Cancer prevention is one of the most pressing challenges that public health needs to face. In this regard, data-driven research is central to assist medical solutions targeting cancer. To fully harness the power of data-driven research, it is imperative to have well-organized machine-readable facts into a knowledge base (KB). Motivated by this urgent need, we introduce the Collaborative Oriented Relation Extraction (CORE) system for building KBs with limited manual annotations. CORE is based on the combination of distant supervision and active learning paradigms and offers a seamless, transparent, modular architecture equipped for large-scale processing. We focus on precision medicine and build the largest KB on ‘fine-grained’ gene expression–cancer associations—a key to complement and validate experimental data for cancer research. We show the robustness of CORE and discuss the usefulness of the provided KB.Database URLhttps://zenodo.org/record/7577127 In 2020, there were 19.2 million cancer cases worldwide. The World Health Organization estimates a 32% overall increase by 2040.Although experimental data are invaluable for cancer research, they require further investigation, processing and validation by experts to be used to populate a KB. Luckily, in most cases, the analysis and interpretation of experimental data are described in peer-reviewed publications, making the scientific literature a critical source to complement and validate the data. Given the high economical and time costs to manually extract knowledge ( e.g. scientific facts) from the domain-specific literature , in receDistant supervision , 13 and We note that there are a handful of knowledge resources containing data about gene expression–cancer associations  and mostMore general and large-scale resources on gene–disease associations—i.e. DisGeNET  and the Hence, there is a need for KBC systems that can scale to large text corpora and stay up to date while generating fine-grained information about gene expression–cancer associations. We present the Collaborative Oriented Relation Extraction (CORE) system, a KBC system based on the combination of automated ML-based methods and domain experts’ feedback. CORE features a seamless, transparent, modular architecture, where the different components can be easily plugged-in. It also employs active learning to bootstrap the KB to be produced and exploits the fine-grained aspects involved in gene expression–cancer associations to perform iterative tests that measure the reliability of the data to be stored in the KB. Finally, it returns small, selected samples to domain experts for annotation. The high-quality data generated by this process are then used as reinforcement to retrain the ML models from scratch. Active learning makes CORE suited to iterative KB versioning. Therefore, with the data annotated by domain experts, retrained ML models are deployed to build subsequent versions of the KB.http://w3id.org/corekb/sparql. The KB can also be accessed via COREKB  presents the required background; Section (The CORE system) outlines the CORE system; Sections (NERD)–(Active learning) describe the components of CORE; Section (Implementation and experiments) presents the system settings and the experiments; Section (KB exploration) performs some exploratory queries and analyses on the generated KB; Section (Search platform) showcases the search platform and Section (Conclusions and future work) draws final remarks.Generally, precision medicine is about tailoring of medical treatment to the characteristics of an individual patient and moves beyond the traditional approach of stratifying patients into treatment groups based on phenotypic markers . Among tMost knowledge resources about ‘gene expression–cancer associations’ consist of experimental data obtained through microarray and next-generation sequencing technologies . A relevBeyond experimental data, BioXpress  and OncoRegarding literature-based resources, CoMAGC  is a corKBs have gained a great deal of attention recently as a key component for supporting search and recommendations at the Web scale. From seminal works such as DBPedia , FreebasKBs are also increasingly used by large companies and organizations as a means for organizing and understanding their data . Industrv1 the source vertex and v2 the target vertex. In our context, the nodes of G are entities and the edges are the relationships between them.Definition 1.Ai has a name and a domain jthaspect value of Ai. Ai .We call aspect an attribute of a relationship between a pair of entities. An aspect Example 1.e) between gene (v1) and cancer (v2): the change of gene expression (CGE), the change of cancer status (CCS) and the gene–cancer interaction (GCI). Following Definition Dom(GCI) = {causality, correlation, notinf}.Let us consider gene–cancer associations. There are three aspects describing a possible relationship . The signature function defines a set of mapping rules depending on the domain of interest. We use the signature function to map multi-aspect gene expression–cancer relationships to gene prospective roles in cancer  are The goal of the CORE system is to harvest facts from text corpora to populate KBs. We model a KB as a directed graph To obtain facts, CORE collects the scientific literature from different sources, identifies sentences containing pairs of entities relevant to the considered task and extracts aspects from them. Depending on the combination of extracted aspect values, a sentence expresses a specific signature type. Note that, for a given pair of entities, different sentences can express various signature types, as we show in the next example.Example 2.See these two sentences from the biomedical literature:Colorectal cancer (CRC) growth and progression is frequently driven by RAS pathway activation through upstream growth factor receptor activation or through mutational activation of KRAS or BRAF.BRAF, have been found in various types of human cancers such as malignant melanoma, and CRC.Somatic mutations of the BRAF gene, causing constitutive activation of up, CCS = progression and GCI = causality, leading to the signature type s) = oncogene. On the other hand, the aspect values of sentence B are CGE = up, CCS = progression and GCI = correlation, leading to the signature type s) = biomarker.In both sentences, the following entities are extracted mentclass2pt{minimFrom Example Definition 5.aj carries a likelihood Ai.Given an aspect Definition 6.n-tuple of aspect–probability sets.Let Thus, for each pair of target entities, CORE computes the probabilities for all the aspect values and combines them into tuples of aspect–probability sets—i.e. a probability distribution over multi-aspect relationships. Sentences serve as supporting or contradicting evidence that strengthens or weakens the likelihood of a fact.k automatically annotated sentences associated with them are reannotated by experts. This process triggers an active learning workflow that reinforces the RE methods.In the deployment workflow, the automatic annotations expressing multi-aspect relationships between entities are provided by the RE methods (Module 4) previously trained in the bootstrapping phase. Then, in the last module (Module 5), relation-annotated sentences are grouped by entity pairs and used to generate facts to further populate the KB. Module 5 is composed of (i) a knowledge enrichment component computing the probabilities for all the aspect values and combining them into tuples of aspect–probability sets and (ii) a reliability testing component that uses these probabilities to perform multiple tests to tag the facts as ‘reliable’ or ‘unreliable’. Only the facts tagged as ‘reliable’ are used to populate the KB. When the deployment workflow is complete, ‘unreliable’ facts are ranked by ascending reliability score and the top-jth iteration of the bootstrap and deployment workflows. Once the jth version of the KB has been deployed, the active learning workflow starts by generating the batch of unreliable sentences for bootstrapping the The active learning workflow makes CORE suitable to iterative KB versioning. We define a KB version as the graph CORE recognizes gene and cancer entities from text and links them to relevant and authoritative KBs. In our setting, gene entities are linked to the National Center for Biotechnology Information (NCBI) Gene database , whereasAs NERD component, CORE integrates the PubTator system . Given bDom(CCS) = {progression, regression, notinf}; Dom(GCI) = {causality, correlation, notinf}.CORE involves experts to manually annotate some sentences with multi-aspect relationships about gene expression–cancer associations. For annotation, CORE adopts a common and shared schema in the biomedical domain . GCC indicates the coarse-grained association between gene and cancer and serves as a filter that helps differentiating between gene–cancer associations related to changes in the gene expression levels and those encompassing other types of gene–cancer relationships. To this end, GCC has the following domain: Based on the annotation schema presented earlier, domain experts perform multi-aspect manual annotations between gene–cancer pairs and return relation-annotated sentences (Module 2). Depending on the considered workflow, the sentences to be annotated come from different modules at different stages. At the beginning of bootstrapping, (entity-annotated) sentences come from Module 1 as the output of the NERD component. After active learning, (unreliable) sentences come from Module 5 due to the reliability testing. In both cases, any errors associated with the NERD component are corrected too. For training RE methods (Module 3), the CORE system employs the complete set of relation-annotated sentences, whereas to populate the KB (Module 5), CORE keeps only the sentences with s driver .CORE’s RE methods are trained to automatically annotate multi-aspect relationships on sentences. Once trained, the RE methods are applied on new, unseen sentences to generate knowledge and thus update the KB.For each aspect to be annotated, a different RE method is trained (Module 3) and deployed (Module 4). Together, different aspect-based annotations compose the multi-aspect relationship. Although simple, this approach reflects the transparent and modular architecture of the CORE system, where different components can be easily plugged in and plugged out since every RE method can be retrained—or changed—without affecting others.The RE methods serve two purposes: classify sentence utility and extract gene expression–cancer aspects. Every RE method presents the same architecture but addresses a different aspect. As the underlying ML model, all RE methods adopt SciBERT , a pretrDefinition 7.Ai be an aspect, aj one of its values and T a set of sentences. We define t returns how close the aspect value aj is to the truth.Let Every RE method instantiates the score function tailored for its aspect extraction task. A RE method returns a specific sentence-aspect score always in the range of Remark 1.t and an aspect Ai given a sentence t are a probability distribution over the aspect values aj.Given a sentence Thus, given an entity-annotated sentence (Module 1), CORE first masks gene and cancer entities with special tokens to avoid bias and then applies RE methods to extract CGE, CCS, GCI and GCC aspects. GCC