Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods  and four other FS methods . The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data. In recent years, a huge amount of data has been produced from several domains, such as business, economics, industry, biology, and medicine. It is difficult to obtain useful information from these raw data, with many points being irrelevant or redundant. A very high-dimensional dataset chokes the models due to the exponentially increased training time and an increased risk of overfitting. Therefore, feature selection (FS) is vital in data mining and machine learning model development for preparing the data properly . The priIn recent decades, researchers from various fields have conducted a wide range of studies on aspects of FS, including statistics, data mining, pattern recognition, and bioinformatics data analysis ,9,10,11.d features contains FS is a common discrete optimization problem. An FS problem with problems ,25,26.Heuristic-based approaches can usually solve any optimization problems since the search process is a black box, and no domain knowledge or prior knowledge needs to be obtained in advance. These heuristic-based approaches are randomly initialized with a variety of candidate solution(s) in the search space and iteratively refine the solution(s) based on the heuristic functions. Due to the population mechanism, heuristic-based approaches produce a variety of solutions during a search, which makes it suitable for multi-objective FS , especiaAll kinds of FS methods work well on single or multiple datasets, but there is room for improvement, especially when dealing with data with high-dimensional features. As the theorem “no free lunch”  states tThe organization of this paper is arranged as follows. Feature subset selection using exhaustive search is an NP-hard problem. Using heuristic-based methods to solve optimization problems has become increasingly popular due to their ability to efficiently search for a global optimum. Most heuristic-based algorithms are motivated by natural phenomena. For instance, the Simulated Annealing (SA) algorithm  is inspiHowever, regardless of the viability of such classical heuristic search algorithms, they cannot provide optimal solutions for all kinds of optimization problems, especially when dealing with high-dimensional optimization problems. Thus, a range of novel optimization algorithms have been proposed in recent years. In 2014, Xing et al.  identifiThere is a tremendous number of recent studies in the literature that utilize heuristic search algorithms for FS. For instance, in , The BBOSome heuristic-based algorithms have been used extensively for feature selection on gene expression datasets. For instance, reference  proposesIn addition to the methods introduced above, there are still many recent heuristic-based methods for FS in bioinformatics. They all try to solve FS problems with high-dimensional feature space. The significant advantage of heuristic-based approaches is that they do not need any prior knowledge nor any assumption of the feature search space, such as the space being linearly or nonlinearly separable. The fact that a number of datasets in bioinformatics, such as those containing gene expression data, are composed of mutual dependence and interacting features in high-dimensional search space makes heuristic approaches suitable to process these data. In this study, we propose a general feature selection approach based on EGFA, called EGFAFS. A detailed description of EGFAFS will be introduced in This section provides a full description of the EGFA in two different parts. The inspiration for the EGFA is given in The Explode Gravitation Field Algorithm (EGFA) is a novel nature-inspired heuristic algorithm and was proposed in 2019 by our research team. The basic idea of EGFA is to simulate the formation process of planets based on SNDM and the Big Bang Theory . In EGFAThe procedure of EGFA is divided into seven main steps, as shown in Step 1. Initialize. Initialize Algorithm 1. The pseudo-code of the Explosion Gravitation Field Algorithm.Input: the size of dust population 1. 2. Initialize the dust population of size whiledo3. 4.   Divide the dust population into 5.   Move surrounding dust particles to their center by Equations (2)–(4)6.   Some surrounding dust particles are absorbed by their center7.   Explosion strategy produces some dust particles by Equation (5)8.   end while9. 10. return the best solutionStep 2. Group. Randomly divide the dust population .0.068 in . In this 0.01 in .Step 4. Absorb. Some surrounding dust particles close to the center dust particle are absorbed by their center. In this process, the size of the dust population will decrease. Step 5. Explode. Some new dust particles will be generated by the explosion strategy based on the center dust particle. In this process, the size of the dust population will increase to the original scale. The new dust particle generated in this step is represented by the following formulation:Step 6. Check the stopping condition. If the stopping condition is not met, go to step 3; otherwise, stop the loop.The flow chart of EGFA is given in The original EGFA is proposed to solve continuous optimization problems, such as the Sphere, or Ackley benchmark problems in low-dimensional search space, having 2, 3, 5, 10, or 20 dimensions. The EGFA has achieved excellent performance in terms of efficiency and accuracy. However, at the same time, EGFA faces challenges when dealing with problems in higher dimensions in terms of accuracy and running time, like all population-based search algorithms.It is well known that FS is an NP-hard problem. Finding the best subset from a high-dimensional search space is a combinatorial optimization problem. In this study, we utilize EGFA to solve the combinatorial optimization problem, i.e., feature selection for datasets with high-dimensional feature space, called EGFAFS. To verify the performance of EGFAFS, we test EGFAFS on eight gene expression datasets with high-dimensional features. It is noted that the concept of “feature selection” for a general dataset corresponds to the concept of “gene selection” for gene expression data. Since gene expression data usually consist of relatively few samples characterized by high-dimensional features, simple feature selection methods, such as filter-based methods, are not suitable to deal with gene expression data. Our proposed EGFAFS constructs a recommended feature pool for initialization. Attaching more attention to the features in the recommended feature pool, rather than all features in the original feature space, can help find the best solution more efficiently. In addition, considering all features in the original search space during the Explosion process of EGFAFS decreases the probability of being trapped in local optima.Algorithm 2. The pseudo-code of constructing a recommended feature pool.Input: The No. of RFs 1. Whiledo2. 3.   Sample 4.   Feed the samples sliced by 5.   Compute the importance scores for features in End while6. 7. Merge all the importance scores for all features of the 8. Rank all features by sorting the scores of importance9. return Since gene expression data consist of high-dimensional features, we construct a recommended feature pool based on a series of Random Forests (RF) . Based oThe procedural steps of this strategy are described in detail, as follows:Step 1. Use the Python package named sklearn.ensemble to initialize Step 2. Randomly sample Step 3. Feed the samples with Step 4. Compute the importance scores of features in RF based on the Gini index.Step 5. Merge all the importance scores of features computed by Step 6. Rank all the features by sorting the scores of importance obtained by Step 5; the In this study, we set A Random Forest is composed of several decision trees (binary trees), and the features selected are used to decide to which class the input data belong. In this study, the average loss of the entropy criterion, such as the Gini index, is adopted for growing decision trees. The Gini index for each split node Therefore, if a Random Forest consists of The final score is given as Equation (10) after normalization.The original EGFA is a novel nature-inspired heuristic search algorithm proposed by our research team in 2019 for continuous optimization problems. Detailed information about EGFA is introduced in Step 1. Construct a recommended feature pool based on a series of Random Forests by ranking the scores of features’ importance. This step is described in n dust particles, the location of Algorithm 3. The pseudo-code of EGFA for feature selection (EGFAFS).Input: The size of the recommended feature pool 1. 2. Construct a recommended feature pool by RFs based on the Gini index3. Initialize the dust population of size whiledo4. 5.   Move the surrounding dust particles toward their center as Figure 36.   Some surrounding dust particles are absorbed by their center7.   Explosion strategy produces some dust particles as Figure 48.   End while9. 