F-measure. It is feasible for BERT to classify the genomic mutation text within literature-based datasets. Consequently, BERT is a practical tool for facilitating and significantly speeding up cancer research towards tumor progression, diagnosis, and the design of more precise and effective treatments.The identification of profiled cancer-related genes plays an essential role in cancer diagnosis and treatment. Based on literature research, the classification of genetic mutations continues to be done manually nowadays. Manual classification of genetic mutations is pathologist-dependent, subjective, and time-consuming. To improve the accuracy of clinical interpretation, scientists have proposed computational-based approaches for automatic analysis of mutations with the advent of next-generation sequencing technologies. Nevertheless, some challenges, such as multiple classifications, the complexity of texts, redundant descriptions, and inconsistent interpretation, have limited the development of algorithms. To overcome these difficulties, we have adapted a deep learning method named Bidirectional Encoder Representations from Transformers (BERT) to classify genetic mutations based on text evidence from an annotated database. During the training, three challenging features such as the extreme length of texts, biased data presentation, and high repeatability were addressed. Finally, the BERT+abstract demonstrates satisfactory results with 0.80 logarithmic loss, 0.6837 recall, and 0.705  Nowadays, genomic, transcriptomic, and epigenomic studies have been benefited from the development of inexpensive next-generation sequencing technologies, which play essential roles in exploring tumor biology –3. TumorDespite the achievements traditional ML methods have made, potential drawbacks such as low accuracy exist when they are applied on clinical text classification. In 2018, Google proposed that the BERT method achieved state-of-the-art results in 11 projects, including text classification . DescripTo overcome these difficulties, deep learning (DL) has emerged to handle large-scale and complex datasets since its performance increases with the enlargement of datasets –34. For F-measure scores.Hence, we fine-tune the BERT model to classify mutation effects (9 classes) using an expert-annotated oncology knowledge base. Our BERT method is developed based on the original BERT model and is capable of obtaining different syntactic and semantic information. Three main characters of training datasets including extreme length of text entry, data imbalance, and repetitive description are engineered during training challenges. We propose three truncation methods including abstract+head, head only, and head+tail to deal with extreme length of text entry and repetitive description. Besides, data imbalance is relieved by negative sampling. Overall, we improve the BERT method to classify complex clinical texts, and obtain 0.8074 logarithmic loss, 0.6837 recall, and 0.705 The treatment of cancer is closely related to the identification of mutant genes . At presIn recent years, with the rise of artificial intelligence, natural language processing, which uses linguistics, computers, mathematics, and other scientific methods to communicate between human beings and computers, has developed rapidly –46. AmonD = {d1, d2, ⋯dn} refers to the set of texts classified, where the ith classified text is represented by di, and n is the number of classified texts. The collection C = {c1, c2, ⋯, cm} is a collection of predefined classification categories, where the jth category is represented by cj, and m is the number of predefined categories. F is a function representing a mapping relationship.In the formula, the collection K-nearest Neighbor method (KNN) [Currently, the most common methods for text classification are statistical ML and DL-based methods. Statistical ML methods usually preprocess texts in the first place, then manually extract high-dimensional sparse features. Consequently, they use statistical ML algorithms to obtain classification results. In 1998, Joachims first employed support for vector machine (SVM) in text classification and achieved favorable results . In the od (KNN) , decisiood (KNN) , boostinod (KNN) , and Ligod (KNN) . Among tod (KNN) . AlthougAlthough the traditional statistical ML models can classify texts faster than the manual method, they require manual feature extraction, which leads to a large amount of labor cost and is difficult to obtain effective features –63. On tThe text description of the individual sample shows considered lengths. There are differences in text lengths between different categories of samples. Some categories contain shorter words, while others contain miscellaneous descriptions. Generally, texts in a dataset range from hundreds to thousands of words in length. However, the lengths of the gene mutation in this paper are much longer than usual. We use the BERT method to truncate texts and extract valuable information in the texts using different methods, thus avoiding adverse impacts of excessive differences in text lengths on the results.There is a deviation of total gene number in all categories. Individual genes are unevenly distributed in different categories. Some genes belong to five or more groups, while others only present in two categories. To solve the vast differences in the number of samples between different categories in the dataset, we choose an undersampled data processing method to balance the data deviations between different categories.The whole dataset has a high repeated description. Different examples belong to different categories share the same text entry. Some categories show a high correlation, which may lead to low accuracy. To solve this problem, we improve the BERT model and splice the last three layers of the initial model, which increases the accuracy of the model and reduces the running time.To a certain extent, we illustrate the effectiveness of using DL in the classification of genetic clinical texts. As the data set increases, the DL model represented by BERT will learn the characteristics of the sample better to achieve exceptional results. In the future, DL models will have better performances on similar tasks.Hence, we propose an improved BERT model with high classification accuracy after analyzing the MSKCC mutation gene interpretation database thoroughly. We believe this method can be successfully applied to genetic mutation classification. The main contributions of our work are summarized as follows:Textual descriptions of individual samples exhibit considerable lengths. The text lengths among different classes show variabilities. Some of the classes contain shorter words while other classes contain redundant descriptions.The overall gene numbers presented among the whole classes show biases. The distribution of individual genes in different classes is unequal. Some genes belong to five classes or more, and some of the genes only fit in two classes.High repetitive descriptions exist in the whole datasets. Different samples belong to different classes that share the same text entry. Classes demonstrate high correlations.MSKCC sponsored the training and test datasets in this study for method development and validation. For the past several years, world-class experts have created a clinical evidence annotated precision oncology knowledge database. The annotations contain information about which genes are oncology clinically actionable. We sum up three characteristics of the MSKCC datasets mentioned below:It is reasonable to analyze the length of the entry text as a prior task for textual-based classification. We find that extremely long descriptions with massive irrelevant information are correlated with samples . We plotAnalyzing the composition of datasets can help us construct algorithms at an early stage. We sum up the frequency of genes among 9 classes . The 9 cWe also examin the interactions among different features within target classes. To reduce calculations, we select the top 20 gene types to illustrate the interrelations instead of the whole gene types . SelecteWe further explore the distribution of individual genes within classes, which demonstrates inequitable distributions. For instance, genes such as CDKN2A, PTEN, and TSC2 only present in a limited number of classes (lesser than three). In contrast, BRCA1, ERBB2, FGFR2, and RET are possessed in the majority of classes. Compared with genes only present in a few groups, genes that spread among classes are generally difficult to classify because elaborate texture descriptions can blur the classification standard. Hence, the accuracy of classifications is dependent on the gene compositions. Commonly, genes distributed in lesser classes can show more satisfactory results.