extraction serves to assess sentence utility, while CGE, CCS and GCI extraction to compose multi-aspect relationships. For each aspect, the CORE system keeps the value associated with the highest score. For manual annotation, the scores are set to 1 if the aspect value is present and 0 otherwise.Afterward, relation-annotated sentences where the extracted GCC value is The relation-annotated sentences obtained from manual annotation (Module 2) and RE deployment (Module 4) pass through a knowledge enrichment component, which groups annotated sentences by gene–cancer pairs and generates facts. However, for a given gene–cancer pair, different sentences can have different multi-aspect annotations. This situation occurs because, in the literature, different studies and viewpoints can lead to different conclusions. To address this intrinsic uncertainty, the CORE system assigns to each aspect a likelihood to be true.Definition 8.v1 and v2 and Let where aj over the others, the more its likelihood increases. Vice versa, if RE methods have a larger degree of uncertainty across the different aspect values, then the likelihood for aj also decreases accordingly.By modeling aspect value likelihoods this way, the CORE system takes the beliefs of RE methods into account. The more the RE methods are confident about an aspect value Remark 2.Ai, then Given a pair of entities Example 3.Let us consider a gene–disease pair up, 0.7), CCS, GCICGE , CCS , GCI ,CGE , CCS , GCI,CGE , CCS , GCI .CGE  = (0.7 + 0.0 + 1.0)/(0.7 + 0.8 + 1.0) = 0.68 and Pr(down) =(0.0 + 0.8 + 0.0)/(0.7 + 0.8 + 1.0) = 0.32, leading to the aspect–probability set APCGE = {, }. Similarly, CCS and GCI lead to aspect–probability sets APCCS = {, , } and APGCI = {, , }. Thus, given the fact First, sentence For each fact, CORE combines CGE, CCS and GCI aspects into the tuple of aspect–probability sets, which represents a probability distribution over multi-aspect relationships and performs reliability tests to decide if the fact is reliable enough to populate the KB.down, regression, causality) multi-aspect relationships  = 0.7, Pr(tsg) = 0.2, Pr(biomarker) = 0.1,Pr(oncogene) = 0.5, Pr(tsg) = 0.4, Pr(biomarker) = 0.1.Pr and Pr(GCI = notinf) are lower than α for both facts. On the other hand, only f1 passes the consistency check, since none of the signature type likelihoods of f2 are large enough to overcome the others by a margin of β. In this regard, for f1, we have f2, we have f1 is tagged as ‘reliable’ and f2 as ‘unreliable’.Then, let us set the sufficiency threshold The facts that pass both sufficiency and consistency checks are tagged as ‘reliable’ and used to populate the KB. Prior to population, the edges of ‘reliable’ facts are labeled through the tagging function k automatically annotated sentences to domain experts for annotation.The facts deemed as ‘unreliable’ by the reliability testing component (Module 5) are taken over by the active learning process, which ranks them by ascending reliability score and returns the top-Definition 10.e and S the set of signature types. Then, by taking a specific value aij for each aspect Ai of the subset, the reliability score is computed as S, computed as Let k automatically annotated sentences associated with these facts are returned to domain experts for manual annotation (Module 2).In this work, we compute the reliability score by considering the subset of CCS and GCI aspects and by taking their not-informative values sampling . In otheWe use CORE to build a KB for gene expression–cancer associations. To this end, we conducted comprehensive experiments to quantify the extracted knowledge and evaluate the RE methods used to build the KB. In addition, we performed a KB reconstruction task against the state-of-the-art showing CORE effectiveness.Data processing. We use different resources to build the KB, which increase with each subsequent iteration of the KB construction process. Then, in the second iteration (KB1), we introduce DisGeNET data, on which the CORE system deploys the RE methods. DisGeNET collects data on different ‘coarse-grained’ gene-disease associations from several resources and covers most human diseases. Regarding gene expression–cancer associations, DisGeNET contains automatically extracted data that have been identified from the literature using text-mining techniques , 66, 67.k sentences for manual annotation. This new set of manually annotated sentences—together with a second batch from DisGeNET—are added to previously used data and employed to build KB2. In the last iteration (KB3), we collect from PubMed the articles citing those stored within KB2. Then, the CORE system relies on the NERD component to extract gene and cancer entities from titles and abstract sentences and deploys RE methods on them. Finally, PubMed and top-k ‘unreliable’ sentences from KB2 are integrated into KB3 construction.After the construction of KB1, the active learning process ranks ‘unreliable’ facts and returns the top-Manual annotation. The manual annotation process has been carried out by a clinical expert. The annotator has been given the target sentence to annotate/validate, together with the corresponding PubMed article from which it has been extracted—from either the title or the abstract.System parameters. The parameters required by CORE are the sufficiency and consistency thresholds α and β and the number k of sentences to be returned for manual annotation during active learning. Sufficiency and consistency thresholds regulate the degree of reliability of the facts in the KB. A low sufficiency combined with a high consistency threshold leads to fewer facts but with a high level of reliability. Empirically, we set α = 0.7 and β = 0.4. We set k = 250, meaning that 250 sentences are reannotated after each iteration. Note that system parameters can be adjusted as the KB size increases.KB statistics. From the statistics reported in Second, the large number of different genes and cancers in KB3 highlights the scalability of the approach. In this regard, KB3 contains 21 005 genes, which cover 70% of the 30 000 estimated genes in the human genome.Finally, KB3 represents one of the largest literature-derived KBs with fine-grained facts about gene expression–cancer associations. Compared to KB3, BioXpress and OncoMX—both relying on DEXTER text-mined results—contain less literature-derived data. Specifically, BioXpress integrates DEXTER gene expression–cancer associations for 2024 genes in lung cancer, 115 glycosyltransferases in 62 cancers and 826 microRNAs in 171 cancers , and we evaluate the CORE system on them. Note that such sentences are not part of those used to train the CORE RE methods. Given that BioXpress only reports CGE values between cancer and normal samples, we restrict our evaluation to CGE extraction. As a further experiment, we also apply DEXTER to DS2 to evaluate its ability to generalize to heterogeneous sentences, whose syntactic structure can differ from its predefined patterns.Results.We perform some exploratory queries to analyse the contents of the largest KB produced by CORE, that is KB3. The SPARQL queries used to explore KB3 can be found in The KB generated by CORE can also be accessed via COREKB , an intuCOREKB’s architecture consists of multiple components synergically cooperating to facilitate the search and retrieval of scientific facts—i.e. gene expression–cancer associations supported by the scientific literature. The architecture includes a web-based front–end interface built with React.js and a back-end for the business logic, Representational State Transfer Application Programming Interfaces and services built with the Python web framework Django. The system relies on a PostgreSQL database coupled with a Virtuoso Resource Description Framework triple store to memorize the KB. Moreover, Redis is exploited as an efficient in-memory data store and access broker. A search and retrieval component implemented in Python performs NERD on the user-provided queries to identify entity mentions and, in turn, perform a structured search on the database. To this aim, a Redis in-memory dictionary of entities is exploited for fast entity identification.Specifically, when a user query is received, the system assigns a score to each entity based either on an exact match (if it occurs) or on the number of matching terms in the case of a partial match. The score is normalized based on the entity’s length to avoid favoring longer entities at the expense of shorter ones. Then, the retrieved facts are ordered according to their scientific evidence support. In the case of multiple recognized entities, the system promotes gene–cancer pairs with the most matching associations.The interface reports the search results by organizing them into cards; it provides information such as gene class, symbol, cancer label, supporting and conflicting sentences, associated publications, gene class distribution and bibliometrics. The fact claim is emphasized using boldface and a colored circle, indicating the informativeness and reliability of the fact—i.e. green, red and gray colors, respectively, for reliable, unreliable and non-informative facts. Moreover, the interface includes links and references to related entries in external platforms like NCBI and Linked Life Data.For each gene or cancer entity, there is a dedicated landing page that displays comprehensive information. The landing page consists of two major cards. The first card presents detailed information about the entity, for instance, in the case of a gene, it shows its symbol, full name, type, synonyms, designations, last modified date, summary and gene class distribution for different cancer diseases. Long textual information can be expanded or collapsed on click for space-saving purposes. Instead, a second card shows the sentences involving the entity of interest, presented in a tabular form with filtering and sorting features. Users can resize columns, hover over sentences for getting information via tooltips or click on sentences for a separate pop-up view.http://w3id.org/corekb/sparql) or through the COREKB search platform (https://gda.dei.unipd.it).In this work, we presented CORE, a KBC system based on the combination of automated ML-based methods and domain experts. CORE presents a seamless, transparent and modular architecture that can be easily modified and where different components can be replaced without affecting the others. Among its main features, the reliability tests and the active learning process make the system suited to iterative KB versioning. That is, CORE performs iterative tests that measure the reliability of the extracted data and return small, selected samples to domain experts for annotation. The high-quality data generated through active learning are then used to reinforce CORE subsequent versions. We used CORE to build one of the largest literature-derived KBs containing fine-grained facts about gene expression–cancer associations. To show the robustness of the approach, we conducted extensive experiments that highlighted the ability of CORE to scale to large collections of heterogeneous data with limited human annotations. The KB generated by CORE can be accessed via a SPARQL endpoint  as input sources. Robust validation mechanisms and collaboration with experts will be crucial to identify and ingesting reliable content generated by LLMs.