10. Return the optimal subset of featuresStep 2. With initial nt (MCC)  as EquatStep 3. Move the surrounding dust particles toward their center. For the Select a feature from the location of Select a feature from the location of its center randomly, which is named Replace the feature In addition, the features represented by the center will be shuffled in this step, which is the strategy of rotating used in this study. Step 4. The surrounding dust particles with a relatively smaller value of mass are absorbed by their center. Define Step 5. Several dust particles will be generated by the explosion strategy based on the center dust. The new Copy Select For The main loop from Step 3 to Step 5 will run for several epochs (50 epochs in this study). During the main loop, to evaluate a dust particle , we feed the training data sliced by the subset of features represented by the dust to the Support Vector Classification (SVC)  for traiIt is noted that the original EGFA was proposed for continuous optimization problems, such as Ackley and Sphere benchmark problems, but that EGFAFS is an improved version of EGFA for solving combinatorial optimization problems  for real-world data, such as gene expression data. Due to the different types of optimization problems solved by EGFAFS and the original EGFA, the processes of Move and Explode in EGFAFS are different from those in the original EGFA, as introduced in Finally, we have to emphasize that EGFAFS is a general feature selection algorithm which is capable of processing any type of numerical dataset, such as gene expression data.The Cancer Genome Atlas (TCGA) , a landmIn this study, each dataset we employ has few samples but is characterized by high-dimensional feature space. Additionally, for each dataset, the number of cases of each type of tumor is approximately ten times greater than the number of normal cases, which increases the difficulty of distinguishing between the tumor cases and the normal cases. Detailed information about the eight datasets is given in To comprehensively evaluate the performance of EGFAFS, seven widely used evaluation metrics are employed, including accuracy (ACC), F-measure (F1), Recall, Precision (PRE), Matthew’s correlation coefficient (MCC), the area under the Precision and Recall curve (AP) and the area under the ROC curve (AUC)  to measuThe metrics  are defined as Equations (13)–(17). The PR curve is a curve with the Precision (PRE) and the Recall rate as the axes, and the area under the PR curve is the AP. The ROC curve is a curve with the false positive rate (FP-rate) and the true rate (TP-rate) as the axes, and the area under the curve is the AUC. The value of ACC, F1, Recall and PRE range from 0 to 1, MCC ranges from −1 to 1, and AP and AUC usually range from 0.5 to 1. In general, the closer these values are to 1, the better the performance they represent.This section introduces the advantages of using the recommended feature pool. The number of iterations for EGFAFS is set to 50, the number of the Random Forests is set to 2000, the size of the recommended feature pool is set to 300, and the classifier is SVC.Both the EGFAFS initialized based on the recommended feature pool and the EGFAFS initialized in the original feature space are tested on the eight gene expression datasets. Regarding the three datasets: LUSC, THCA, and UCEC, the former has the same performance as the latter in all metrics . The comparison results for the other five gene expression datasets are shown in The parameter The parameter The parameter The parameter https://scikit-opt.github.io/, accessed on 12 March 2022) and initialized based on the recommended feature pool. During training, MCC is employed as the heuristic function for four heuristic-based methods to select features. The value of MCC ranges from −1 to 1, where −1 indicates the prediction is completely wrong, and 1 indicates that the prediction is completely correct. For a fair comparison, the number of the population is set to 50 and the maximum iteration is also set to 50 for five heuristic-based methods . For other methods, the default values of all the parameters are adopted. The target size of the features subset is set at 50, and each dataset is split into a learning dataset and an independent dataset with a proportion of 8:2. The learning dataset is further split into a training and a validation dataset by a random seed with a proportion of 6:4. Different random seeds lead to different training and validation datasets. Each method is trained on a training dataset and is validated on the corresponding validation dataset. After each method converges, the final selected features are evaluated on an independent test dataset. Seven commonly used metrics  are employed to compare the performance.To verify the performance of EGFAFS, four heuristic-based FS methods  and four other FS methods  are adopted for comparison. The four heuristic-based FS methods are implemented based on a Python package called scikit-opt   to perfoIn general, the Gene Ontology (GO)  databaseIn For the HNSC, 40 of the 50 genes are annotated to a GO term in the MF aspect. Moreover, four gene groups with approximately 20 genes are annotated to four GO terms in the CC aspect. The GO terms in the BP aspect are not significant. For the LIHC, a gene group with more than 30 selected genes is annotated to a GO term in the MF aspect. Additionally, two gene groups with more than 20 selected genes are annotated to two GO terms in the CC aspect. For the LUAD and LUSC, a gene group with approximately 20 selected genes is annotated to a GO term in the CC aspect. The GO terms in the BP and MD aspects are not significant. For the PRAD and THCA, all GO terms concerning three aspects are not significant, since the counts of genes for GO terms are less. For the STAD, four gene groups with 20–25 selected genes are annotated to four GO terms in the CC aspect, which is significant. For the UCEC, a gene group with more than 40 genes is annotated to a GO term in the MF aspect, and four gene groups with more than 20 genes are annotated to four GO terms in the CC aspect.We performed enrichment analysis on the gene sets selected by EGFAFS on eight datasets. For six datasets, the genes selected by EGFAFS are enriched in some GO terms with large counts of genes, and the GO terms are significant, belonging to high-level biological functions: CC and MF. For the other two datasets, the enriched GO terms are not significant due to the smaller counts of enriched genes. The overall results shown in In this study, we propose a novel FS algorithm based on EGFA, called EGFAFS. To investigate the performance of EGFAFS, we tested EGFAFS on eight gene expression datasets compared with eight FS methods.To implement the EGFAFS, we first utilize a series of Random Forests to measure the importance of the features based on the Gini coefficient. We then select 300 features with maximum scores of importance to build the recommended feature pool. When this process is completed, we initialize the dust population based on the recommended feature pool and calculate the mass of each dust particle based on a metric Matthews Correlation Coefficient (MCC). The main loop for EGFA consists of three steps: (1) Move and Rotate, (2) Absorb, and (3) Explode. We first pay attention to the features in the recommended feature pool (300 features) in the process of Move and Rotate, and then consider all features in the original search space  in the process of Explode. The experiments demonstrate that we should not only pay attention to the features in the recommended feature pool, but also consider all features in the original feature space, meaning that our EGFAFS works well for feature selection in a high-dimensional search space. To verify the performance of EGFAFS, we test our EGFAFS on eight gene expression datasets and compare it with four classical heuristic-based FS algorithms  and four other FS methods . Seven commonly used metrics  are employed to evaluate the performance of the five methods. The experimental results show that EGFAFS ensures better classification metrics than the other eight FS approaches.To further verify the significance of our EGFAFS for feature selection, we utilize GEPIA2 for tumor/normal differential expression analysis and construct differential co-expression networks for eight datasets. The results show that most of the genes our EGFAFS selected from eight gene expression datasets are in the differential co-expression network. In addition, the degrees of the selected genes are greater than the median value of the whole differential co-expression network.To investigate the biological meaning behind the genes selected by EGFAFS, we utilize the DAVID to perform enrichment analysis. For almost all datasets, the genes selected by EGFAFS are enriched for some biological functions, with large counts of genes. The results demonstrate that the genes selected by EGFAGS play an essential role in some biological functions.In summary, EGFAFS has good performance for feature selection on eight gene expression datasets, compared with eight other well-known FS methods. In addition, the genes selected by our EGFAFS play an important role in the differential co-expression network and in some biological functions. We believe that EGFAFS works well for feature selection.In this study, we developed a feature selection algorithm based on EGFA, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. We then paid more attention to the features in the recommended feature pool. To verify the performance of EGFAFS for feature selection, we tested EGFAFS on eight gene expression datasets compared with eight well-known FS methods. The results show that among the nine FS algorithms, EGFAFS has the best performance for feature selection on gene expression data in most evaluation metrics. Further analysis of the differential co-expression network and GO enrichment on eight datasets demonstrates that the genes selected by EGFAFS play an essential role in the differential co-expression network and in some biological functions.