Δ1751, may belong to different classes, but their descriptions are close, even in the same sentence. Similarly, two mutations of EGFR, such as Del 19 and L858R, also show in pairs , precision (PRE), F1 score, receiver operating characteristic (ROC) curve, and confusion matrix. True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) can be used to calculate some of the indicators mentioned above. TP is the number of categories that are correctly predicted. TN is the number of categories that are correctly predicted as another class. FP is the number of categories that are wrongly predicted. FN is the number of categories that are wrongly predicted as another class.M is the number of samples and N is the number of classifications. ymn is the predicted result of classification, such as 0 and 1. p (ymn) is the predicted probability of ymn.In multiclassification tasks, Logloss is one of the most common loss functions, where the predicted input is a probability value distribution between 0 and 1, and it can be defined as follows:PRE defines the proportion of genes identified correctly belonging to this type of mutation:REC calculates the proportion of genes identified correctly belonging to this type of mutation in all this type of gene:F1 score takes into account the factors of PRE and REC. F1 is the standard metric for this task. It combines precision and recall. Macro-F1 is a parameter index that can best reflect the effectiveness and stability of the model:The ROC curve is created by plotting the TP against the FP at various threshold settings.The confusion matrix is a specific table capable of visualizing the performance of an algorithm. Individual rows of the matrix represent the predicted gene classses, while each column represents the genes in the actual classes.e − 5, and warmup period 0.06; the whole experiment runs for 30 cycles; the maximum sequence length of BERT input is 512; and the optimizer is Adam optimizer, while other model parameters remain unchanged.For easier comparison with other methods, our training process uses the GPU of the server in the lab for training. There are 3136 training sets and 553 verification sets in total. The Python language is selected as the programming language in this experiment. The experiment is completed on Tensorflow's open-source framework and BERT-base. We use the parameters on BERT-base trained by Google through a large number of corpus on Wikipedia as pretraining parameters to accelerate the convergence speed and reduce the convergence difficulty. Our experimental parameters are batch size 128, learning rate 3The BERT model can automatically complete the process of converting each word in the text into a one-dimensional vector by querying the word vector table and inputting it in the model. The input of the model contains three sections: the token embeddings, the segmentation embeddings, and position embeddings.Because BERT is a pretraining model with high generalization ability, the output layer of BERT can be externally connected with corresponding layers to complete downstream tasks. For example, in this experiment, the processed data is substituted into the BERT model for training, and the output layer will connect Softmax function for classification tasks .BERT is an unsupervised model that uses whether the sentences are related to each other as labels and masks some words to make the masked words as labels, thus avoiding the tedious process of manually labeling data. Generally, the data in the dataset are not balanced. Take the samples in the 7th and 8th categories of the dataset as an example. The difference between their numbers even reaches more than 10 times. In this case, the default classification method makes classifiers pay too much attention to the category with a larger number of samples, thus making the generalization ability of the model weak and unable to obtain satisfactory results. Therefore, we use random sampling to eliminate the imbalance between data and extract only a part of samples from the category within a larger number of samples to balance the sample number differences between classes.Simultaneously, because the length of the gene text in the dataset is greater than 512 tokens, which is the longest length that can be retained by BERT, we need to use the truncation method to intercept part of the information in the text. We take three ways to solve this problem. The head only truncation method intercepts the first 512 tokens (at most) as input, the head+tail method intercepts part of the head and part of the tail to form 512 tokens (at most) as input, and the abstract+head method sorts the gene text according to importance, then select the most important 512 tokens (at most) as input.Finally, the processed data are substituted into the BERT model for training. Numerous previous works have shown that fine-tuning a pretrained model which has been trained with a large amount of corpus can significantly improve the classification result. As BERT can learn different contents in different layers, stitching some of the layers together can make the model get richer information, thereby improving the accuracy of the model, so the last three layers in the BERT model are concatenated. Max pooling, fully connected, and Softmax function are added after the concatenated output layer to realize the classification of gene text to improve the classification accuracy of the model.It can be seen from the figure that compared with the LightGBM method, the BERT methods using three types of truncation have higher ACC, REC, and F1 score. The confusion matrix shows our classification situation in a visual way . The redF-measure score is limited by the extreme shortage of training data. Better performance should be obtained when it is applied to large-scale datasets.The performance and ranking of the entries for the proposed four methods are shown in Besides, the ROC curves of the other three methods are below the ROC curve of the BERT+abstract . The ROCMoreover, we compare confusion matrix tables using predict classes versus true classes among different methods . The conIt can be observed that class 1 is easy to be confused with classes 4 and 5. Furthermore, there are more machine judgment errors of texts between type 7 and type 2. These phenomena can be easily attributed to the similarity of texts among these classes as we previously described. Also, it is apparent that classifying classes 8 and 9 is complicated. The computer may misjudge mutation texts with real labels of 8 or 9 as other types but hardly underestimate other types of mutation texts as type 8 or 9 since there are fewer samples in classes 8 and 9. The shortage of samples in classes 8 and 9 also fails to provide sufficient data to distinguish themselves from other classes since there are no intersections. Contrastingly, the classification of class 7 is easier due to the abundant samples. Therefore, the abundance of data plays essential roles in improving the efficiency of classification.In this study, we propose a deep learning algorithm to identify genomic information within texture-based literature abstracts. Aiming to address the classification problem in an extremely long, imbalanced, and repetitive dataset, we test four methods, including LightGBM and three different truncation BERT methods. By analyzing their Logloss, recall, F1 score, ROC curve, and AUC scores, we notice that the abstract+head truncation BERT method has superior results than other algorithms in all indicators.In this study, our BERT method is limited due to the shortage of datasets, and its performance can be improved dramatically with the size of datasets increasing. Moreover, our approach will be potentially applied on diagnosing and treating more than 120,000 patients every year around the world based on the announcement of the MSKCC, which will provide our opportunity to enhance our methods further when large-scale datasets are available. We believe BERT is a promising tool for accelerating tumor genomic-related research and facilitating tumor diagnosis and treatments. Besides, this text-based classifier algorithm demonstrated high universality, and it is applicable not only in tumor-specific research but also in other types of diseases and in other nonacademic areas.


Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model.GSE131418. The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and CatBoost, for classification of CAD with liver metastasis samples were compared using the MCC dataset GSE131418 by detecting their classification test accuracy. In addition, the consortium datasets of GSE131418 and GSE81558 were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug‐targeted network construction of the feature genes when applying the optimal method were conducted.The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset The optimal CatBoost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a “steroid metabolic process” and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathways . In a drug‐target interaction network, F2 and F9 were predicted as targets of menadione.The CatBoost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis. APOB, APOC3, FGA, F2, F9, and NKX2‐3 were potential biomarkers for classification of CAD with liver metastasis. Menadione might be a promising anti‐metastatic drug of CAD cells through functioning its role at sites of F2 and F9. CatBoost model constructed by 33 feature genes showed the optimal classification performance for identifying CAD liver metastasis. A new method for analyzing the transcriptomic differences between primary CAD and a distant metastasis was developed, and FBN2 and MMP3 were identified as CAD metastasis related genes, which may help predict a high‐level risk of CAD metastasis.et al have found that the random forest (RF) has the highest specificity, the Naive Bayes (NB) has highest sensitivity while the traditional machine learning approaches [logistic regression (LR) and linear discriminant analysis] had the highest total accuracy for metastasis prediction in breast cancer.Machine learning a computer‐based algorithm, has shown high degrees of accuracy and prediction that exceeds the abilities of standard statistical methods to make predictions about outcomes in patients.Although machine learning techniques comparisons are widely studied in cancer metastasis such as breast cancer and nonsmall cell lung cancer,22.1GSE131418 and GSE81558, downloaded from Gene Expression Omnibus (GEO) were used in this study. The Moffitt Cancer Center (MCC) dataset GSE131418 was used as the training set, whereas the consortium datasets GSE131418 and GSE81558 were used as internal and external validation sets for the liver metastasis models respectively. GSE131418 includes 333 CAD and 184 liver metastasis samples from the MCC cohort dataset, and 545 primary CAD and 73 liver metastasis samples from a consortium cohort dataset. The transcriptomic data of GSE131418 were generated from the GPL15048 Rosetta/Merck Human RSTA Custom Affymetrix 2.0 microarray platform [HuRSTA_2a520709.CDF]. In addition, a total of 23 primary CAD and 19 liver metastasis samples were analyzed from GSE81558. The sequencing platform of GSE81558 was the GPL15207 [PrimeView] Affymetrix Human Gene Expression Array.Two datasets, 2.2GSE81558, the expression data of the downloaded Series Matrix File(s) were standardized using the robust multiarray average (RMA) algorithm. Subsequently, a principal component analysis (PCA) was conducted to observe the sample grouping by the FactoMineR package in R.Before data preprocessing, GSE131418_RAW.tar was downloaded from GEO using the GEOquery package.2.3P‐value < 0.05 and |log2 fold change (FC)|> 2. In addition, the ggscatter function of the ggpubr package in R was used to draw a volcano plot of the DEGs, and the gene symbols of the top‐30 DEGs ranked by |log2 FC| were presented. The pheatmap package in R was applied to conduct the hierarchical clustering.A modified t‐test applying an empirical Bayesian method was applied to conduct mRNA transcriptomic differences between the primary CAD and CAD with liver metastasis groups. The DEGs were then identified under 2.4The count data of the DEGs were transformed into log2(x + 1) formatted data, and a binary label value of “1” was used for classifying the liver metastasis samples, and a value of “0” was used for classifying the nonmetastasis samples. For each group, 80% of the samples were divided into a training set using the train_test_split machine learning method in Python (version 0.21.2),Before the model construction, the recursive feature elimination (RFE) algorithm based on the sklearn.feature_selection method was applied to the feature selection. Machine learning models, ie LR, based on the sklearn.linear_model; RF and GBDT, based on sklearn.ensemble; an SVM, based on sklearn.svm; and NN, based on sklearn.neural_network, were constructed Data . Another2.5GSE131418 and GSE81558 were used as the internal and external validation sets for the predicted models above respectively. First, the feature DEGs were input into the six well‐trained or constructed liver metastasis models described above. The expression values of the feature DEGs in the samples were utilized as an eigenvalue to classify and identify CAD samples with or without liver metastasis. The risk of liver metastasis in the samples of the validation sets was predicted by assessing the accuracy and AUC values, which were used to evaluate the prediction and classification capability of the six models.The consortium datasets Following the construction and data validation of the six models, the model with the highest AUC value in both the training and validation sets was screened as the optimal model. The feature genes in the optimal model were chosen for the following analysis.2.6P value < 0.05 and count ≥ 2.Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database offering a biological interpretation of the genome function through KEGG pathway mapping, and links genomic information to more ordered information of biological functions.2.7http://www.string‐db.org/) was used to predict interactions of the feature genes with a PPI score of 0.4 (medium confidence), disable structural previews inside network bubbles, and hide disconnected nodes in the network. The pairs obtained were then visualized in a PPI network using Cytoscape software.The Search Tool for the Retrieval of Interacting Genes (STRING) database provides available sources on protein‐protein associations for 5,090 organisms.2.8www.dgidb.org) was used to predict the small drug molecules that interact with the feature genes. The drug‐target interactions reported in previous papers and by the FDA were then obtained. Finally, the drug‐target interaction network was constructed using Cytoscape.The drug‐gene interaction database is an open resource used to excavate information of a drug‐gene interaction and druggable genome.33.1GSE131418 and GSE81558 are shown in Table GSE131418 and GSE81558 respectively. After annotation, a total of 47 408 probes involved in 24 495 genes were obtained from the GSE131418 dataset, and a set of 46 879 probes related to 18 835 genes were obtained from the GSE81558 dataset.Information regarding the total number of samples, the probes, the annotated probes, and the gene symbols of GSE81558 , CC of “blood microparticle” (GO:0 072 562), and MF of “steroid binding”   to create classifiers for CAD classification with liver metastasis samples. The optimal model with the highest accuracy (99%) and AUC (1) based on the CatBoost algorithm was screened, and consists of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a lipid metabolic process such as the “steroid metabolic process” (eg APOB and APOC3) and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathway , revealing the potential biomarkers and pathogenesis of CAD with liver metastasis.According to the comparisons among six classification algorithms, the optimal model CatBoost was achieved for identifying CAD liver metastases with higher classification performance in training set and best reproducibility in validation set. Although NN classier showed a higher or equal classification performance in training, internal and external validation sets than CatBoost classier, the feature genes in this classier were consisted by all the DEGs (131), while there were 33 feature genes in CatBoost classier, which meant that the classification ability of 33 feature genes in CatBoost was almost equal to 131 feature genes in NN. The number of feature gene is also a critical parameter to evaluate the performance of classier, and a method with minimum number of feature genes for a classification problem with an objective function to maximize the classification accuracy is always needed.Except for the number of feature gene, the parameters of accuracy and AUC were also used to measure the performance of classier in this study. Accuracy may be interpreted as the proportion of instances the classifier always classify correctly for an given dataset or other data.CatBoost is a new developed algorithm based on GBDT algorithm that can successfully handle categorical features with advantage of reducing overfitting on available datasets, and outperforms traditional GBDT algorithm to overcome the gradient bias with ordered boosting.In addition, the underlying biological meaning of 33 feature genes in CatBoost classier was analyzed. In our study, we predicted that APOB and APOC3 were both upregulated in CAD liver metastasis samples, and associated with “steroid metabolic process” and “lipoprotein particle receptor binding”. Serum lipids are risk factors of numerous types of cancers, and has crucial roles in cancer metabolism.Coagulation factor II  or prothrombin has a pivotal role in maintaining the vascular integrity by regulating the thrombin using prothrombinase.NK2 Homeobox 3 (NKX2‐3) encodes a homeodomain‐containing transcription factor and as a member of the Nkx family is applied to determine the tissue differentiation.Although several feature genes for predicting CAD with liver metastasis were screened in this study, and a functional analysis and drug prediction of these genes were conducted, the experimental verifications of these findings remain lacking. Thus, future research is required.In conclusion, the CatBoost model showed the optimal classification performance in identifying CAD with liver metastasis. The feature genes in the CatBoost model, such as APOB, APOC3, FGA, F2, F9, and NKX2‐3 were demonstrated to be potential biomarkers for the classification and prediction of CAD with liver metastasis samples.5The authors declare that no conflicts of interest exist.All authors participated in the conception and design of the study; Conceived the manuscript: Han Shuwen and Yang Xi; Wrote the paper: Han Shuwen, Yang Xi, and Zhuang Jing; Processed the data: Wu Wei and Liu Jin; Drew figures: Zhuang Jing; All authors read and approved the paper.6Not Applicable.7Not applicable.8The datasets generated during the current study are not publicly available but obtained from corresponding authors on reasonable request.Figure S1.Click here for additional data file.Figure S2.Click here for additional data file.Figure S3.Click here for additional data file.Figure S4.Click here for additional data file.Data S1.Click here for additional data file.


Triticum aestivum versus extracted allotetraploid) as ground truth. The error rates in the two species were very similar. The standard approaches showed higher error rates  while subgenome-classification approaches showed much lower error rates . Although downstream analysis may partly mitigate mapping errors, the difference in methods was substantial in hexaploid wheat, where Kallisto appeared to have systematic differences relative to other methods. Only approximately half of the differentially expressed homeologs detected using Kallisto overlapped with those by any other method in wheat. In general, disagreement in low-expression genes was responsible for most of the discordance between methods, which is consistent with known biases in Kallisto. We also observed that there exist uncertainties in genome sequences and annotation which can affect each method differently. Overall, subgenome-classification approaches tend to perform better than standard approaches with EAGLE-RC having the highest precision.Genome duplication with hybridization, or allopolyploidization, occurs in animals, fungi and plants, and is especially common in crop plants. There is an increasing interest in the study of allopolyploids because of advances in polyploid genome assembly; however, the high level of sequence similarity in duplicated gene copies (homeologs) poses many challenges. Here we compared standard RNA-seq expression quantification approaches used currently for diploid species against subgenome-classification approaches which maps reads to each subgenome separately. We examined mapping error using our previous and new RNA-seq data in which a subgenome is experimentally added  or reduced (allohexaploid wheat  Genome duplication, termed polyploidization, is widespread in plants with up to 35% of land plants being recent polyploids . Many crde novo genome assembly for allopolyploids is no longer as formidable as it once was, a large number of polyploid reference genomes are expected to become available in the near future to facilitate genome-wide studies. Accordingly, there is a need to evaluate expression quantification methods given the presence of homeologs in allopolyploids.Recent improvements in long sequencing read technologies and assembly strategies  have allHigh levels of sequence similarity pose many challenges to read mapping and consequently, expression quantification as well as other types of sequence analysis. Mapping bias was shown to be an issue in numerous studies on diploid allele specific expression , which iArabidopsis kamchatica (Fisch. ex DC.) K. Shimizu & Kudoh and performed tests with its two direct parental accessions of Arabidopsis halleri and Arabidopsis lyrata. For hexaploid wheat Triticum aestivum Chinese Spring [Aegilops tauschii the diploid progenitor of wheat subgenome D.In this study we pose the question ‘How do subgenome-classification approaches perform in comparison to traditional RNA-seq methods in allopolyploids?’ and evaluate different approaches and methods for homeolog expression quantification in allopolyploids. To evaluate methods in polyploids, analysis on genetic materials with and without subgenomes is highly valuable as a form of ground truth. Here we used synthetic allotetraploid e Spring  with sube Spring  as well Although genome-alignment tools designed for diploid species such as STAR , TopHat (i) A standard genome-alignment-based RNA-seq analysis on the full allopolyploid reference genome with two different alignment tools STAR and LAST, with read counts obtained using featureCounts .