This study highlights the significance of quality assurance in biobanking facilities, specifically in the context of high-throughput research and novel molecular techniques. We established specific quality management workflows utilizing biospecimens collected from oncological patients in Polish clinics. The process for serum/plasma samples involved essential steps, such as monitoring hemolysis, controlling RNA extraction, assessing cDNA library quality and quantity, and evaluating NGS raw data before bioinformatics analysis. Pathologic evaluation of fresh frozen tissue samples was conducted to verify histological images and determine tumor content. Consequently, we developed workflows for preanalytical quality control of serum/plasma and fresh-frozen tissue samples, which can be tailored to individual biobank capabilities. These integrated molecular-based quality control methods ensure that biobanking procedures fulfill the requirements of high-throughput assays, offering access to biospecimens of confirmed omics-grade quality. We strongly recommend implementing these workflows for specimen qualification before conducting multi-omics assays, as they exhibit exceptional sensitivity and specificity.Biobanks are vital for high-throughput translational research, but the rapid development of novel molecular techniques, especially in omics assays, poses challenges to traditional practices and recommendations. In our study, we used biospecimens from oncological patients in Polish clinics and collaborated with the Indivumed Group. For serum/plasma samples, we monitored hemolysis, controlled RNA extraction, assessed cDNA library quality and quantity, and verified NGS raw data. Tissue samples underwent pathologic evaluation to confirm histology and determine tumor content. Molecular quality control measures included evaluating the RNA integrity number, assessing cDNA library quality and quantity, and analyzing NGS raw data. Our study yielded the creation of distinct workflows for conducting preanalytical quality control of serum/plasma and fresh-frozen tissue samples. These workflows offer customization options to suit the capabilities of different biobanking entities. In order to ensure the appropriateness of biospecimens for advanced research applications, we introduced molecular-based quality control methods that align with the demands of high-throughput assays. The novelty of proposed workflows, rooted in innovative molecular techniques, lies in the integration of these QC methods into a comprehensive schema specifically designed for high-throughput research applications. The application of next-generation sequencing (NGS) has revolutionized our understanding of cancer development and progression. Identifying cancer-related mutations and resistance biomarkers improved our knowledge of the molecular basis of different cancer types . AlthougMUB Biobank’s organization was possible by founding the MOBIT project from National Centre for Research and Development in the framework of the STRATEGMED II Program. The MOBIT project aimed to combine biobanking with high-throughput methods to create a reference model for early lung cancer diagnosis and personalized treatment . Within To maintain the comparability and reproducibility of research conducted on samples stored in a biorepository, standard operating procedures (SOPs) were prepared to regulate every step of biobanking. Moreover, QC forms are used for recording sample-connected processes. Both SOPs and QC forms are subjected to periodic review to maintain compliance with the latest scientific reports.Documenting the exact time of resection start, vessel ligation, resection end, and tissue sample preservation is essential for monitoring warm and cold ischemia. The ideal sample represents in vivo conditions, so efforts are made to shorten the period between tissue resection and preservation. Recently published reports describe the impact of postoperative ischemia duration on tissue sample biology, e.g., gene expression and RNA stability ,13. In MTo compile large amounts of data in a user-friendly environment, it is necessary to digitalize the paper reports in a database. During the MOBIT Project, the SmartBioBase—a platform for collecting, integrating, and analyzing clinical and omics data—was created . This enApart from detailed sample collection, processing, and storage documentation, measuring and analyzing key process indicators plays an essential role in quality improvement. Accordingly, we developed molecular-based models for QC of liquid and tissue biospecimens to ensure their utility for high-throughput research. As shown in The samples were visually assessed by expert technicians using standard hemolysis assessment protocol as presented in Analytical methods for assessing blood hemolysis are necessary to provide high-quality serum/plasma samples for molecular assays because even small amounts of cellular contamination in these biospecimens can affect the miRNA expression profile. Moreover, increased concentration of free hemoglobin and other molecules  released from red blood cells may affect proteomics and metabolomics profiles. While these methods primarily address the specific issue of hemolysis, they are integral components of a broader QC framework for omics approaches to ensure the reliability and accuracy of omics data, enhance data reproducibility, and facilitate valid comparisons across different studies.The first step of the QC process developed for fresh-frozen tissue includes the histological examination of biospecimens. All samples were subjected to frozen section histology for precise tissue sectioning. Cut specimens were mounted on glass slides and stained with hematoxylin and eosin to allow microscopic examination of sample morphology, including the pathological interpretation of the image and calculation of tumor content. For the inclusion criteria for further molecular assays, ≥50% was determined as sufficient tumor content in the tissue samples collected from the tumor center and the tumor periphery . AdequatThe assessment of RNA integrity is a critical step in obtaining meaningful gene expression data. The RNA integrity number (RIN) scale ranges from 1 (degraded) to 10 (intact) and is a widely used metric to assess the quality and integrity of RNA samples for omics studies, particularly transcriptomics. While there is no universally agreed-upon threshold for the RIN value, a common recommendation is to aim for a RIN value of 7 or higher . During ® Small RNA-Seq Kit v3 . The complementary DNA (cDNA) libraries were assessed qualitatively using a high-sensitivity DNA Chip assay on Bioanalyzer 2100. Size selection of the libraries to obtain the appropriate cDNA products (~150 base pairs) for NGS was performed on apparatus Blue Pippin . Then, quantities of cDNA libraries were assessed using a KAPA Library Quantification Kit for Illumina Platforms . The NGS of the prepared samples was carried out on HiSeq 4000 Illumina. QC of sequencing data was performed using the mixture of spike-ins, added to the samples after total RNA isolation. The set of ExiSEQ NGS spike-ins allowed for thorough QC of the NGS data by assessing the reproducibility and linearity of the reads mapped to exogenous sequences.QC of RNA extracted from fresh-frozen tissue samples from tumor periphery, tumor center, and normal adjacent tissue included the isolation of total RNA from fresh-frozen tumor samples using mirVana miRNA Isolation Kit . To monitor the comparability and reproducibility of the whole process from RNA isolation to sequencing, a mix of 5′ phosphorylated microRNAs of different concentrations  was added to each RNA after the extraction of nucleic acids process. RNA quantity and quality were assessed using a UV/VIS spectrophotometer NanoDrop 2000c . The level of integrity required for the NGS analysis (RIN ≥ 7) was determined for the extracted RNA using Agilent RNA 6000 Nano Kit on Bioanalyzer 2100. Libraries for NGS were prepared using NEXTflexContinuous evaluation and improvement of QC protocols are necessary to adapt to evolving technologies and best practices in NGS. It ensures that the data used for downstream analyses is of sufficient quality to produce more accurate and meaningful interpretations, reducing the risk of false discoveries or incorrect conclusions. The QC process for NGS starts with the storage of raw FASTQ data in a dedicated database within the Bioinformatic Cluster managed by MUB. Data quality checks are performed on the reads to assess their overall quality, including metrics such as sequencing depth, read length, and base quality scores. The next step involves mapping