Background: Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN).Methods: Gene expression data of two large cohorts of patients with periodontitis, GSE10334 and GSE16134, were downloaded from the Gene Expression Omnibus database. We screened for differentially expressed genes in the GSE10334 cohort, identified key periodontitis biomarkers using a Random Forest algorithm, and constructed a classification artificial neural network model, using receiver operating characteristic curves to evaluate its diagnostic utility. Furthermore, patients with periodontitis were classified using a consensus clustering algorithm. The immune infiltration landscape was assessed using CIBERSOFT and single-sample Gene Set Enrichment Analysis.Results: A total of 153 differentially expressed genes were identified, of which 42 were downregulated. We utilized 13 key biomarkers to establish a periodontitis diagnostic model. The model had good predictive performance, with an area under the receiver operative characteristic curve (AUC) of 0.945. The independent cohort (GSE16134) was used to further validate the model’s accuracy, showing an area under the receiver operative characteristic curve of 0.900. The proportion of plasma cells was highest in samples from patients with period ontitis, and 13 biomarkers were closely related to immunity. Two molecular subgroups were defined in periodontitis, with one cluster suggesting elevated levels of immune infiltration and immune function.Conclusion: We successfully identified key biomarkers of periodontitis using machine learning and developed a satisfactory diagnostic model. Our model may provide a valuable reference for the prevention and early detection of periodontitis. Periodontitis, one of the most common oral diseases, is associated with plaque biofilms and accompanied by periodontal attachment loss and alveolar bone resorption . WithoutWith the rapid development of microarray screening and high-throughput sequencing, bioinformatics analysis carries great significance in exploring the mechanisms, diagnosis, and prediction of prognosis of periodontitis. Biomarkers are biological indicators with high diagnostic and prognostic value, indicating various stages of periodontitis and providing help in its prevention and treatment . Matrix metalloproteinase-8 (MMP8) and interleukin (IL)-1beta, the most studied biomarkers in the periodontitis field, demonstrate convincing clinical diagnostic validity . One stuResearch into periodontitis diagnosis based on mRNA expression has certain limitations; most studies use single-marker screens, and classification models with multiple indicators have not yet been constructed . In addiIn this study, we combined RF and ANN to develop a multi-mRNA diagnostic model using Gene Expression Omnibus (GEO) periodontitis expression data  interdental clinical attachment loss detectable at ≥ 2 nonadjacent teeth or 2) buccal or oral clinical attachment loss with pocketing >3 mm detectable at ≥ 2 teeth . First, p-values < 0.05 as the screening criteria  between the periodontitis and control groups in the training set were identified using the “limma” R package, with |logFC| > 1.0 and criteria . DEGs wecriteria . The topWe employed the “randomForest” R package for further DEG screening . First, For the construction of the ANN diagnostic model, we first used the min–max method to normalize the input data, converting the expression data of 13 key biomarkers into gene scores. The biomarker expression level for each sample was compared to the median value of all samples. If the expression level of an upregulated gene was greater than the median value, its gene score was defined as one; otherwise, it was 0. Similarly, if the expression level of a downregulated gene was less than the median value, it was defined as 1. We then utilized the “neuralnet” R package to calculate the gene weight and establish the ANN classification model . The ANNCIBERSORT is a deconvolution algorithm quantifying cell types based on gene expression profiles and can assess the distribution of 22 immune cells in tissues . We usedBased on the expression of key biomarkers, we applied the “ConsensusClusterPlus” R package to perform unsupervised cluster analysis on the training cohort to identify potential molecular subtypes. The k-means algorithm with 1,000 iterations and an 80% resampling rate was used to guarantee classification stability. The t-distributed stochastic neighbor embedding (tSNE) analysis was utilized to verify the classification accuracy and visualized using the “ggplot2″ R package.p < 0.05 was considered statistically significant.The Wilcoxon signed-rank test was used to analyze differences in immune scores between the control and periodontitis groups. The differences between two molecular subgroups were also analyzed using the Wilcoxon signed-rank test. Spearman’s rank correlation coefficients were used to explore the correlation between periodontitis biomarker expression and immune cells or immune functions. Statistical analyses were performed using R. Unless otherwise stated, A flow chart of the study process is presented in We used GO and KEGG enrichment analyses to determine the functions of these genes. GO analysis revealed that the DEGs mainly regulated immune-related functions such as phagocytosis, humoral immune response, activation of the immune response, and neutrophil migration . MeanwhiTo screen for reliable diagnostic biomarkers of periodontitis, we entered the DEG gene scores into the RF model. According to the relationship between the RF tree number and the model error rate, we chose the tree number corresponding to the lowest error rate n = 34; . Figure We used ANN to analyze the weights of the 13 biomarkers based on gene scores. The ANN diagnostic model consisted of 13 input, five hidden, and two output parameters . The weiWe used CIBERSORT to assess the distribution of 22 immune cells, and plasma cells were the main cell type in periodontitis samples . The relWe employed ssGSEA to calculate immune function enrichment scores . The enrPatients with periodontitis were classified using consensus clustering to gain better insight into the roles of biomarkers in disease development. The results suggested that, when the number of clusters (k) was 2, the periodontitis samples in the consensus matrix obtained the best clustering, with the highest intra-omic correlation and the least inter-omic interference . TherefoPeriodontitis is not only a common cause of severe tooth loss but also a driver and direct manifestation of several diseases, such as diabetes, cancer, cardiovascular disease, and rheumatoid disease . Early dPapantonopoulos et al. first applied an ANN algorithm to develop a classification model for chronic and aggressive periodontitis based on immune parameters . Shimpi The present study first identified 153 DEGs between periodontitis and healthy samples from the GEO dataset. Gene enrichment analysis showed that these genes were mainly involved in phagocytosis, humoral immune response, immune response activation, neutrophil migration, cytokine interaction, cell adhesion molecules, and the IL-17 signaling pathway. These results suggested that the DEGs are actively involved in inflammatory processes in periodontitis and may be critical for its development. The RF classifier screened 34 potential markers and obtained 13 key periodontitis biomarkers. We found that these key markers were associated with periodontitis, immune cells, or apoptosis. CSF3, ICAM2, and MMP7 serve as diagnostic and prognostic biomarkers for periodontitis . C3, C4ACRCR4, CXCL1, and SAA1 regulate immune cell distribution and induce inflammatory cell recruitment in periodontal diseases . The macA growing body of studies has shown that different immune cell infiltration is seen in the periodontitis inflammatory microenvironment, which is crucial for its occurrence, development, and treatment . We founIn the present study, we applied an innovative combination of RF and ANN algorithms to the early diagnosis of periodontitis, demonstrating excellent diagnostic performance in a large cohort. Although DNA methylation biomarkers and miRNA-based models for periodontitis classification have been developed, these studies used small sample sizes and showed modest performance . In addiThe present study also had several limitations. First, the model input data was provided by gingival tissue samples, and tissue acquisition presents a challenge in clinical practice. Point-of-care testing platforms and microfluidic technology may help identify mRNA signatures in biopsy samples. Second, mechanistic explanations of the correlations between some biomarkers and periodontitis are lacking, and further experimental studies are necessary. Third, a lack of clinical information prevented further exploration of clusters A and B. Furthermore, cellular-level characterization is required to determine whether plasma cells were the predominant cell population in our findings. Finally, more independent patient cohorts should be used to evaluate the ANN classification model’s performance. Our next step is to collect patient tissue samples from an affiliated hospital for verification.In summary, we constructed a new periodontitis classification model using on machine learning algorithms, demonstrating satisfactory performance in an independent cohort. In addition, we comprehensively assessed the association of key biomarkers with immune infiltration. We believe that the diagnostic model and biomarkers discussed here may shed new light on the exploration of mechanisms and clinical diagnosis of periodontitis. However, it is noteworthy that further experimental studies and independent patient cohorts are warranted to validate the present results. , Feres , Nagash and Rav


Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes. For therapy selection, precise subtype identification and individualized prognosis are mandatory, but currently limited. Our aim was to refine subtyping and outcome prediction across main subtypes, assuming that a tumor is composed of molecular features present in distinct pathological subtypes.n = 864; ccRCC: 512; pRCC: 287; chRCC: 65) as well as 92 independent histopathologically well-characterized RCC. Predicted continuous subtypes were correlated to cancer-specific survival (CSS) in the TCGA cohort and validated in 242 independent RCC. Association with treatment-related progression-free survival (PFS) was studied in the JAVELIN Renal 101 (n = 726) and IMmotion151 trials (n = 823). CSS and PFS were analyzed using the Kaplan–Meier and Cox regression analysis.Individual RCC samples were modeled as linear combination of the main subtypes (clear cell (ccRCC), papillary (pRCC), chromophobe (chRCC)) using computational gene expression deconvolution. The new molecular subtyping was compared with histological classification of RCC using the Cancer Genome Atlas (TCGA) cohort (P = 4.1E − 10), pRCC (P = 6.5E − 10), chRCC (P = 8.6E − 06)) in TCGA. Translation into a numerical RCC-R(isk) score enabled prognosis in TCGA (P = 9.5E − 11). Survival modeling based on the RCC-R score compared to pathological categories was significantly improved (P = 3.6E − 11). The RCC-R score was validated in univariate  and multivariate analyses including clinicopathological factors . Heterogeneous PD-L1-positive RCC determined by molecular subtyping showed increased PFS with checkpoint inhibition versus sunitinib in the JAVELIN Renal 101  and IMmotion151 trials . The prediction of PFS significantly benefits from classification into heterogeneous and unambiguous subtypes in both cohorts (P = 0.013 and P = 0.032).One hundred seventy-four signature genes enabled reference-free molecular classification of individual RCC. We unambiguously assign tumors to either ccRCC, pRCC, or chRCC and uncover molecularly heterogeneous tumors , which are at risk of worse outcome. Assigned proportions of molecular subtype-features significantly correlated with CSS (ccRCC (Switching from categorical to continuous subtype classification across most frequent RCC subtypes enables outcome prediction and fosters personalized treatment strategies.The online version contains supplementary material available at 10.1186/s13073-022-01105-y. Renal cell carcinoma (RCC) is among the ten most diagnosed cancers worldwide and especially in Western countries its incidence is rising –4.RCC comprises histologically defined subtypes, which differ in pathophysiology, clinical course, response to treatment, and prognosis. Major subtypes are clear cell (ccRCC), papillary (pRCC), and chromophobe (chRCC) RCC , 5, whicUsing computational deconvolution and molecular features of the three main histologically defined subtypes of RCC , 5  . A pathological re-evaluation of the TCGA RCC cohort was obtained from Ricketts et al. [RCC cohort C3 Table  consisteu et al. . Somaticu et al.  were dows et al. . PatientCohort 4 (C4) included 92 independent cases  and was used to evaluate our method with expression data generated from FPPE tissues ; from the Department of Urology, University Hospital Carl Gustav Carus, Dresden, Germany (n = 44); from the Department of Pathology, Portuguese Oncology Institute of Porto (IPO Porto), Portugal (n = 27); and from the Department of Urology, University of Greifswald, Germany (n = 10). C5 was used to validate results from the survival analysis  Table  comprisen = 726) [n = 823) [Additionally, clinical and RNA-seq data from the JAVELIN Renal 101 trial (n = 726) , 34 and n = 823) , 36 weren = 823) . The IMmTranscriptomic data as provided by the Broad-Novartis Cancer Cell Line Encyclopedia (CCLE) , 38 as wwww.ebi.ac.uk/ega/home), which is hosted by the EBI and the CRG, is EGAS00001001176. Processing of publicly available transcriptome data of all other cohorts is described in supplementary data.Total RNA was isolated from fresh-frozen tissue of cohorts C1, C2, and C5 as previously described , 42. RNADetailed information of all statistical and bioinformatics methods is given in Additional file http://cran.r-project.org). GEOquery_2.46.15 [http://www.bioconductor.org, version 9). For Affymetrix microarrays, customized CDF files provided by brainarray [All statistical analyses were performed with R-3.6.1  includin_2.46.15 , limma_3_2.46.15 , oligo_1_2.46.15 , org.Hs._2.46.15 , pda.hta_2.46.15 , Rgraphv_2.46.15 , SCAN.UP_2.46.15 , Summari_2.46.15 , and TCG_2.46.15  are partainarray , 64 ).For the principal component analysis, FPKM-UQ and TPM expression values were log2-transformed  and PFS were conducted by Kaplan–Meier curves and corresponding log-rank tests as well as uni- and multivariate Cox models. Comparisons of Cox models were performed by analysis of deviance.P-value < 0.05. Where indicated, P-values were corrected for multiple testing applying Holm’s [All statistical tests were two-sided. Statistical significance was defined as g Holm’s  method.The Akaike information criterion (AIC) was used for model selection. It is an estimator of the relative amount of information lost by a given model, measuring the trade-off between model fit and model complexity. The preferred model is the one with minimum AIC value.Subtype-specific genes were determined using gene expression data from C1 as described in Additional file Samples from RCC tissue were considered as composite samples that may combine specific molecular features from ccRCC, pRCC, and chRCC. The proportional subtype assignment (PSA) was determined using gene expression deconvolution. In brief, the expression of the 174 signature genes in an RCC sample of interest was modeled as the weighted sum of expression of these genes in ccRCC, pRCC, and chRCC. Based on the weights identified by robust linear regression, the proportional composition of the sample was then calculated such that c + p + h = 100%, where c, p, and h represent the ccRCC, pRCC, and chRCC proportions, respectively  functions as well as with cubic polynomials in Cox proportional hazard regression. Linear predictors from Cox proportional hazard models were used as prognostic index (PI). The predictive accuracy of single subtype scores as well as their combination was compared by repeated tenfold cross-validation in cohort C3. For comparison, the pathological classification was evaluated as categorical predictor of survival. The ccRCC-score modeled via cubic polynomials, hereafter termed RCC-R score, showed the best trade-off between model complexity and predictive accuracy and consequently was selected as a biomarker for risk prediction  of the TCGA RCC cohort (C3) Table  includinNext, rather than categorizing a tumor, we intended to model its molecular characteristics through proportional subtype assignment (PSA). Therefore, transcriptomes of 864 RCC from RCC cohort C3 were deconvolved and PSA for each sample were computed  and the COSMIC Cell Lines Project (CCLP) confirmed unambiguously ACHN and CAL54 as cell lines with pRCC characteristics, whereas all other cell lines are classified as predominantly ccRCC Fig. : Fig. S5P = 0.0024, Fisher test). Among pRCC, tumors with mixed features were mostly of subtype 2, according to the previous WHO 2016 classification of renal cancer [Next, PSA and standard pathological categorization were compared using a recently published pathological re-evaluation of the TCGA RCC cohort  Fig. 3AA3A–C. Thl cancer . Notablyl cancer , were aml cancer .Fig. 3DiIn three cases, PSA and pathological classification differed significantly Fig. A–C. The Next, the amount of explained transcriptional variance in simulated populations of RCC based on cohort C3 was used as a measure to compare the information content in PSA and pathological classification   or by papillary features (n = 12) to demonstrate the reliability of PSA particularly for histologically challenging cases  and type 2 (n = 14) was not significant. In addition, PSA confirmed the original histopathological diagnosis of 23 mixed-type RCC with assigned proportions of molecular features of ccRCC and pRCC, confirming the prevalence of either clear cell or papillary features.Cohorts C1, C2, and C3 included only fresh-frozen tissue. Therefore, we assessed the applicability of PSA for gene expression data derived from FFPE tissue. As shown in Fig. ses Fig. B–D. PSA P = 4.1E − 10, log-rank test) and the pRCC-score  . In Fig. P = 0.85, chi-square test)  based on the RCC-R score is detailed in the “C5) was calculated. By means of the cutoffs learned in C3, 241 cases with C5 . Hence, calibration and discrimination of the RCC-R score model were similar in C5, indicating successful independent validation [C5 remained significant.The RCC-R score-based prediction of CSS was validated in an independent cohort C5) including 134 ccRCC, 86 pRCC, 16 chRCC, and 6 cases with known mixed subtypes  and the IMmotion151 trials (n = 823) through PSA using public available RNA-seq data. Molecularly heterogeneous tumors with ccRCC and pRCC features were uncovered in both cohorts  and in the IMmotion151 trial   and the IMmotion151 trials (P = 0.032).First, we investigated the tumors of patients in the JAVELIN Renal 101 n = 72 and the RCC is a heterogeneous disease thereby complicating reliable subtype identification based on histopathology alone. Subtype identification is crucial for treatment-related outcome prediction. Selection of therapeutic strategies including immunotherapy might be improved by incorporating molecular data as previously demonstrated for other cancer entities , 73. Of n = 92) including 48 pRCC cases, enabled valid classification of main and heterogeneous RCC subtypes. Here, no significant differences in pRCC proportions between type 1 and type 2 cases were found supporting the novel WHO classification. Finally, to consider misclassification due to the presence of non-tumor renal tissue, PSA was assessed using TCGA data from non-tumor tissues. PSA identified unambiguously non-tumor tissue that differed constantly in composition from the assignments found in tumor samples.In the present work, we developed a reference-free subtype classification system for individual RCC samples using gene expression data of 174 defined subtype-specific genes  and the COSMIC Cell Lines Project (CCLP) confirms that the underlying signature genes derived from bulk tumor tissues indeed enable classification of pure tumor cells, and PSA is not impaired by e.g. stroma or immune infiltration. Notably, results from the new PSA based on only 174 genes is in line with findings from complex genomic characterization of cell lines by Sinha and colleagues .Our computational approach of proportional assignment of molecular subtype features to individual RCC samples allows not only independent molecular classification, but the PSA-based RCC-R(isk) score enables also reliable prognosis and prediction of therapeutic outcome. Considering heterogeneity of RCC, of course, multiregion sequencing data for subtype prediction indicated some intratumor variability .


