and Drug Use by Drivers (NRS), which collected toxicological testing and self-reported data on cannabis use for drivers. The data fusion approach provided multiple imputations (MIs) of toxicological testing results on cannabis use for the participants in the NSDUH, which were then used in the case-control analysis. Bootstrap was used to obtain valid statistical inference.ResultsThe analyses revealed that cannabis use was associated with 3.55-fold (95% CI: 2.75–4.35) increased odds of homicide victimization. Alcohol use, being Black, male, aged 21–34 years, and having less than a high school education were also significantly associated with increased odds of homicide victimization.ConclusionsCannabis use is a major risk factor for homicide victimization. The data fusion with MI method is useful in integrative data analysis for harmonizing measures between different data sources.

Data Integration and Predictive Analysis for Healthcare: Survey

In modern healthcare, handling of medical data is often divided, with sensitive information stored in various formats in documents like prescriptions, lab reports and patient summaries. The lack of standardization and management of such data can lead to errors and inefficiency in patient care. Various techniques like HCR, OCR, machine learning algorithms are for the purpose of data integration and predictive analysis. The aim of this paper is to conduct the survey of various techniques used in healthcare systems and domain for data handling and performing operations like predictive analysis on that data. We have reviewed and shortlisted 7 papers and the methods used in those studies. This survey is beneficial to those who wish to manage and store data of healthcare and use the same for their respective purposes.

Molecular phylogeny reveals a new genus and species of freshwater mussel (Bivalvia, Unionidae, Gonideinae) from Jiangxi, China

Freshwater mussels of the tribe Gonideini (Bivalvia: Unionidae: Gonideinae) constitute one of the most taxonomically diverse groups and serve as keystone species in riverine and lacustrine ecosystems across East Asia. A new genus and species of Gonideini (Bivalvia: Unionidae) is described from Jiangxi, China, as Pseudopostolata angulagen. et sp. nov. based on an integrative analysis of shell morphology and molecular data. The validity of the new genus and species is supported by distinct conchological features: a short, rounded anterior; a long, wide posterior; a slightly downward-curved dorsal margin; and a distinctly obtuse angle at the center of the posterior margin. The multi-locus (COI + 16S rRNA + 28S rRNA) phylogeny showed that the species formed a monophyletic group in the tribe Gonideini of the subfamily Gonideinae. Pseudopostolata angulagen. et sp. nov. is identified as the sister group to a clade comprising the genera Postolata, Cosmopseudodon, Obovalis, Ptychorhynchus, Parvasolenaia, and Koreosolenaia. We emphasize the significant morphological convergence in freshwater mussels, particularly within Gonideini, highlighting the necessity of an integrated taxonomic approach for accurate generic classification of this group.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.