the reads to a reference genome to determine their alignment and identify any potential variations or mutations. This is followed by variant calling where specific genetic variations, such as single nucleotide polymorphisms (SNPs) or insertions/deletions (indels), are identified and annotated. Additionally, genome assembly is performed to reconstruct the full genome sequence based on the mapped reads. This step allows for the identification of structural variations and complex genomic rearrangements. Biospecimen and data distribution for the multi-omics approach is an integral part of biobank sustainability and participation in scientific projects. Before the preparation of molecular analysis, the repository provides necessary information about the specimens, including sample quality reports.For the implementation of Best Practices in the QC of serum, plasma, and tissue samples prior to their application in multi-omics assays, we developed comprehensive workflows. As results of high-throughput studies strongly depend on the quality of the biospecimens, the essential part of plasma and serum QC should include an assessment of hemolysis, as described above in the Materials and Methods section. Following RNA extraction from serum and plasma, we included the molecular assessment of RNA in the QC workflow. The efficiency and correctness of the RNA purification process were analyzed on the basis of deviations in cycle threshold (Ct) in the set of synthetic RNA spike-ins. To identify the outlier samples with quality not sufficient to perform further omics assays, we determined the set of quality assessment factors, as presented in In the process of developing the QC workflow for fresh-frozen tissue samples collected from the tumor center, tumor periphery, and normal adjacent tissue, the first step included histopathological evaluation of collected biospecimens. After meeting the inclusion criteria, RNA from tissue samples was isolated and subjected to QC that in the primary step included the assessment of RIN. For all tissue samples, the checkpoint of QC was performed by nucleic acids fluorometric quantification . Detailed data on the average concentration values are presented in Samples with RIN ≥ 7 were then qualified for the next stages of evaluation, as described in the Materials and Methods section and presented in A total number of 218 fresh-frozen tissue samples from MUB Biobank were used in the process of QC workflow development, including 147 samples collected from the lung tumor periphery and 71 from the lung tumor center. The final diagnosis made on the basis of microscopic tissue examination was lung adenocarcinoma in 121 cases and squamous cell carcinoma in 97 cases. A total number of 152 fresh-frozen tissue samples collected from adjacent normal tissue were used as control samples.The combined workflow developed to address essential preanalytical and analytical QC in miRNA research conducted on liquid and tissue samples is shown in To avoid the potential influence of preanalytical aspects on plasma/serum metabolome, plasma metabolic fingerprinting was applied with the use of samples collected from patients the day before lung cancer surgery and three months after this surgical procedure. For the purpose of this test, two sets of plasma samples were prepared: one with plasma stored in cryotubes and one with plasma stored in Eppendorf tubes. The results of plasma metabolic fingerprinting performed with liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) method revealed that samples collected from patients before surgical tumor resection were separated from the samples obtained from the same patients after the surgery  Panel A.Biobank functions as a comprehensive infrastructure consisting of interdisciplinary teams responsible for specific tasks based on detailed SOPs, including patient qualification, personal and clinical data collection, and collection, storage, and quality control of biological material. During the biobanking process, each team must report on its activities and is a subject of routine control for compliance with procedures. Biobanking for challenging omics analyses requires a strict following of the scheme since the quality of samples undoubtedly strongly affects the final results . BiobankThe amount of molecular information obtained from various biospecimens depends on handling and processing the samples. Well-prepared and deposited high-quality samples allow for obtaining reliable, corresponding data for target validation and drug discovery. Analyzing this data across multiple sets can answer important biological questions about effective biomarker identification in any clinical stage of disease, also related to progression. Moreover, this information is essential to identifying disease subtypes and understanding which patients will likely respond to the candidate medications in the treatment algorithm. High-quality biobanking can lead to better results of an investigation using the multi-omics approach, effective and better-targeted clinical trials, decreased discovery costs, expanded drug development, and final effect-improved patient outcomes.In several experiments, the influence of warm and cold ischemia time on protein and gene expression have been investigated ,26,27. EBiofluids are characterized by low levels of RNA, high levels of inhibitors, and sensitivity to many preanalytical variables. Therefore, it is essential to provide specialized and optimized protocols for QC procedures. Fluorescence-based quantification assays that enable precise quantification down to as little as 1 pg/μL  are ofteBiospecimens used for diagnostic procedures, clinical testing, or biomarker identification must be collected under strictly regulated conditions to minimize the effect of preanalytical biases on study results . TherefoCooperation between biobanks, researchers, and industry is fundamental to accelerating research and integrating innovative translational activities. Nevertheless, the establishment and management of biobanks require large workloads combined with the preparation of multiple bases: ethical, legal, scientific, and clinical. Furthermore, building a committed team with well-qualified personnel aware of the whole set of procedures presents a challenge. Despite these obstacles, we need to facilitate the procedures for supporting the internationalization of access to biobank resources, since the biospecimens and data obtained in controlled and standardized conditions should be adopted for research on novel approaches to cancer prevention, early diagnosis, and personalized treatment. To facilitate access to detailed QC workflows, datasets, and practices supporting omics research, we have made efforts to ensure their availability on the MUB Biobank website in the Data Repository . This inTo further enhance the accessibility and visibility of MUB Biobank resources, which became a member of the Biobanking and Biomolecular Resources Research Infrastructure, European Research Infrastructure (BBMRI-ERIC) Consortium, in 2020, the data about collections and datasets from MUB Biobank will soon be included in the BBMRI-ERIC Directory. The BBMRI-ERIC Directory serves as a valuable platform for researchers worldwide, providing them with the means to discover and access biospecimens and related data from various biobanks . By addiIn summary, our study addressed the challenges posed by novel molecular techniques in biobanking for high-throughput translational research. By using biospecimens and data collected under highly standardized conditions, we developed specific quality control workflows for serum/plasma and tissue samples. These workflows, tailored to the needs and capabilities of different biobanking entities, encompass various steps, such as hemolysis monitoring, RNA extraction control, cDNA library quality assessment, and NGS raw data verification. By implementing these molecular-based quality control methods, biobanks can ensure the provision of biospecimens with confirmed omics-grade quality for advanced research applications. The workflows we proposed offer a reliable process for qualifying specimens prior to conducting multi-omics assays, demonstrating high sensitivity and specificity. This contribution to the field of biobanking promotes standardized practices, enhances research reproducibility, and fosters reliable translational research outcomes. The novelty of the presented workflows lies in their integration and comprehensive nature, providing researchers with a valuable tool for ensuring the quality of biospecimens in high-throughput research where existing guidelines may be limited or fragmented.