We have successfully demonstrated that although there are significant analytical challenges involved in the qualitative discrimination analysis of sub-mm sized (microfragment) glass samples, the task can be solved with very good accuracy and reliability with the multivariate chemometric evaluation of laser-induced breakdown spectroscopy (LIBS) data or in combination with pre-screening based on refractive index (RI) data. In total, 127 glass samples of four types  were involved in the tests. Four multivariate chemometric data evaluation methods  for LIBS data were evaluated with and without data compression . Classification tree and random forest methods were found to give the most consistent and most accurate results, with classifications/identifications correct in 92 to 99% of the cases for soda–lime glasses. The developed methods can be used in forensic analysis. Glass is a common material; it is widely used in the industry, such as in the construction , automotive , or optical and lighting industries , as well as in households  or in chemical and biological laboratories  and other fields. From this variety of uses and prevalence comes the importance of glass as evidence in forensic investigations. Splinters and glass microfragments (<1 mm in size) often can be found at the locations of car accidents, car thefts, hit-and-run accidents, vandalism, burglaries, assaults, bomb attacks, etc. These microfragments are generated during the fracture of glass, can occasionally fly more than three meters away from the source and can be trapped in the clothing, shoes or hair of a person present at the scene; thus, the fragments can link people to each other or to locations ,3. Thus During forensic analysis, glass fragments are examined in several ways. Typically this includes refractive index (RI) measurements at standardized wavelengths, often with temperature programming, and the quantitative analysis of trace/minor elements by solid sampling instrumental methods with spatial resolution such as electron microscopy with energy dispersive spectroscopy (SEM-EDX) ,5,6, micIt has already been well established in the analytical literature that RI measurements compared to multielemental sensing of minor and/or trace elements have a much smaller efficiency in the discrimination of glasses. This is due to the fact RI values only depend very weakly on the minor/trace glass composition, and since most glass applications  do not dictate the control of the RI, many glass types have similar refractive indices. At the same time, good results (85–99% accuracy) in glass discrimination or comparative analysis were obtained by using XRF, LA-ICP-MS or SEM-EDX trace elemental composition data alone or combined with RI data e.g., ,8,11,12),1211,12]Laser-induced breakdown spectroscopy (LIBS) is a laser-based atomic emission spectroscopy technique. A short-duration, high-intensity laser beam is focused onto the surface of the solid sample, which evaporates the material of the sample located in the focal spot. The ablated material is also atomized and excited by the laser pulse, which leads to the formation of a microplasma. The analytical information about the elemental content of the sample is gained from the observation of the plasma emission. LIBS has a far more practical list of features that can be potentially well exploited for forensic analysis of samples, including glass samples. It is similar to LA-ICP-MS in terms of minimal sample preparation and high spatial resolution (micrometer-range), but does not use a complicated vacuum system (in contrast to both SEM-EDX and LA-ICP-MS). It is built around a robust, compact (even field-portable), reasonably cost-efficient and fast-to-use instrumentation and can work with almost any shape and size of solid samples and operate under ambient conditions. As opposed to XRF and SEM-EDX, it is sensitive to all elements, including the light or hard-to-ionize ones, which allows the recording of fingerprint-like, feature-rich spectra of the sample that contain a plethora of information about all major, minor and trace elements (down to the ppm level) ,21,22. EIn a later study, Bridge et al.  examinedIn their chemometric study, McIntee et al. suggested the adoption of a non-parametric permutation test for the discrimination of glass samples via their LIBS spectra, pointing out that any deviation from the normal distribution when the basis of similarity is the Pearson correlation coefficient . El-DeftThus, the literature data so far suggest that LIBS has a good potential for forensic glass analysis, especially because it uses a cheaper, faster and easier examination technique. In our present study, we investigate the performance of LIBS for glass discrimination when the samples are microfragments (around or below 100 µm in size), where the homogeneity and laser ablation behavior of the samples are particularly important, because a large number of repetitive measurements are not possible. Our study focuses on the case of soda–lime glasses, because this represents the most widely used group of glass and therefore is also one of the most frequently studied types of forensic evidence. We paid particular attention to comparing and combining the discrimination performance of LIBS and RI. Because RI data are scalar numbers and LIBS spectra are large data vectors, their combination can not be efficiently done via data fusion . Instead, our approach was to use RI data for pre-screening.−1 , controlled by the mass flow controller of the LIBS instrument. Each sample was brought into focus by using the built-in dual (laser distance meter and camera driven) autofocus system of the J-200. If not indicated otherwise, 25 spectra of each glass sample were recorded (in 5 repetitions at 5 different locations).LIBS experiments were performed on a J-200 tandem LA/LIBS instrument  equipped with a 266 nm, 6 ns Nd:YAG laser source and six-channel CCD spectrometer with a spectral resolution of around 0.07 nm at the Department of Inorganic and Analytical Chemistry, University of Szeged. LIBS spectra were recorded in the Axiom instrumentation data acquisition software using a 1 μs gate delay and 1 ms gate width, in the wavelength range of 190 to 1040 nm. The smallest spot size available on the instrument, 40 μm, was used throughout the experiments. The pulse energy was 15 mJ (with a 2% RSD) and the pulse repetition rate was 10 Hz. The ablation chamber was rinsed by a 99.995% purity argon gas flow at a flow rate of 0.5 L·min−1. The RI value was determined using a temperature-matched calibration curve based on the measurement of 10 glass standard samples  measured on six different edges.Refractive index (RI) measurements were carried out at 589 nm by the oil immersion method using a GRIM 3 system  at the Hungarian Institute for Forensic Sciences. All glass microfragments were mounted on separate microscope slides, covered with a few drops of silicone oil , crushed with a scalpel and covered with a thin glass cover plate. The fragments were observed under a phase-contrast microscope as the temperature of the sample was ramped at a rate of 4 °C·minLaser ablation craters in the glass samples were investigated by using a Veeco Dektak 8 contact profilometer  available at the Department of Optics and Quantum Electronics, University of Szeged. The craters were fully mapped, not just line-scanned. The used stylus had a 2.5 μm radius of curvature and it was pressed against the sample by using 30 μN force. Recorded maps have a 330 nm lateral and 400 nm vertical stepping resolution.Most glass samples investigated in the present study were provided by the Hungarian Institute for Forensic Sciences. Glass standards  and some additional glass samples were supplied by the Department of Inorganic and Analytical Chemistry, University of Szeged. A total of 127 glass samples, representing four major glass types  were analyzed. Our study focuses on soda–lime glasses, hence the majority of the glass fragment samples (95 pieces) were of this type. Within the soda–lime group, float, container (beverage flask or jar), patterned and security  glasses were included. Since the majority of flat glasses are nowadays manufactured by the float method , float gOur sample preparation followed the routine utilized by the Hungarian Institute for Forensic Sciences. Samples were typically splinters or macro  sized fragments which, after cleaning, were later crushed into smaller pieces for the purposes of the study. Surface contaminations were removed by spectroscopy-grade acetone, followed by, in case of macro fragments, wiping dry using Kimwipes tissues . Finally, glass fragments were attached to plastic sample holder discs with double-sided adhesive tape.In this study, five well-established chemometric methods, namely linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), classification tree (CT), random forest (RF) and principal component analysis (PCA) were tested. Details of these methods are described in chemometric textbooks, such in ; thus, hPCA is one of the most commonly used data compression methods. It projects the measurements into a new space, where none of the variables are correlated, and aims to describe the data as best as possible. It is often used for the visualization of high-dimensional data and data preprocessing for other chemometric methods. LDA and QDA are commonly used for qualitative discrimination purposes. They are also considered data projection methods, but the projection is not used to describe the dataset, but to highlight the differences between existing groups. CT utilizes a so-called decision tree, whose construction consists of binary conditions (yes or no questions) and its purpose is to divide the data space along the existing variables into regions, and these regions are assigned to sample groups. The borders of the regions are determined in such a way as to minimize the misclassification rate. The results of this method are usually easy to interpret, because conditions can be directly assigned to physical parameters, such as wavelengths. The hierarchical tree structure, however, may make this method vulnerable to small changes in the data. Newer methods were developed to lessen this sensitivity and make CT more robust; one