(ii) A pseudo-alignment-based method with Kallisto on the full allopolyploid transcriptome.(iii) A subgenome-classification approach with HomeoRoq, which maps read sequences to each subgenome separately, with read counts obtained using featureCounts.(iv) A subgenome-classification approach with EAGLE-RC, which maps read sequences to each subgenome separately and also explicitly uses genotype variations that discriminate between homeologs as constraints in analysis, with read counts obtained using featureCounts.We test, to the best of our knowledge, all general approaches to quantify expression in polyploids:A. kamchatica, 0.40%; hexaploid wheat, 0.49%) for alignments to the correct subgenome, while Kallisto had the highest error rate . LAST and STAR had similar error rates in A. kamchatica, but LAST was more precise in hexaploid wheat. In general, performance between methods was comparable for A. kamchatica but not for hexaploid wheat. We also observed systematic differences in low-expression genes that impacted the homeolog expression bias results. Other concerns include uncertainty in the completeness and accuracy of the genome sequence and annotation, which affected each method differently due to differences in constraints. This may be especially relevant as polyploid species have only recently begun to be sequenced and assembled in large numbers and the gene annotations are in their 1st iterations. In the face of this uncertainty, EAGLE-RC is the most precise in our evaluations.Our results show that EAGLE-RC had the lowest error rate  [A. lyrata lyrpet4  [The natural species mchatica , 43 was recently , 44. It recently . To cons 712 kb)  and A. l 1.2 Mb) . The two 1.2 Mb) , the subA. kamchatica, we used data from the parental species so that the ground truth of a read’s subgenome origin is known. A. halleri subsp. gemmifera and A. lyrata subsp. patraea RNA-seq data [A. halleri and A. lyrata with 2 x 100 bp paired-end reads for a total of ~20 and ~21 Gb, respectively. For differential homeolog expression analysis, synthetic allotetraploid A. kamchatica RNA-seq data [A. kamchatica before and after cold stress with 2 x 100 bp paired-end reads for a total of ~12 and ~10 Gb, respectively.To assess classification accuracy in synthetic seq data  were obtseq data  was obtaT. aestivum with subgenomes ABD were obtained from the International Wheat Genome Sequencing Consortium [The whole genome assembly and annotation for hexaploid wheat nsortium  . Subgenome D of hexaploid wheat is known to be derived from this species, thus it should be highly similar though there is divergence due to within-species variations [A. tauschii in triplicate for a total of 2.1 and 2.8 Gb, respectively, under DDBJ run accessions DRR155268–DRR155273. For differential homeolog expression analysis, T. aestivum RNA-seq data were obtained from National Center for Biotechnology Information (BioProject PRJEB12358) with SRA accessions ERR1201752-ERR1201754 and ERR1201770-ERR1201772 describing samples, in triplicate, 24 h after inoculation of fungal pathogen Fusarium graminearum and mock inoculation for a total of 17.3 Gb and 16.1 GB, respectively.To assess classification accuracy in wheat, we generated new tetra-Chinese Spring and crossing . Thus, triations . We consA. tauschii were grown at 16°C in 8 h light/16 h dark cycle with 60% relative humidity for 2 weeks, and leaf tissues were harvested. RNA was extracted from each tissue using RNeasy Plant Mini Kits  in combination with DNase I treatment (QIAGEN). Illumina sequencing libraries were made by TruSeq Stranded mRNA Library Prep Kit. RNA-seq was conducted using Illumina HiSeq 4000 at the Functional Genomics Centre, Zurich.Tetra-Chinese Spring and A. kamchatica, we constructed RNA transcripts from the gene models in the A. halleri and A. lyrata gene annotations using gffread (v0.9.12). Homeologs were then identified based on reciprocal best hit for each subgenome’s transcripts using LAST (v809). We required hits to have an E-value less than 10−10 with at least 200 aligned bases in both transcripts, resulting in 24329 homeolog pairs identified. This method of identifying homeologs is simple but efficient and also allows for the enumeration of genotype differences between subgenomes, a necessary resource for EAGLE-RC.To annotate homeologs in T. aestivum, we constructed RNA transcripts from high-confidence gene models belonging to subgenomes A, B and D, including the UTR regions. We then identified pair-wise homeologs through reciprocal best hit for combinations AB, AD and BD  and then triple copy homeologs by checking for genes in AB that share the same hit for D in their respective AD and BD hits, resulting in 21196 triple copy homeologs identified. Though these gene models were not used in this study, for reference, we discovered 3977, 4781 and 4814 two-copy homeologs in AB, AD and BD, respectively. For singletons, there were 13 459, 14 093 and 11 551 out of 44 796, 45 463 and 43 754 total gene models in subgenomes A, B and D, respectively.To annotate homeologs in (i) Map reads to the allopolyploid reference genome—STAR (v2.5.2b)  and LAST(ii) Count reads using featureCounts (v1.5.1)  at the t(iii) Extract homeolog specific read counts.We tested a standard genome-alignment-based RNA-seq expression quantification approach  aimed atOther miscellaneous sequence-processing tasks such as indexing and sorting were done using samtools (v1.5.2).A. kamchatica, we concatenated the reference genomes of its two parental species, A. halleri and A. lyrata, to obtain an allopolyploid reference. For LAST, we also filtered out read alignments with MAPQ scores less than 20, while STAR MAPQ scores are not as useful for thresholding. In T. aestivum, we excluded all reads that mapped to chrUn.To construct the reference genome of est_counts) at the transcript level. When evaluating classification accuracy, we used the pseudobam option in Kallisto to output read assignments and evaluate the proportion which were misassigned.We tested a pseudo-alignment RNA expression quantification workflow  using KaWe tested a subgenome-classification approach where, in contrast to the standard approach, the sequencing reads are mapped separately to each subgenome of an allopolyploid’s reference genome  and D usHomeoRoq  Figure  classifiA. kamchatica, reads are mapped to the reference genomes of A. halleri (H), and A. lyrata (L), separately. EAGLE-RC then calculates the probability for read r given each subgenome as the reference hypothesis The basic EAGLE model  is a genT. aestivum subgenome classification, we performed a bottom-up workflow using a series of pair-wise classifications  [A. halleri-derived subgenome and A. lyrata-derived subgenome separately. We required a homeolog to be differentially expressed with 0.05 or better false discovery rate (Benjamini–Hochberg FDR) in at least one of the subgenomes. We then performed Fisher’s exact test in R on the combined read counts to determine significant differential homeolog expression ratio (P < 0.05 and fold change ≥ 2). This definition of differential expression requires significant change in gene expression in at least one