The toxicogenomic approach has gained increased attention for understanding key molecular events within organisms to deal with exposure conditions. However, finding relevant biomarkers to predict toxicity still remains a challenging task. In this study, we investigated machine learning applications as a tool to aid toxicity prediction and gene ranking method by training eight different standard supervised machine learning classifiers with qPCR gene expression data. We have shown that the toxicogenomic approach can benefit greatly from machine learning to identify unique expression profiles and predict toxicity, as well as visualization of genes by using Shapley values.Daphnia magna gene expression by using quantitative PCR. A method for gene ranking was explored using techniques from game theory, namely, Shapley values. The results show that standard machine learning classifiers can classify both Zn concentration and water hardness simultaneously, and that Shapley values are a versatile and useful alternative for gene ranking that can provide insight about the importance of individual genes.Zinc (Zn) is an essential element that influences many cellular functions. Depending on bioavailability, Zn can cause both deficiency and toxicity. Zn bioavailability is influenced by water hardness. Therefore, water quality analysis for health-risk assessment should consider both Zn concentration and water hardness. However, exposure media selection for traditional toxicology tests are set to defined hardness levels and do not represent the diverse water chemistry compositions observed in nature. Moreover, these tests commonly use whole organism endpoints, such as survival and reproduction, which require high numbers of test animals and are labor intensive. Gene expression stands out as a promising alternative to provide insight into molecular events that can be used for risk assessment. In this work, we apply machine learning techniques to classify the Zn concentrations and water hardness from  Daphnia magna reproduction test lasts for 21 days [The amount of zinc (Zn) intake for humans has an influence for many physiological functions that can cause both deficiency and toxicity symptoms. The recommended dietary allowance (RDA) for the essential trace metal Zn for adults over 19 years old is 11 mg/d for men and 8 mg/d for women . Zn is n 21 days . AssessiOrganisms regulate their gene expression to cope with the changes in water chemistry to maintain homeostasis. Analysis of the key genes that respond to changing hardness and Zn levels can be used to generate representative expression profiles for unique combinations of the two variables. These profiles can be used to predict the exposure conditions of test subjects from different origins. There are various approaches for collecting gene response data for studying toxicological outcomes of organisms, such as quantitative PCR (qPCR), microarrays, and RNA-Seq. qPCR is commonly used to gain insights into emerging toxicity . It is lMachine learning (ML) is a subfield of artificial intelligence (AI) that can construct hypotheses and find patterns to explain complex relationships in the data and is therefore suitable for molecular biology data. ML has previously been used for water quality assessment , microarThe advantage of using ML is that it can provide a fast, accurate, and un-biased classification of the Zn concentration and water hardness. Another advantage is that it can provide insights into the data and the importance of individual genes that is of use for biologists, who are trying to discover relationships between genes and diseases, but also the relationships among genes .Daphnia magna collected using qPCR. Furthermore, an analysis of the selected 22 genes was performed using the feature selection method of χ2, and feature ranking using a trained Random Forest (RF) and Shapley values in order to rank the genes that are most optimal for the tasks of Zn level estimation and water hardness prediction.The aim of this study was to evaluate ML techniques to classify Zn concentrations and water hardness from gene expression with 22 genes from The goal in many ML applications is to use a given dataset to train algorithms that learn to perform a specific task. In supervised learning, where the target outputs are known (input data is labeled), the task could be to classify the input data into discreet categories or predict it into a continuous real value. On the contrary, in unsupervised learning the targets are unknown, but the task can then instead be to find patterns or clusters in the unlabeled input data. Classification and prediction can also be used in an unsupervised (or semi-supervised) setting, but the key factor for achieving high performance is to use the correct ML model with a large, labeled training set in a supervised setting . HoweverThe measure of success for a trained ML model is tested by validating how well it can generalize on new unseen test data. A key factor is to avoid poor generalization and overfitting, where the model performs well on the training data but not on unseen test data. This is influenced by the choice of model and model settings (amount of regularization), pre-processing and normalization procedures, the number of samples, and the number and selection of attributes (genes). Another aspect to keep in mind is to avoid any difference in the distribution between the training data and test data; for example, if biological data are collected in different settings such as using different handlers, experimental parameters, organisms, genomic contexts, or growth conditions . This isIn this work, the process of applying ML is to try out several choices for ML model selection, normalization method, feature (gene) selection and use it for training, and then determine which choices give the optimal performance during validation. For validation, a k-fold cross-validation  is used https://github.com/martinlangkvist/geneexpression (accessed on 1 May 2023). All models used standard values for hyperparameters from sci-kit learn 1.0.2 with the exceptions that K-nearest neighbor (KNN) used 5 nearest neighbors, Random Forest (RF) used 1000 estimators and max depth of 10, and Neural Network (NN) used ADAM optimizer, learning rate 0.1, 1 layer of 50 hidden units with ReLU activation function.The code used in this paper can be accessed at One key factor of success for any ML model is to have enough training data to capture the distribution of the complex relations between the input and the target output. The amount of data needed depends on the complexity of the data and is difficult to know beforehand, but a common rule of thumb is that the training data should have at least 10 times more samples than the number of input features . For genFinally, one disadvantage of ML predictive models is that they are often black boxes in the sense that the model provides little insight into how it arrived at a certain decision. To achieve good performance on the task the model is trained for, interpretability might not always be necessary. However, if it is achieved, it will result in ML models being more reliable for applications in patient diagnosis, risk assessment, and decision-making. Furthermore, it would give insight into the input data and explain which features are important, which for biologists can provide new knowledge about the importance of certain genes. Some general models for ML interpretability are locally interpretable model-agnostic explanations (LIME) , layer-w2-test, which scores each gene by measuring the dependence between each gene and the class so that genes that are independent of class, and therefore irrelevant for classification, get a lower ranking. This has previously been used for gene selection [There are several methods for ranking candidate genes within statistics (hypothesis tests), machine learning techniques , and game theory . One common statistical hypothesis test used for gene ranking is the χelection .Within ML there are standard feature selection methods, such as sequential forward selection which has been used for gene selection . Some MLThe method for using ideas from game theory for gene expression analysis was proposed  where thDaphnia magna; a freshwater Cladocera commonly used in toxicity testing. The gene expression data used in this study was obtained from our previous study conducted by following OECD guidelines with media modifications to achieve desired Zn and hardness concentrations [3/L), medium (100 mg CaCO3/L), and hard (200 mg CaCO3/L). For details on experimental setup, RNA isolation, and gene expression analysis, see [The study was conducted using trations ,44. Expomta, mtb, mtc, vtg1, vtg2, ecra, ecrb, vmo1, hsp60, ftz1-f1, e74, e75nr, usp, vasa, hsp70, hsp90, cat, gst, dap1, jhe, cyp314, and hsp90b.The data set contains 128 samples with 22 genes and was collected by a single handler. The 22 genes in the data set that is used in this work are: We first analyzed the dataset by classifying samples for their hardness level, Zn level, and the combination of both. The dataset contained fold change values for 128 samples with 22 genes obtained by using ∆∆Ct method . These gTo further analyze the importance of the classes on gene expression, we then tested eight different classifiers to compare their classification accuracy . ClassifTo gain more insight into how each class affected the prediction results and where the confusion between classes occurred, we generated a confusion matrix for class predictions with the best classifiers. The confusion matrix for water hardness class is summarized in Similarly, Zn concentration classification prediction for all the tested concentrations ranged between 88% to 92% . InteresFinally, we constructed a confusion matrix combining Zn concentration and water hardness classes. The confusion matrix for the combination classifier calculated with Random Forest classifier can be seen in gst and vtg1, did not have overlapping values for medium and hard water hardness , water hardness (3 classes), and the combination of both (16 classes). The accuracy ranged between 86% and 91% for the first two tasks and reached 82% for the final task. The best model was between Random Forest and Neural Network depending on the task, which could merely be a consequence of non-exhaustive model parameter search. The added benefit of using Random Forest, besides achieving good classification performance, is that it naturally provides feature importance estimates and requires minimal hyperparameter tuning . The maiDaphnia, as supported by previous studies. This approach of utilizing game theory techniques can enable biologists to manually examine the influence of each gene in different scenarios of interest, rather than relying on only the average gene performance as is typically done in other gene ranking methods.The use of Shapley values to visualize the impact of individual genes on overall classification performance, per-class classification performance, and per-sample gene impact has been explored in this paper. Machine learning applications in biological studies should aim to identify previously unknown genes for prediction and/or classification tasks. However, any approach may provide a matching result from a machine learning standpoint and needs to be validated with true results. Therefore, this study constitutes the first step of validation of tested applications on a smaller dataset, where the interpretation of biological significance is established. Our analysis has identified two crucial genes that play a role in oxidative stress and reproduction. The impact of the exposure on oxidative stress and reproduction was evident through phenotypical endpoints, and our gene ranking results were consistent with well-established stress biomarkers for This work shows that unbiased interpretation of gene expression data by machine learning is a useful tool for toxicogenomics and the work can be extended to other applications of gene expression analysis. Unique gene expression profiles obtained from different exposure conditions can be used to train machine learning, which in turn can be used to predict exposure conditions. The learning outcomes of this study can be further tested with larger data such as transcriptomics, which would potentially lead to the discovery of novel biomarkers.