of the most successful of these is the random forest, which reduces the instability by repeatedly building decision trees, with the exclusion of randomly selected data points. The categorization of the unknown samples is decided by the majority vote of members of the forest .Data processing was carried out using version 18.0 of the Clarity software  and the open-source R Studio Desktop software package (v1.3). In the latter case, custom codes were developed using the Chemometrics, MASS, ALS, RPart and RandomForest packages.2 (silica), 13 m/m% Na2O (lime) and about 12 m/m% CaO (lime), plus one or more of up to fifty other compounds  as minor components added to affect the color, viscosity, durability, heat absorption or some other physical property. A survey of the elements in these additives, such as B, Al, Mn, Co, Fe, Cr, Li, K, Cu, Pb, Sn, rare earth elements and others, can therefore help the investigator to narrow down the manufacturing method, and thus the qualitative analysis of the host glass item of the fragment in a crime scene . The lack of elements, such as in fused silica, can also help the discrimination. Other elements, originating from the impurities of raw materials or from manufacturing tools, will also be present in glasses at trace (or minor) concentrations. These are characteristic of the particular batch or source of manufacturing and, therefore, their analysis is mainly useful for the comparison of individual glass fragments [There are some particular analytical challenges involved in the task of classification/discrimination of glass microparticles using LIBS-based sensing. One of these is that essentially the basis of discrimination is the elemental composition , which is quite similar for most glass types, especially for soda–lime glasses . The typragments ,33.A further aspect concerning the challenges of basing the discrimination on concentrations arises from the fact that there is a significant difference between the nanosecond laser ablation behavior of glass types. As is illustrated in It is easy to see that for a reliable qualitative analysis of microfragments, it is imperative that the glass composition is homogeneous. If it is not the case, then the subtle differences in composition can be effectively buried in lateral or depth variations in the (trace) elemental composition. Luckily, an experimental assessment of the dissimilarities in composition can be performed with LIBS on sub-millimeter glass fragments, as LIBS analysis has a spatial and depth resolution in the micrometer range . We inveRay plots in 3) for floating a sheet of glass (a density of 2–3 g/cm3) to make it smooth and flat—during this process, Sn diffuses into the glass [As expected, it was found that there is a reasonable similarity in the location-related LIBS spectra , which indicates a generally good lateral homogeneity. Depth-resolved comparisons reveal that elemental depth profiles are also typically flat, except for Sn, which shows a several times higher concentration in the top layer, which gradually decreases with the depth. This is the result of the float glass manufacturing process, which actually uses a bath of molten Sn , we could not test QDA directly, only with PCA. During data reduction, the number of principal components was optimized by scrutinizing the elbow point of scree plots. In the application of all methods, LIBS spectra of samples were separated randomly into training and validation (testing) sets, keeping the ratio of the number of spectra to around 7:3. Three repeated evaluations were carried out, during which the samples were randomly redistributed into training and validation sets. Since the LIBS data of all samples showed good repeatability , neitherIt is immediately apparent that all four methods provided decent cumulative accuracies in training, between 93% and 89%, and the repeatability of the classification was also very good at only a couple of percent. The reliability of the results  was also good, reflected by the closeness of accuracy values obtained for validation sets to those of training sets. Data compression was clearly found to be disadvantageous, as it decreased the accuracy by 5% to 10% for the three methods, for which results with uncompressed data could also be processed. This is probably due to the loss of information occurring during dimensionality reduction. The best, and actually identical, results were given by uncompressed RF and LDA methods (93.3%) for the training sets, but if the average of the results for a given method with training and validation data sets are considered, then RF slightly outperformed LDA (88.8% as opposed to 85.9%). The success of RF is not surprising as it is known to handle high dimensionality data very efficiently and is considered to be one of the highest accuracy classification methods. RF data evaluation of LIBS data has been found to be very efficient in the literature recently for a variety of sample types, from biological samples to ceramics and alloys to rocks ,40,42,43In the confusion matrices presented in The random forest method provides the user with information about the “relative importance” of every variable (based on the frequency of their occurrence and success rate in tree conditions). Since in our case the variables are atomic emission wavelengths and they are linked to elements, these importance values indicate how much the model relies on the intensities of certain spectral lines. High importance wavelengths here could be assigned to metals present in glass additives , such as 455.40 nm for Ba, 841.75 nm for K, 267.71 nm for Cr, 261.19 for Fe, 234.78 nm for Co and Fe, and 339.10 nm for Ni.The most information has to be mined from the experimental data during the qualitative analysis of individual glass samples, when an unknown sample is compared to a database of known samples. This situation is common and clearly highly important during the forensic examination of evidence, when examiners assess the probability of a fragment potentially originating from a known glass sample from the scene —or more commonly, how certainly it can be stated that two samples are not identical. The reason that the question is phrased like this is that in the case of such mass-produced and quite homogeneous samples such as glasses, the chance is higher that two samples are falsely identified as coming from the same sources . Present practices in forensic examinations handle this analytical task often by refractive index comparisons. Although the precision of RI data of glass samples is very good , we found that the reliability of such pairwise comparisons based solely on RI data is not sufficient. Only a small portion (4–32%) of samples belonging to the same soda–lime sub-type showed significantly different RI values.Because the glass sub-type classification proved to be quite accurate in In order to improve the accuracy of the identification of individual samples, we also carried out the test using combined RI and LIBS datasets. In the first step of this approach, we run a comparison of the RI data of the unknown sample against the RI data of all other soda–lime glass samples in the same sub-type. All RI data was represented in this test with a mean and a range set by ±3 times the standard deviation based on six repeated RI value determinations. Two samples were considered to not have significantly different RI values if the range of their RI values overlapped with each other. If the match was unique, then the identification was considered terminal and successful. Far more commonly, however, the match was not unique but involved several database samples (close hits). In these cases, as the second stage of identification, a chemometric evaluation of the LIBS data was carried out, but it only included those samples of the database (known samples) which were found not distinguishable by the RI values . We execThe performance of this approach is consistently very good; the accuracy is around or above 80% for all chemometric methods. The reliability and repeatability of the results are also excellent. Again, CT and RF methods gave the highest overall accuracies of 95.0% and 93.5%, respectively. It is a considerable improvement considering the ca. 20% and 80% accuracy of the RI and RF, respectively, when used alone. The average number of close RI hits was found to be around 3.5, but this number depends highly on sample quality. For example, the average number of close RI hits for SL/P glasses was found to be 2.5, while it was 4.2 for SL/F glasses. The highest number of close RI hits was found among the SL/F samples as well, where some of the samples were found to have an RI value that was nearly identical to that of eight other samples.We have successfully demonstrated that although there are quite a few analytical challenges involved in the forensic examination-oriented qualitative discrimination analysis of glass microfragment samples, the task can be solved with very good accuracy (90%+) and reliability with the multivariate chemometric evaluation of LIBS data, or alternatively in combination with a pre-screening based on RI data. The RI pre-screeing substantially raised the accuracy of the classification. On average, the improvement was 15%, but for certain methods, it reached 40%. The qualitative analysis can be successful even in the case of very small differences in glass quality, such as between the sub-types of common soda–lime glasses. Classification tree and random forest methods were found to give the most consistent, most accurate results. In several aspects, our study and the findings are more detailed than former studies on the qualitative LIBS analysis of glasses in the literature. These novel and improved aspects include the fact that we investigated far more samples (127) than any other studies, and uniquely focused on sub-mm sized glass fragments. We not only discussed the performance in glass type classification, but also the identification of individual glass samples by using classification methods. Apart from LIBS and RI data, the laser ablation behavior of different glass types and sub-types were also studied and described, including ablation crater volumes and laterally and depth-resolved element distribution.