subgenome according to DESeq2 along with significant change in the homeolog expression ratio between subgenomes according to Fisher’s exact test, under different conditions. There may be a multiple-testing issue with the Fisher’s exact tests for selected homeologs but we did not perform further FDR corrections.To identify differentially expressed homeologs (DEHs) in v1.20.0)  in the RT. aestivum, we used DESeq2 on the read count data for each of subgenomes A, B and D separately, requiring a gene to be differentially expressed with 0.05 or better FDR in at least one of the subgenomes across three pair-wise tests. We then performed a series of Fisher’s exact tests on the combined read counts  to test for differential homeolog expression ratio. A homeolog is differentially expressed if at least one test has P < 0.05/3 (Bonferroni correction) and fold change ≥ 2. Similar to the tetraploid analysis, we did not perform further FDR corrections.To find DEH in hexaploid Generally, many types of sequence analysis rely on how accurately read sequences can be mapped to a reference. In the case of allopolyploids, read alignment must deal with a higher degree of repetitiveness than in diploids due to homeologs.We tested the standard genome alignment approach using the widely used RNA-seq read mapping tool, STAR. Though this tool is often used in diploid read mapping, it is unclear whether it will be suitable for allopolyploids. Unfortunately, the mapping quality score from STAR is not suitable for thresholding ``uniqueness” due to how it assigns scores in an almost binary manner. Thus, we also tested LAST, a general sequence alignment tool that can estimate the probability that an alignment represents the genomic source of the read. For example, a given read aligns to a single location with no mismatches, but aligns to five other locations with one mismatch each. This read may be deemed a unique best hit, but there may be a reasonable probability, depending on read length, that it came from any of the other alignments with a single base-calling error. LAST allows us to set a degree of uniqueness (i.e. 0.05 mismap probability) as a cut-off threshold that is convenient for handling this type of uncertainty.A. kamchatica first because it may be less complicated than hexaploid wheat, which we describe later in this study. Here, we evaluated the accuracy of each tool and each approach by how well they assigned reads to the correct subgenome. For A. kamchatica, the ground truth is known by testing with pure A. halleri and A. lyrata RNA-seq data. A. kamchatica. One point to note is that the mapping rate is affected by alignments to non-homeologs, thus we also showed the number of homeologs with detected expression in our evaluations.We examined tetraploid We calculated the precision by using classification error rate as the criterion. It is clear that subgenome-classification approaches (HomeoRoq and EAGLE-RC) performed better than the standard alignment-based approaches  using a concatenated genome or pseudo-alignment on a concatenated transcriptome. In the standard alignment-based approach using a concatenated reference genome, LAST’s mismap probability model was seen to be beneficial to read classification showing a much lower classification error rate than STAR, with only a slightly lower number of expressed homeologs detected.A. halleri genes with randomly assigned SNPs at varying degrees of sequence divergence . HomeoRoq’s and EAGLE-RC’s error rates increased slightly due to these methods’ discarding reads that are deemed unclassifiable. However, a large number of unclassifiable reads are due to equivalent alignments to both subgenomes, deemed subgenome common, which can be used to estimate expression levels such as RPKM by distributing proportionally to each subgenome. This has been demonstrated previously for HomeoRoq  and is aA. kamchatica and quantified the A. halleri read count proportion Next, we examined the ratio of homeologous pairs. We are interested in any shifts in expression between homeologs across conditions, so we analyzed RNA-seq data from r2) between the results for all methods. The RMSD is a measure of the average distance between two sets X and Y:While we do not know the ground truth expression levels, we can evaluate the concordance between the different methods to describe how results might differ depending on which approach was used. To compare the results between different methods, we calculated the pair-wise root–mean–squared distance (RMSD) and coefficient of determination . HomeoRoq was also concordant (average r2 = 0.9084) with STAR and LAST. This is consistent with the error rates in the classification results.We examined the ratio of homeologous pairs using RNA-seq data from . The r2  and RMSD. The r2  for \docr2 values between methods showed that though read mapping precision can vary greatly, downstream read counting methods were somewhat able to correct for artifacts or ambiguity in read alignment to arrive at a similar quantified expression. Kallisto was discordant from all other methods with an average RMSD of 18.88%. This may be due to its built-in read-counting method using expectation–maximization (EM) whereas all other methods used featureCounts, which does not consider multi-mapped reads. There were also issues with low-expression genes where 420 and 447 A. halleri and A. lyrata homeologs, respectively, were reported to have near-zero expression (<10 reads) by all other methods in all samples but Kallisto reported more than 100 reads. The homeolog expression ratio scatter plots reflect this potential artifact  of the reads assigned to A. halleri mapped to regions that were not aligned between homeologs  in RMSD. This may be due to a higher proportion of reads being ambiguous and each method’s difference in handling these ambiguously mapped reads. This may have large implications if the goal is to determine homeolog expression bias.Intuitively, genes with lower expression are expected to be more affected by uncertainties due to read count differences having more of an effect on the proportion . Indeed,T. aestivum, the three homeologous copies further complicate read mapping and also requires a more complex workflow for subgenome classification. To test classification performance, we used RNA-seq data of a tetraploid wheat line (tetra-Chinese Spring) with subgenomes AB and a diploid A. tauschii line (accession KU-2076) with subgenome D as the ground truth. For hexaploid wheat A. kamchatica and allohexaploid wheat  reads had much higher error rates, propagated from errors in the alignment. Again, STAR and Kallisto were the most affected by the increased distance in these samples.As expected, the error rate was higher in hexaploid wheat than in T. aestivum by calculating the proportion of subgenome A reads Next, we examined homeolog expression shifts across conditions and quantified the homeolog expression ratio in T. aestivum samples 24 h after fungal inoculation and after mock inoculation. The r2 . The results here reiterate that discordance was systematic, where Kallisto’s tendency to overestimate low-expression genes led to significant differences in the homeolog expression ratio.Then, we examined differentially expressed homeologs using RNA-seq data from  methods  show thaRecent improvements in sequencing technology have reduced the difficulty in constructing allopolyploid reference genomes  and therIt is well known that the presence of repetitive sequences in diploids  present technical challenges for read alignment , 56 and There has been a long discussion and dispute about the bias of expression between homeologs and subgenomes because different species show different patterns . In thisIn our evaluations, we presented results both at the read mapping stage and after expression quantification. It is difficult to be completely fair when evaluating the accuracy of read mapping due to differences in each of the four approaches. Kallisto does not attempt to identify primary versus secondary alignments, thus a primary alignment only evaluation is not suitable. The standard genome alignment approach may have secondary alignments due to the presence of homeologs, which can be indistinguishable from the primary alignment in terms of alignment score, in which case a primary alignment is picked randomly. The subgenome-classification approach of mapping reads to each subgenome separately has inherently fewer secondary alignments, which is an advantage of this approach. Thus, we evaluated the classification accuracy with all alignments, as secondary alignment errors are a factor in all approaches though to different degrees. The results after read counting then present the performance of each approach after potentially accounting for ambiguous alignments. In this way we show the performance at both major steps in the RNA-seq expression quantification process, though there are many other read counting methods that we were not able test. Read counting methods may also benefit from the higher precision of the subgenome-classification approach by utilizing classified reads in lieu of uniquely mapped reads and distributing ambiguous reads through EM.A. kamchatica and hexaploid wheat T. aestivum using RNA-seq. We examined the standard genome alignment based approach with STAR and LAST, the subgenome-classification approach with HomeoRoq and EAGLE-RC and a pseudo-alignment approach with Kallisto.In this study, we evaluated methods for homeolog expression quantification in tetraploid A. kamchatica and hexaploid wheat.The presence of homeologs had the largest effect on STAR and Kallisto, resulting in higher read classification error. We observed that discordance was systematic, occurring mostly in low-expression genes and can result in large shifts in the homeolog expression ratio. The explicit use of genotype differences between homeologs in EAGLE-RC seems to be a factor in reducing uncertainties in the reference genome and annotation. Our results show that EAGLE-RC was the most precise method in both tetraploid Supp_bby121Click here for additional data file.


Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning. When applying to relational datasets (R) for comprehensive clinical analysis and practice, the functional/statistical relations ) overlapping with many “either-or” cases, further complicate the decision and interpretation in ML. As a result of these entanglements, ML research continue to encounter difficult problems such as (i) lacking transparency for understanding the inputs, models and outputs3; (ii) difficulty in identifying the mislabeled/anomalies4; and (iii) getting biased results when the record size is small, or the class distribution is imbalanced6.Machine Learning (ML) engages in the development of theories and algorithms for building computation models to make predictions or decisions based on sample data. ML has important empirical successes on data, such as images, signals, texts and speech, with outcomes akin to human cognition and discernment. However, the interpretability of these methods is still a challenge2 that AI focuses on accuracy improvement but provides little explanation. In the biomedical areas, this may lead to overdiagnosis in the healthy population, thus increasing the burden to health care systems instead of relieving it7. Current Explainable AI studies tend to focus on model explanation but not result interpretation. They are unable to spot/reveal erroneous inputs, misused features or entangled outputs. However, results interpretation is highly desired in the clinical context7. Methodologically, the explainability addressed in PDD attempts to meet the clinical challenges, not merely to pose a technical discourse. It intends to provide clinical results that are explainable to a clinical practitioner, comprehensible by the patients, and efficacious for selecting diagnostic characteristics, determining the therapeutic treatment of patients, and detecting rare cases from imbalanced clinical data.Topol has noted in9 or Pattern Discovery (PD)10 were proposed. However, they typically produce an overwhelming number of overlapping/redundant patterns coming from entwined classes/groups11. These patterns are hard to partition and summarize13 for revealing precise “knowledge” inherent in the source environment, thus making interpretation difficult and lowering prediction accuracy. Recently, our bioinformatics study16 furnishes strong scientific evidence that AVA entanglement exists (even among interacting amino acids in complex protein binding environment) but can be disentangled by our new method15 to unveil six major statistical/functional spaces each of which reflects a specific amino-acid interacting functionality. The use of such knowledge significantly leverages prediction accuracy and renders succinct explanation.To render interpretability, ML methods such as Decision Trees/Forests, Frequent Pattern Mining15, we mean functions, relations and associations that are inconspicuous at the raw data level due to source entanglements but can be discovered and represented in a unified interpretable knowledgebase that links a much smaller set of explicit patterns to individual entities as well as their classes or underlying causes that begat those specific associations. This paper presents the problem-solving and explainability capabilities of PDD as applied to proteomics and disease prediction/diagnosis in support of therapeutic and prognostic evaluation to further bring forth PDD’s scientific and clinical value.Accordingly, a data-driven exploratory method, Pattern Discovery and Disentanglement (PDD) has been developed to discover robust/succinct patterns with statistical support and implicit functional clues that can be used to explain the underlying phenomena and augment scientific exploration as well as achieve high prediction accuracy even for rare and imbalance groups. PDD discovers deep knowledge from relational datasets. By deep knowledge17, statistical information theory19, pattern discovery20, pattern clustering11 and knowledge discovery16. We are proceeding from data to information to patterns to knowledge. Information measures and patterns are both accounting event associations deviating from random/independent default models. The former is a measure assigned to events, while the latter are tangible items extracted from the data for further analysis. To support ML tasks and explainability, we then developed pattern discovery (PD)20, pattern clustering11 and summarization. While PD, in principle, is