Colorectal cancer (CRC) is a heterogeneous disease, with subtypes that have different clinical behaviours and subsequent prognoses. There is a growing body of evidence suggesting that right-sided colorectal cancer (RCC) and left-sided colorectal cancer (LCC) also differ in treatment success and patient outcomes. Biomarkers that differentiate between RCC and LCC are not well-established. Here, we apply random forest (RF) machine learning methods to identify genomic or microbial biomarkers that differentiate RCC and LCC.RNA-seq expression data for 58,677 coding and non-coding human genes and count data for 28,557 human unmapped reads were obtained from 308 patient CRC tumour samples. We created three RF models for datasets of human genes-only, microbes-only, and genes-and-microbes combined. We used a permutation test to identify features of significant importance. Finally, we used differential expression (DE) and paired Wilcoxon-rank sum tests to associate features with a particular side.PRAC1 expression was the most important feature for differentiating RCC and LCC in the genes-only model, with HOXB13, SPAG16, HOXC4, and RNLS also playing a role. Ruminococcus gnavus and Clostridium acetireducens were the most important in the microbial-only model. MYOM3, HOXC4, Coprococcus eutactus, PRAC1, lncRNA AC012531.25, Ruminococcus gnavus, RNLS, HOXC6, SPAG16 and Fusobacterium nucleatum were most important in the combined model.RF model accuracy scores were 90%, 70%, and 87% with area under curve (AUC) of 0.9, 0.76, and 0.89 for the human genomic, microbial, and combined feature sets, respectively. 15 features were identified as significant in the model of genes-only, 54 microbes in the model of microbes-only, and 28 genes and 18 microbes in the model with genes-and-microbes combined. Many of the identified genes and microbes among all models have previously established associations with CRC. However, the ability of RF models to account for inter-feature relationships within the underlying decision trees may yield a more sensitive and biologically interconnected set of genomic and microbial biomarkers.The online version contains supplementary material available at 10.1186/s12885-023-10848-9. Despite being part of the same organ, colorectal cancer tumours can have different pathogenicity, histology and patient outcomes depending on subtype  and whicGut microbiota has been shown to play an influential role in CRC carcinogenesis and progression. However, the mechanisms by which this occurs largely remains unknown . In addiMachine learning (ML) methods are frequently applied for classification in tasks that rely on high-dimensional genomic data. Here, to query the relationships between the expression of genomic features in CRC and microbial content, we use Random Forest (RF) classification . We seleHere, we explore the ability of RF models to predict CRC sidedness using three different datasets: human genomic feature expression level (RNA-seq), microbial count data (from unmapped human reads), and a combined genomic feature and microbial count dataset. We subsequently use differential expression (DE) analysis on the most important features of the RF model  to find differential genomic and microbial features and relationships between RCC and LCC. Finally, we discuss the possible biological mechanisms driving differences in these biomarkers.308 colorectal cancer tumour samples were obtained from patients via surgical resection . Patients with inherited CRC and those who had received preoperative chemotherapy or radiotherapy were excluded. Patients were over the age of 18 and gave written informed consent. Tumour tissue was obtained between January 2002 and January 2016, with a median tumour tissue date of August 2006. The study was approved by the University of Otago, New Zealand, Human Research Ethics Committee . Patient and clinical data, including anatomical site of tumour, in addition to genomic and microbial data profiles were available for all patients. Samples were snap frozen in liquid nitrogen at time of surgery and stored at -80 °C and transitioned for RNA Extraction using RNAlater®-ICE. RNA was then extracted using the QIAGEN RNAEasy mini kit and sequenced using Illumina HiSeq machines (2 × 125 bp PE v4 sequencing). The samples were machine-randomized to limit any machine-specific noise or calibration bias. Raw Sequence Reads are available at SRA Accession: PRJNA788974.Sequence reads were first mapped to the human genome (GRCh38) using STAR (v2.73a). The remaining unmapped reads were classified using Kaiju (v1.6.2) to obtain microbial abundances , 14. RawThe RF models were built on the following training datasets: the first contained 58,677 TPM normalized genomic features, the second contained CPM normalized microbial counts for 28,557 taxa, and the third contained a combination of both. A separate validation cohort of 30 samples  was held out from model generation, leaving 278 patient samples for model development. Genomic and microbial data was available for all 308 patients.The RF models were parametrized in parallel on high-powered cluster computing nodes with 8,136 cores in 226 × Broadwell nodes, and a total system memory of 31 TB.RF models were generated using the Python-based scikit-learn random forest module , 16. ModEach model was trained using the finalized set of parameters using a 75% train, 25% test split on the dataset of tumor samples from the 278 different patients. The model metrics of accuracy, out-of-bag score, f1 score, ROC AUC score, recall, and precision for each of the three models are reported in Table The feature importance scores  were extracted from each of our three RF models. Given that the models have large numbers of features (greater than 50), it is prudent to perform feature reduction such that only features with high importance (weight) and a high degree of statistical evidence are retained. Using the R package Rf2pval , we implDE analysis was performed on each of the feature lists from the three models using edgeR . WilcoxoWe found that the random forest models from all three datasets clearly differentiated between LCC and RCC. Model accuracy on the validation sets ranged from 0.7 to 0.9, with genomic features having an accuracy of 0.94 and 0.9 on the training and validations sets, respectively microbial counts having an accuracy of 0.76 and 0.7, and genomic features with microbial counts having an accuracy of 0.8 and 0.87 Fig. . Out-of-PRAC1) gene, which has higher expression in LCC. Other significantly important features included those in the HOX family of genes, HOXB13, HOXC4, HOXC6 and HOXC8.Of the 15 significant features in our gene-only dataset, Fig.  the highTable Ruminococcus gnavus, which shows higher counts in RCC . There is also a subset of RCC that show higher expression of HOXC4, HOXC6, and HOXC8 (middle of heatmap), although not all RCC exhibit this pattern. Heatmap for microbes-only model is shown in Supplementary Fig. We used hierarchical clustering to ascertain connections between gene expression profiles and clinical characteristics and consensus subtyping scores Fig. . As expePRAC1, SPAG16, HOXC4, RNLS, HOXC6 and PRKAA2; and six microbes which were common to both our microbes-only, and genes-and-microbes models: Ruminococcus gnavus, Ruminococcus sp. TF10-6, Selenomonas sp. CM52, Verrucomicrobia bacterium, Anaerostipes caccae and Turicibacter sanguinis.In total, our models discovered 107 unique genomic and microbial features which played a significant role in the differentiating between CRC anatomical sides. Only six genomic features were common to both the genomic and genomic-plus-microbial models: Many of the previous studies on RCC vs. LCC and gene expression have used the publicly available TCGA data. Here, we used a novel dataset of 308 patients, with microbial data from human unmapped reads, which adds to the growing body of evidence of the genomic and microbial differences between the sites , 21.Fusobacterium, Prevotella, Clostridium, Akkermansia, and Ruminococcus are among the most frequently reported bacteria in studies on CRC-related microbial dysbiosis [One difficulty in characterizing the roles of the microbiome and the genome in RCC vs. LCC is that there is genomic and microbial heterogeneity both between and within the two anatomical locations . A primaysbiosis , 25. AllAll three RF models showed strong predictive accuracy. The microbes-only model showed the poorest predictive capability while the genes-only model was the highest performing. It is perhaps surprising that the combined model was not the most predictive. We postulate that this may be due to the fact that while microbes and genes may both affect CRC, microbial taxa are in fact indirect players, with effects that are reflected as altered genomic expression within the tumour, leading to cancer growth.Finally, there were a number of highly important genes that differed between the genomic features only and combined RF models. One other point of interest is that there are some different top genes in the genes-only model when compared with the genes-and-microbes model. This may suggest that these genes and microbes act in consort and our genes-and-microbes RF model may have identified some underlying biological interactions.HOX family of genes was characteristic of RCC. Specifically, we observed an upregulation of HOXC4, HOXC6, HOXC8, and HOX-related lncRNA AC012531.3, and a downregulation of HOXB13 (Tables HOX (homeobox) gene family is most well-known for guiding embryonic development [HOX mutations that cause either increased or decreased expression have been associated with several types of cancer [The RF models showed that increased expression of the elopment . HOX mutf cancer  as tumorf cancer .Ruminococcus gnavus, Clostridium acetereducens, Lachnospiraceae, and, Ruminococcus sp. TF10-6  models and differential gene expression (DE) to discover and associate genetic and microbial biomarkers with LCC and RCC. Three RF models with accuracy scores of 0.9, 0.7, and 0.87 were created and these yielded 15, 54 and 46 significantly important features. DE analysis was used to quantify changes in expression between CRC side. Our genes-and-microbes model identified microbes that did not appear in our microbes-only model, including Below is the link to the electronic supplementary material.Supplementary Material 1