In recent decades, various previous research has established empirical formulae or thermodynamic models for martensite start temperature (Ms) prediction. However, most of this research has mainly considered the effect of composition and ignored complex microstructural factors, such as morphology, that significantly affect Ms. The main limitation is that most microstructures cannot be digitized into numerical data. In order to solve this problem, a convolutional neural network model that can use both composition information and microstructure images as input was established for Ms prediction in a medium-Mn steel system in this research. Firstly, the database was established through experimenting. Then, the model was built and trained with the database. Finally, the performance of the model was systematically evaluated based on comparison with other, traditional AI models. It was proven that the new model provided in this research is more rational and accurate because it considers both composition and microstructural factors. In addition, because of the use of microstructure images for data augmentation, the deep learning had a low risk of overfitting. When the deep-learning strategy is used to deal with data that contains both numerical and image data types, obtaining the value matrix that contains interaction information of both numerical and image data through data preprocessing is probably a better approach than direct linking of the numerical data vector to the fully connected layer. Martensite start temperature (Ms) is a critical phase-transformation parameter for steel. This parameter could help to guide processing improvements in various advanced steels, such as third-generation advanced, high-strength steels ,2,3,4; uLinear empirical formulae are the most concise and commonly used methods for predicting Ms values. Various formulae for different alloy systems have been established, including the Andrews , Mahieu Therefore, with continued development of advanced steels, more powerful methods, such as machine learning, which can consider high-dimensional factors ,21,22,23To account for the effect of grain size on Ms in modeling, in previous studies, modifications were made to both the empirical formulae and the thermodynamic models. In 2007, Jimenez-Melero et al. ,27 modifIn this study, a deep learning (DL) strategy  was usedIn this study, a medium-Mn steel data set was established. The compositions of the samples in the data set are listed in −4. For all of the aforementioned modeling of the CNN model and other AI models, data preprocessing and model training were implemented using Keras (version_2.4.3) and Scikit-learn (version_1.1.1) in Python (version_3.8.8). PyCharm  was used as the programming software in this research.In this study, a CNN, many of which are widely used for classification and regression of images in the field of artificial intelligence (AI), was used for Ms prediction based on both composition and microstructure-image input. The framework used in this study is illustrated in 2) and mean absolute error (MAE) were used to evaluate the performances of different models.For the training and testing processes, 20% of the submatrices (4095 samples) in the data set were randomly selected as the validation set. The remaining submatrices were randomly divided into two parts 10 times, with 80%  and 20% (3276 samples) of the submatrices used as training and testing sets, respectively. Finally, the predicted Ms output was obtained via passing the data through the convolutional, pooling, and fully connected layers. After these Ms predictions were obtained, the coefficient of determination  was also lower than that of the CNN model (0.993).To explain the necessity of adding microstructure images as input, different traditional AI methods, including support vector regression (SVR), XGboost (XGB), random forest (RF), gradient boosting regression (GBR), and AdaBoost (Adb), were also used to predict Ms, with only composition and processing parameters as input. For the traditional AI methods, the data set with 32 samples was divided into training and testing sets at a ratio of 8:2. In summary, compared with the traditional AI methods, the CNN model trained with microstructure images showed advantages in terms of both prediction accuracy and stability. With our understanding of materials science taken into account, it is reasonable that the CNN model had better performance because it contained more critical factors related to microstructure. In addition, in the field of AI, the better performance of the CNN model in this study could also be explained as follows: In this study, the data set contained only 24 samples with different compositions or processing details; therefore, the data set belonged to the class of extremely small-sample problems. Therefore, it is understandable that even SVR or XGboost, which are typically good at handling small-sample problems, could not completely solve the problem of overfitting in this study. However, with the addition of microstructure subimages, the 24 samples were augmented to 20,475 samples with more information. Therefore, this data augmentation greatly relieved overfitting and helped fully utilize the performance advantages of the DL. It should also be noted that the performance of SVR or XGboost can be improved through addition of more samples into the database. However, more samples mean more fabrication and sample preparation. Usually, traditional AI methods, such as SVR or XGboost, need at least hundreds of samples. This will lead to extremely high time and funding costs. Therefore, instead of addition of samples to the database, the CNN model, which only needs 24 samples, can be used as a more efficient way. In addition, compared with other traditional models, the main advantages of this CNN model are the efficiency of not only the small sample requirement but also the way that it can easily add complex microstructure factors into the model.2 values of the testing and validation sets were slightly lower than that of the training set, both the gap and the error bars were small 2 values of the validation sets were <2 °C and >0.99, respectively. This overcame the limitation that microstructures could not be digitized into numerical data or considered as factors in most previous models.This CNN model made accurate predictions of the Ms values for medium-Mn steels. The MAE and R(2)This CNN model offers significantly better prediction accuracy and stability than do traditional AI methods, especially decreasing the risk of overfitting, because the data preprocessing step used in this study enables data augmentation through use of microstructure images.(3)When a DL strategy is used to deal with small-sample problems for different data types, such as Ms prediction, using data preprocessing to obtain the value matrix that contains the interaction information of both numerical and image data is probably a better approach than directly linking the numerical data vector to the fully connected layer.(4)Although this CNN model is a powerful method for adding complex microstructure factors, its expandability should be further evaluated in other issues with different databases.In order to fully consider the effect of microstructure on Ms, a CNN model with a specific data preprocessing approach was established for Ms prediction, and both composition and microstructure image information were used as input. The main conclusions thereof are as follows:


Systemic characterisation of the human faecal microbiome provides the opportunity to develop non-invasive approaches in the diagnosis of a major human disease. However, shared microbial signatures across different diseases make accurate diagnosis challenging in single-disease models. Herein, we present a machine-learning multi-class model using faecal metagenomic dataset of 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, colorectal adenomas, Crohn’s disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular disease, post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 microbial species derived from 14.3 terabytes of sequence. The trained model achieves an area under the receiver operating characteristic curve (AUROC) of 0.90 to 0.99  in predicting different diseases in the independent test set, with a sensitivity of 0.81 to 0.95  at a specificity of 0.76 to 0.98 (IQR 0.83–0.95). Metagenomic analysis from public datasets of 1,597 samples across different populations observes comparable predictions with AUROC of 0.69 to 0.91 (IQR 0.79–0.87). Correlation of the top 50 microbial species with disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease model has potential clinical application in disease diagnostics and treatment response monitoring and warrants further exploration. Here, using fecal metagenomics data of 2,320 individuals, the authors develop a microbiome-based machine learning approach showing high accuracy for multi-class disease diagnosis, highlighting its potential application in improving noninvasive diagnostics and monitor responses to therapy. The current development of microbial markers has mostly used binary classifiers5. Emerging evidence, however, suggests that most health conditions exhibit overlapping gut microbiome signatures6, thus single-disease diagnostic models are likely to be confounded by unrelated diseases and may lead to misclassification. Although an attempt has been made to develop a multi-class diagnostic model, heterogeneity, technical bias and batch effects involved in the previous work relying on public datasets for analyses would limit accuracy7. Here, we develop the largest single-site dataset to date covering multiple diseases, adopt a machine learning multi-class model to predict different diseases using species-level faecal microbiome profiling, and validate the findings using public metagenome datasets across different populations.Recent studies have shown that imbalanced intestinal microbiota, termed “dysbiosis”, contributes to various human diseasesn = 174), colorectal adenomas , Crohn’s disease , ulcerative colitis , irritable bowel syndrome , obesity (n = 148), cardiovascular disease , post-acute COVID-19 syndrome  and healthy controls (n = 893). In total, we obtained 14.3 terabytes of the sequence at an average depth of 6.15 gigabases for each metagenome and identified 1208 bacterial species. Amongst them, 325 bacterial species had a relative abundance higher than 0.15% and these species were present in over 5% of the subjects (Source Data).We performed metagenomic sequencing of faecal samples from 2320 Hong Kong Chinese . We found a total of 1061 significant associations between these nine phenotypes and 215 bacterial taxa at the species level (FDR < 0.05). Amongst the 215 species, more than 94% were significantly associated with two or more diseases, which is consistent with previous works that numerous signals are shared among different diseases9 . Whilst common microbial signatures were shared across diseases, these findings pointed to the presence of disease-specific microbial composition. However, it is unknown whether binary classifiers can capture these disease-specific signatures. Therefore, we