able to reveal explainable patterns, yet due to their entanglement in the complex multiple source environments, it usually produced an overwhelming number of redundant/overlapping/entangled patterns defeating the purpose of explainability. This led to the development of PDD.The fundamentals and theoretical development of PDD stemmed from database managementFigure 21. We treat an APC as R by considering each aligned site (column) as an attribute with amino acids on it as AVs . To show the efficacy of PDD in solving biomedical problems, two healthcare datasets, the Breast Cancer dataset22 and the Heart Disease dataset23 from UCI repository24 were used . The details of the datasets are summarized as below.To examine the notion of pattern entanglement and disentanglement and their impact in ML and scientific applications, we designed and conducted a synthetic experiment  with aligned patterns from 80 samples obtained from an ensemble with imbalanced classes: 30 Mammals, 25 Plants, 20 Fungi and 5 Insects.The first APC dataset taken from cytochrome c25 with different function domains. It consists of 12 attributes and 95 samples taken from 5 distinct classes located in 5 different function domains. Their patterns from different functional domains were entangled, as we found later.Another APC was obtained from Class A Scavenger Receptor family (SR-A)26, we inserted into the dataset two small transition groups—Transition1 and Transition2 . They were stochastically generated with transitional AVs from Benign to Malignant to mimic the early stage of cancer. Figure 27.It contains nine numerical cytohistological attributes with 682 cases . To exemplify PDD’s ability to discover patterns for small/rare classes and discriminate biases/anomalies23 dataset contains 13 mixed-mode attributes  with or without AVA disentanglement; and (b) with or without class labels given. Analysis II explored PDD’s unsupervised ML capability, not relying on prior knowledge, to cluster entities coming from different function-domains/classes with noise and pattern entanglement and compared the results with those of K-means. Analysis III demonstrated PDD’s capability in detecting anomalies and rare groups, which were added artificially, from a cytohistological dataset (the breast cancer dataset). Analysis IV focused on supervised classification when anomalies are present in R and investigated how their identification and removal could improve classification accuracy. As for each of the above cases, we highlighted the practical aspects of PDD in solving real life genomic/healthcare problems and also its efficacy in data analytics and explainable AI.21, with imbalanced group distribution. We compared its results with a traditional pattern mining algorithm, Apriori9. Figure 9  and Transition2 (DSU = [2 2 1]), are found separated from each other. Some specific patterns as inserted  ing Fig. d showed ing Fig. a top enc29 before and after removing all detected outliers and mislabeled entities  and Artificial Neural Network (ANN)ies Fig. c. It is 30. First, as shown in Analysis I, PDD discovers patterns in the AVA disentangled spaces based on intrinsic statistically significant AVAs in different AVA Spaces without requiring explicit priori knowledge. It separated taxonomic groups including a very small Insect group with 5 samples. Second, in Analysis II, from an APC of a diverse Class A Scavenger Receptor family, patterns clusters obtained from the DSs are precisely separated corresponding to distinct functional groups. In Analysis III and Analysis IV, PDD outputs a statistical supported comprehensive and interpretable unified knowledge representation (PDDKB) containing a much smaller set of distinct and explicit patterns/pattern-clusters related to different functional sources. It interlinks patterns, source environments and individual entities, accomplishing the targeted ML tasks, and allowing biomedical knowledge interpretation, exploration and organization. In addition, the unsupervised learning results rendered superior performance of PDD to K-Means in entity clustering and anomaly detection. Finally, in Analysis IV, supervised classification comparison results show that PDD, upon the identification and removal of anomalies, could help to improve the classification performance of all three ML models by approximately 10%. Such ability of correcting ground truth is novel in ML.As for data analytics, PDD has a significant advantage over ‘blackbox’ ML algorithms for it overcomes the major hurdles—interpretability, credibility and applicability—in ML4. Hence, it can contribute significantly to early disease prediction/diagnosis, treatment, and prognosis evaluation of various conditions, particularly for depression31, complex neuropsychiatric disorders such as Autism Spectrum Disorders32 and stroke33.In applications to real world problems as exemplified by the proteomic and medical studies, the novel capability and robustness of PDD have been empirically, statistically and functionally demonstrated. In proteomics, PDD can reveal imbalanced taxonomic classes (rare mutants) and subgroup characteristics of conserved functional domains, obtaining accurate and explicit predictive analytic results without relying on prior knowledge . In the medical data analytics, PDD furnishes clinical/statistical support, linking diagnostic patterns to the etiological origins and individual patients, with evidence explicitly displayable to medical professionals, allowing them to make further examination, testing, assessment and therapeutic decisions . In addition, anomalies can be detected by PDD in an interpretable way rather than leaving them as undecided problematic cases. This is very important for disease diagnosis since outliers not having significant disease association and mislabeled patients in the training record attribute to lowering the diagnostic accuracy34. It hence overcomes the limitations of current ML methods on bias, rare groups, anomalies5 and lack of transparency3.The novel theoretic concept, the efficacy of the algorithm design and the depth and breadth of the all-in-one integrated deep knowledge representation are strong evidence that PDD is a game changer in relational data analysis. It is the first AI system to discover patterns from disentangled AVA spaces, each of which relate to more specific underlying sources/causes. It represents the results in a unified representation interlinking the sources, patterns and entities together to enhance accuracy, avoid biases and render interpretability for various ML tasks and various parts of the analytical processes. Since the results which PDD obtains are robust, explicit, displayable and explainable for experts’ interpretation, question-answering and knowledge base construction, it has great potential to enhance ML and render a new form of Explainable AI35. It will play an important role in empirical and data sciences as it brings AI closer to experts with insight and accountability, meeting the scientific, economic, legal and social challenges for AI in healthcare and data analytics for the years to come.In conclusion, PDD can bridge the ‘AI chasm’—the gap between creating a scientifically sound algorithm and its application to real-world problemsSupplementary information.