Single cell RNA sequencing technology (scRNA-seq) has been proven useful in understanding cell-specific disease mechanisms. However, identifying genes of interest remains a key challenge. Pseudo-bulk methods that pool scRNA-seq counts in the same biological replicates have been commonly used to identify differentially expressed genes. However, such methods may lack power due to the limited sample size of scRNA-seq datasets, which can be prohibitively expensive.Motivated by this, we proposed to use the Bayesian-frequentist hybrid (BFH) framework to increase the power.In our idiopathic pulmonary fibrosis (IPF) case study, we demonstrated that with a proper informative prior, the BFH approach identified more genes of interest. Furthermore, these genes were reasonable based on the current knowledge of IPF. Thus, the BFH offers a unique and flexible framework for future scRNA-seq analyses. Traditional bulk RNA sequencing technologies measure the average expression level of all cells in the population, which mask the uniqueness of each cell. In contrast, isolation of cells is an important step in scRNA-seq. It enables the identification of different cell types within complex tissues . As demoscRNA-seq has the advantage in processing thousands or even millions of single cells simultaneously  and has One important question in analyzing scRNA-seq data is the identification of differentially expressed genes (DEG) between groups. Compared to the gene expression data generated from other technologies, scRNA-seq data have some unique features including overdispersion, sparsity, high proportion of zeros due to dropout events , and the hierarchical structure embedded in the data . Early sWith more and more gene expression data becoming publicly available, many approaches and tools for the differential expression (DE) analysis have been developed for scRNA-seq data. For example, ZINB-WaVE  and ZingPseudo-bulk methods, which pool the scRNA-seq counts in the same biological replicate, have been developed to address this variability. Regardless, pseudo-bulk methods could still lack power to detect genes of interest due to the limited sample size. To overcome these limitations, we propose to use a Bayesian-frequentist hybrid (BFH) inference method to analyze the scRNA-seq data at the individual level. The BFH theoretical framework was originally proposed by 2.2.1.sup were taken in the space of BFH inference is designed for models that have both frequentist and Bayesian parameters . Supposeibution.  proved t2.2.The Lungmap dataset used in this study was from a published human lung tissue study . The celThe idiopathic pulmonary fibrosis (IPF) scRNA-seq dataset was obtained from a previous study on pulmonary fibrosis (PF) disease mechanisms and the corresponding cell types in human lung tissues . This daWe also obtained bulk RNA-seq IPF data from human lung tissues  in the p2.3.For scRNA-seq dataset, we selected genes with average expression level across cells greater than 0.1. We removed cells when the number of detected genes was below the lower 2-percentile or with more than 10% of mitochondrial gene expression. For the cell weight calculation, we aligned Lungmap dataset and IPF single cell RNA-seq datasets by their common genes, resulting in 13,988 common genes and 44,294 and 50,383 cells for Lungmap and IPF, respectively. For coefficient estimation of each gene, we matched the IPF scRNA-seq and IPF bulk datasets and obtained 7,886 common genes.2.4.n and the number of regression parameters p, the data can be arranged in Y of dimension n × 1 as the response, and X of dimension n × p as the design matrix. The regression parameter in the model β can be arranged in a vector of dimension p × 1.Here, we would like to introduce each of the methods we attempted to identify genes associated with IPF. In linear regression analysis, given the sample size Y is the weighted average of a gene’s expression for a particular cell type at the individual level, p=1, and X the design matrix is composed of a vector of 1s, X1 the disease group of the individual (control or IPF), and X2 the predictive probability of each cell belonging to alveolar macrophage averaged per individual with subsequent negative log transformation. The linear model is I is an identity matrix with dimension n by n. So In our analysis the outcome The conjugate prior of the regression parameter can be written as Finally, following the EM algorithm in [Step 1.] Initialize parameters [Step 2.] Given the current value of frequentist parameters [Step 3.] Given [Step 4.] Iterate steps 2-3 as in EM algorithm.p-value from the two-sided test  of whether this value is equal 0 or not based on the underlying normal distribution.For frequentist, Bayesian, and hybrid inferences can all generate parameter estimates and the corresponding estimation variances. An estimate and its variance are used to construct a 95% confidence interval (estimate minus and plus 1.96 times of the standard error) and to calculate a 2.5.Alveolar macrophage cells have been recognized to play a crucial role in the pathogenesis of IPF . Rather HierXGB is a supervised machine learning algorithm that aims to classify each single cell in the query dataset into one of cell types from a reference dataset. With a pre-defined cell-type hierarchical tree structure, the algorithm annotates the cell from ancestor to one of descendant subtypes iteratively until reaching the bottom layer. For dataset with a clear cell type hierarchy, including Lungmap, it outperforms other state-of-arts methods in terms of both accuracy and efficiency . In the 2.6.X2. A higher X2 indicates that the cells from this donor are more likely from alveolar macrophages. We also used this alveolar macrophage probability to generate outcome Y. For each gene, Y was also a length-22 vector, where the value was the weighted average of counts in this gene across cells. The weights were alveolar macrophage probabilities, and such weighted averages are called pseudo-bulk counts in single cell data analysis. A higher value of Y indicates that this gene expresses highly in alveolar macrophage cells.In our example, we averaged the probability of being in alveolar macrophages across cells within each of the 22 donors and generated a length-22 vector as the predictor 2.7.diag for We use non-informative priors such as  and 2.8.Pathway analysis is usually performed using a set of selected features, in this case, differentially expressed genes . The goa3.3.1.We exemplified and compared the frequentist, Bayesian, and hybrid inferences with and without informative prior. TGF-β pathway is well-known in terms of its role in pulmonary fibrosis, therefore, we used the results of TGF-β1 as an example . In the Frequentist: All model parameters were frequentist parameters, and ordinary least square estimates were reported.Bayesian inference with non-informative prior: A non-informative prior distribution was imposed on all the parameters Hybrid inference with non-informative prior: The same non-informative prior was imposed on The Bayesian and hybrid Bayesian inference with informative prior had the Normal distribution with mean −0.31 and variance 0.096 as the prior for The frequentist and Bayesian analyses for the samples with averaged expression across all predicted alveolar macrophages cells by HierXGB had parameters With non-informative prior, the frequentist with Bayesian inferences resulted in similar estimates of fibrosis .We also conducted analysis on the samples with average expression of cells predicted as alveolar macrophage by HierXGB. In this analysis the probability of alveolar macrophage prediction was no longer used but the expression was averaged across identified alveolar macrophages cell types. This analysis was consistent with the traditional pseudo bulk analysis, ignoring the predictive probability of cell identity. The frequentist regression analysis p-value is equivalent to that from the two-sample t-test, because the independent variable phenotype is binary, and the F-statistic from regression (or ANOVA) is the square of the t-statistic in the t-test. In this analysis, the Bayesian inference with non-informative prior had similar results as frequentist and both were not significant. Such inference was worse than BFH method with informative prior.As a result, the analysis of TGF-β1 gene indicates that BFH inference outperforms both frequentist and Bayesian inference. The inclusion of cell type predicted probability for all the cells , and the informative prior were all valuable for identifying potential significant genes.3.2.We applied each of the five methods to the IPF single cell dataset. The hybrid method with an informative prior detected the largest quantity of genes and biological meaningful pathways. The hybrid method with informative discovered most pathways with a total of 38, whereas the Bayesian method discovered 36 . The top4.Analysing scRNA-seq data has been a challenging topic, especially given the high cost of running the experiments, which typically results in limited sample sizes. Pseudo-bulk methods, which pool scRNA-seq counts per patient per cell-type, have been commonly used for DE gene detection. However, its performance also relies on the sample size, and hence may lack detection power when sample size is limited. A natural way to overcome this challenge is to borrow information from other studies. Here, we have shown that BFH with informative priors should be considered and has advantages over other approaches. BFH can be viewed as an adaption of Bayesian method and can incorporate prior information with potentially less uncertainty compared with Bayesian methods.In our BFH analysis, we used pseudo-bulk summarization as the response variable. However, the way to calculate pseudo-bulk is still a topic of discussion in the field. While many researchers have used the annotated cell types directly and summarized the data within a particular annotated cell type, such summarization may potentially lose information since the annotation is based on classifiers with certain thresholds to define the cell types. In our study, we derived the cell-type-specific probability for each cell instead of relying on a classifier to define the cell types. We used this probability as the weight to summarize the data to avoid loss of useful information.1 so that the prior would have better dispersion. Properly setting priors remains as an interesting topic and should be explored further. The BFH method inherits flexibility from the Bayesian framework and can be used iteratively to integrate the current results as new prior information with the new data when appropriate.To boost the power of our coefficient estimate, we used bulk RNA-seq data as the prior for each gene. While bulk RNA-seq data are not cell-specific, they offer several advantages as informative priors. First, bulk data have better coverage than scRNA-seq data, thus providing prior information on a more compressive gene list than a typical scRNA-seq experiment. Second, it is relatively inexpensive and readily available. As we have summarized the scRNA-seq data into pseudo-bulk format, the prior derived from bulk RNA-seq data is compatible with our scRNA-seq data. In addition, we transformed the sample variance for βOur case example demonstrated the substantial increase in detection power of BFH framework when using informative priors. When non-informative priors were employed, either no differentially expressed genes were identified or only a small number were found. The use of informative priors significantly increased the detection power, as evidenced by the reasonable pathway identified for IPF in terms of the underlying mechanism. The work reinforces the importance for TGF-β pathway and cytokines such as TNF and IL1/IL4, which are well-known for their roles in the IPF mechanism . ConsequA potential limitation of the BFH method is its heavy reliance on informative priors. In situations where relevant bulk RNA-seq or scRNA-seq data are unavailable, alternative data types such as methylation data could be considered as prior information. However, developing such priors needs biological justification and consideration of how to align such data types to pseudo-bulk format of scRNA-seq data. When alternative data types are unavailable, the use of non-informative prior for parameters is inevitable. As discussed in literature , using BDespite its limitations, the BFH method is a flexible approach with the capability to incorporate informative prior to enhance detection power. The current framework of the BFH method is implemented using conjugate priors, which reduces the computation time and makes it a suitable method for high throughput analyses in the future.


The classification of glioma subtypes is essential for precision therapy. Due to the heterogeneity of gliomas, the subtype-specific molecular pattern can be captured by integrating and analyzing high-throughput omics data from different genomic layers. The development of a deep-learning framework enables the integration of multi-omics data to classify the glioma subtypes to support the clinical diagnosis.Transcriptome and methylome data of glioma patients were preprocessed, and differentially expressed features from both datasets were identified. Subsequently, a Cox regression analysis determined genes and CpGs associated with survival. Gene set enrichment analysis was carried out to examine the biological significance of the features. Further, we identified CpG and gene pairs by mapping them in the promoter region of corresponding genes. The methylation and gene expression levels of these CpGs and genes were embedded in a lower-dimensional space with an autoencoder. Next, ANN and CNN were used to classify subtypes using the latent features from embedding space. CNN performs better than ANN for subtyping lower-grade gliomas (LGG) and glioblastoma multiforme (GBM). The subtyping accuracy of CNN was 98.03% (± 0.06) and 94.07% (± 0.01) in LGG and GBM, respectively. The precision of the models was 97.67% in LGG and 90.40% in GBM. The model sensitivity was 96.96% in LGG and 91.18% in GBM. Additionally, we observed the superior performance of CNN with external datasets. The genes and CpGs pairs used to develop the model showed better performance than the random CpGs-gene pairs, preprocessed data, and single omics data.The current study showed that a novel feature selection and data integration strategy led to the development of DeepAutoGlioma, an effective framework for diagnosing glioma subtypes.The online version contains supplementary material available at 10.1186/s13040-023-00349-7. The accurate classification of brain tumors is crucial in modern clinical practice. For clinical decision-making, it is vital to distinguish various types of brain tumors, such as subtypes and primary gliomas, from metastases. Presently, the characterization of brain tumors is mainly performed based on imaging and histopathology –3. Due tp-value < 0.001) in all subtypes  and their corresponding survival-associated genes. The linking of these two layers of genomic data identifies the CpG-gene pairs, which are involved in cancer progression. We found that in LGG, a total of 1110 genes (DEGs) and 3204 CpGs (DMRs) in the promoter, and in GBM, 268 genes (DEGs) and their 447 CpGs (DMRs) in the promoter are linked to patient survival  and validation (30%) sets. 70% of the data was utilized to optimize the model’s parameters and evaluate the performance of each model, and the remaining 30% of the data was employed as independent predictors. The gene expression and methylation matrices were fed into the autoencoder with concatenated inputs (CNC-AE). The methylation and gene expression levels are combined and compressed in the latent space or bottleneck layer learned by the autoencoder , 27–29. We implemented two DL algorithms, i.e., artificial neural networks (ANN) and convolutional neural networks (CNN), and compared their performance for subtype classification. During the model training step, we used the grid search method to find the best combination of hyperparameters . Then, using these optimal hyperparameters, we performed stratified k-fold cross-validation k = 10) on the latent variables and computed the average performance measures for each DL model (Table 0 on the n = 3204 for LGG and n = 447 for GBM) and genes (n = 1110 for LGG and n = 268 for GBM) from preprocessed data. Then this unmapped, randomly selected methylation and gene expression data were fed into the autoencoder. Then, ANN and CNN were used to classify the subtypes using the latent features from random datasets. We repeated the process ten times, and the accuracy varied from 60.68 to 71.43% in LGG and 62.42 to 72.14% in GBM in all iterations  to the autoencoder and compared their performance to that of DeepAutoGlioma. First, we compared the performance of mapped CpGs and gene expression with randomized CpG-gene pairs as input into the autoencoder. We randomly selected the CpGs  lower  and 96.54%  using gene expression and methylation data, respectively  [n = 52) were divided into three groups based on their clinical information: classical (n = 16), mesenchymal (n = 22), and proneural (n = 14). Similarly, the LGG patients (n = 281) were divided into three groups based on cancer subtype, i.e., astrocytoma (n = 96), oligoastrocytoma (n = 75), and oligodendroglioma (n = 110). We obtained the external data set from the Gene Expression Omnibus (GEO) repository. The subtyping of LGG was validated using GSE74462, GSE43378 (gene expression data), and GSE129477 (DNA methylation data). The subtyping of GBM was validated using the gene expression data from GSE145645 and the DNA methylation data from GSE128654.We retrieved methylome (Illumina Infinium HumanMethylation450 platform) and transcriptome (RNA-seq) data of TCGA from UCSC Xena (csc.edu)  log2 .Univariate Cox regression analysis was implemented to build the prognostic risk-score model for a particular gene and CpG site . Univari……., ……., Where t is survival time, h(t) is the hazard function determined by a set of covariates , then we screened those CpG-gene pairs. Next, we constructed methylation and gene expression matrices using these CpGs and genes fed in an autoencoder using two separate layers.p-value < 0.05, the biological process or pathway was considered significantly enriched.We analyzed biological processes and pathway enrichment using the Metascape tool . EnrichmAutoencoders are feed-forward neural networks that aim to copy the input variable to the output variable with the minimum loss of information. It compresses the inputs into latent variables in the bottleneck layer’s embedding space and then reconstructs the output from the embedding space. The autoencoder is composed of two parts: the encoder and the decoder. The encoder maps the high dimensional input data into latent variables in embedding space, and the decoder reconstructs the input data from the embedding. Here, we used one concatenated layer, one hidden layer, and a bottleneck layer in the encoding part. We used a concatenated autoencoder to integrate the gene expression and methylation data. We used the Keras library with TensorFlow  to impleANNs, which imitate the human brain, are feed-forward neural networks. ANNs are represented by a weighted, directed graph connecting inputs to a series of interconnected “hidden” layers that are composed of multiple nodes called “neurons” that are in turn connected to an output layer . ANNs arCNN is a type of deep learning method that directly learns from the data. CNN consists of three layers: convolutional, pooling and fully connected (FC) layers . The conThe performance of the DL model was evaluated based on the eight criteria: accuracy, sensitivity, specificity, precision, F1-score, FPR, geometric mean, and MCC. A true positive (TP) would indicate that the cancerous cell is identified correctly, while a false positive (FP) indicates that the cancerous cell is identified as healthy. Conversely, true negatives (TN) and false negatives (FN) are calculated for healthy cells. The following equations define the metrics:We used the sklearn.metrics library in Python to calculate the above score by importing functions such as confusion_matrix and classification_performance.Pairwise comparison was done using the Mann-Whitney U test using Sigma Plot 11.0.Additional file 1: Supplementary Table 1. List of input features in autoencoder.Additional file 2: Supplementary Table 2. Model performance in LGG subtype classification using random features.Additional file 3: Supplementary Table 3. Model performance in GBM subtype classification using random features.Additional file 4: Supplementary Table 4. Model performance in LGG and GBM subtyping using preprocessed data as a feature.Additional file 5: Supplementary Table 5. Classification performance of deep learning algorithms on LGG and GBM subtyping using mono-omics data.