tested the specificity of our trained binary models in unrelated diseases, and the results showed a high misdiagnosis rate  and richness (number of species) in different diseases, and we found that both indices vary across phenotypes , multi-layer perceptron (MLP), support vector machine (SVM), and graph convolutional neural network (GCN)) to classify different diseases using species-level data from the training set (70% samples with the same class proportions as the cohort) and presented their final performance from the withheld test set . We found that using our RF multi-class model there were high probabilities whereby prediction cannot be made as they failed the corresponding threshold for most subjects  and a significant increase in Bacteroidetes (FDR < 0.05). Imbalance in Firmicutes/Bacteroidetes ratio had previously been reported primarily in patients with obesity and IBD14, but its associations with other diseases have not been reported. Nonetheless, such shared microbial signatures may serve as a basis for distinguishing health and disease. Then, we identified specific microbial signatures that can classify different diseases . All subjects provided written informed consent.17  of over 28 and with no other medical co-morbidities. Subjects with cardiovascular disease (CVD) were recruited from the public as part of a survey of cardiovascular health in the Hong Kong general population. Subjects underwent carotid ultrasounds to measure intima-media thickness (IMT) of the common, internal, and external carotid arteries  and carotid bulbs and subjects that had ≥50% stenosis in a single or multiple vessels were regarded as having the risk of CVD. Subjects with post-acute covid-19 syndrome (PACS) were defined as those with at least one persistent symptom or long-term complications of SARS-CoV-2 infection beyond 4 weeks from the viral clearance which could not be explained by an alternative diagnosis, and we assessed the presence of the 30 most commonly reported symptoms post-COVID after illness onsetHealthy controls were recruited during the same recruitment period from the community through advertisement and from the endoscopy centre at the Prince of Wales Hospital and included subjects who had a normal colonoscopy . The exclusion criteria for healthy controls were known complex infections or sepsis; known history of severe organ failure ; bowel surgery in the last 6 months ; the presence of an ileostomy/stoma; and current pregnancy; any long term drugs for chronic diseases; the use of antibiotics in the last 3 months; the use of laxatives or anti-diarrhoeal drugs in the last 3 months or recent dietary changes . Finally, a total of 2320 subjects were recruited. Clinical metadata and dietary data were collected during clinical interviews. Besides, an additional 60 subjects  were prospectively followed up for up to two years after the COVID-19 infection and were confirmed to have fully recovered from the initial infection without any symptoms of PACS. These subjects served as an independent validation cohort and provided serial faecal samples after SARS-CoV-2 clearance.18.Faecal samples were collected at home by all subjects using tubes prepared by investigators containing preservative media . The Norgen preservative can preserve and allow safe transportation of microbial DNA & RNA at ambient temperature eliminating sample variability. The stool sample was sent to the hospital within 24 h of collection and stored at −80 °C refrigerators until further processing. We have previously shown that data on gut microbiota composition generated from faecal samples collected using this preservative medium was comparable to data obtained from fresh samples that were immediately stored at −80 °CAfter removing the preservative media, microbial DNA was isolated with the Qiagen  QIAamp DNA Stool Mini Kit, according to the manufacturer’s instructions. After the quality control procedures by Qubit 2.0, agarose gel electrophoresis, and Agilent 2100, extracted DNA was subject to DNA libraries construction, completed through the processes of end repairing, adding A to tails, purification and PCR amplification, using Nextera DNA Flex Library Preparation kit . Libraries were subsequently sequenced on our in-house sequencer Illumina NextSeq 550 (150 base pairs paired-end) at the Center for Microbiota Research, The Chinese University of Hong Kong. All samples were in random order for DNA extraction, library construction and sequencing. ZymoBIOMICS Spike-in Control I  and ZymoBIOMICS Microbial Community DNA Standard (Cat: D6306-A) were used as positive controls during DNA extraction, library construction, sequencing and quality assessment.Raw sequence data were quality filtered using Trimmomatic V.39 to remove the adaptor, low-quality sequences , and reads shorter than 50 base pairs. Contaminating human reads were filtered using Kneaddata  with default parameters. Following this, microbiota composition profiles were inferred from quality-filtered forward reads using MetaPhlAn3 version 3.0.14. GNU parallel (v2018) was used for parallel analysis jobs to accelerate data processing. Species whose average abundance and prevalence were <0.15% and 5% were filtered out. Alpha diversity metrics  were calculated by using the phyloseq package (v1.26.0).https://github.com/kassambara/ggpubr) performed nonparametric statistical testing between groups and accounted for multiple hypothesis testing corrections when necessary. Principal coordinates analysis (PCoA) based on beta-diversity  was used to visualise the clustering of samples based on their species-level compositional profiles. The microbiome composition differences between different phenotypes were calculated by permutational multivariate analysis of variance (PERMANOVA) using distance matrices (adonis) in the adonis function of vegan R package V.2.5–7 with 999 permutations. Associations of specific microbial species with phenotypes were identified using the multivariate analysis by linear models (MaAsLin2) statistical frameworks implemented in the Huttenhower Lab Galaxy instance  with healthy controls as reference. The linear model also included age, sex and technical factors  to further correct for potential batch effects and confounders. BMI was not included as apart from the obese group, all subjects from other disease groups had a normal BMI requirement (18.5–22.9) and there was no difference in the BMI across different phenotypes. Benjamini–Hochberg correction was used to control for multiple testing, and results were considered significant at false discovery rate (FDR) < 0.05.All statistical analyses were done using R version 4.0.3. The ggpubr package  and then were applied to the withheld validation set (30%) to access the final performance. This process was repeated 20 times to obtain a distribution of random forest prediction evaluations on the validation set, and the mean AUROC value was calculated accordingly for the visualisation of and reporting of results.To account for sequencing batch effects for all samples processed in different periods, we performed Combat by SVA package (v3.44.0)n = 1724) and a test set for independent evaluation  with balancing class proportions across the cohort, training set and test set. Random forests (RF), K-nearest neighbours (KNN), SVM multi-layer perceptron (MLP) and support vector machine (SVM) were used as classifier models for the diagnosis of different phenotypes by using taxonomic profiles at the species level of the faecal microbiome. We implemented the RF multi-class classifier with the following modifications to the default SciKit-learn settings: n_estimaters = 2000 and class_weight = balanced. KNN, SVM and MLP were implemented from SciKit-learn with the default settings. Besides, we reconstructed a graph convolutional neural network (GCN) model based on a published work with the same parameter settings7. A nested cross-validation procedure was applied to calculate within-training set accuracy by splitting data into training and test sets for 20-times repeated, fivefold-stratified cross-validation . The optimal models selected based on cross-validated results were evaluated in the withheld evaluation dataset as the final performance for predicting different diseases. This process was repeated 20 times to obtain a distribution of random forest prediction evaluations on the validation set, and the mean AUROC and AUPR value was calculated accordingly for the visualisation of results. The highly ranked and frequently selected microbial features were considered predictive signatures for further interpretation. We retrieved prediction performance using the same training datasets.Multi-class models are implemented by Python 3.6.7 using standard libraries that are publicly available: pandas (0.23.4), numpy (1.14.5), scikit-learn (1.1), and matplotlib (2.2.3). For each phenotype, samples were randomly divided into a training set  and recall (or sensitivity) with a baseline that equals the proportion of positive disease cases in all samples.23, 102 for CD24, 218 for CVD25, 177 for CRC28, 86 for adenoma28, 83 for IBS-D31, 81 for Obesity32, and corresponding 736 health controls). Besides, a total of 60 raw shotgun faecal metagenomes from subjects with liver cirrhosis33  or constipation-dominant IBS31  were downloaded to construct an unrelated disease dataset. After downloading, the quality filtration and species-level taxonomic profiling were performed according to the above process.For the construction of the external dataset, a total of 1,597 raw shotgun faecal metagenomes were acquired from 12 independently published studies across 11 countries (114 for UCn = 1724) and a test set for independent evaluation . Within the training set, a nested cross-validation procedure was applied to calculate within-training set accuracy by randomly splitting data into training and test sets for 20-times repeated, fivefold-stratified cross-validation . The study did not include any interventions and thus the conventional blinding  was not relevant to this study.No statistical method was used to predetermine the sample size. Instead, the study focused on obtaining the largest possible sample size to capture the highest performance of the machine learning multi-class model. 2,320 samples were all successfully sequenced and passed the quality assessment (read depth > 10 million), thus no data were excluded from the analyses. For the machine learning multi-class model, a nested cross-validation procedure was applied to calculate within-training set accuracy by splitting data into training and test sets for 20-times repeated, fivefold-stratified cross-validation . The optimal models selected based on cross-validated results were evaluated in the withheld evaluation dataset as the final performance for predicting different diseases. This process was repeated 20 times to obtain a distribution of random forest prediction evaluations on the validation set, and the mean AUROC and AUPR value was calculated accordingly for the visualisation of results. For each phenotype, samples were randomly divided into a training set (70% of samples, total Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary