MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier.

MOTIVATION: Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can predict only one of all subchloroplast locations of this kind of multilabel proteins. RESULTS: To address this problem, through utilizing label-specific features and label correlations simultaneously, a novel multilabel classifier was developed for predicting protein subchloroplast location(s) with both single and multiple location sites. As an initial study, the overall accuracy of our proposed algorithm reaches 55.52%, which is quite high to be able to become a promising tool for further studies. AVAILABILITY AND IMPLEMENTATION: An online web server for our proposed algorithm named MultiP-SChlo was developed, which are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/multip-schlo/. CONTACT: pandaxiaoxi@gmail.com or gzli@tongji.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble.

BACKGROUND: It has become a very important and full of challenge task to predict bacterial protein subcellular locations using computational methods. Although there exist a lot of prediction methods for bacterial proteins, the majority of these methods can only deal with single-location proteins. But unfortunately many multi-location proteins are located in the bacterial cells. Moreover, multi-location proteins have special biological functions capable of helping the development of new drugs. So it is necessary to develop new computational methods for accurately predicting subcellular locations of multi-location bacterial proteins. RESULTS: In this article, two efficient multi-label predictors, Gpos-ECC-mPLoc and Gneg-ECC-mPLoc, are developed to predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. The two multi-label predictors construct the GO vectors by using the GO terms of homologous proteins of query proteins and then adopt a powerful multi-label ensemble classifier to make the final multi-label prediction. The two multi-label predictors have the following advantages: (1) they improve the prediction performance of multi-label proteins by taking the correlations among different labels into account; (2) they ensemble multiple CC classifiers and further generate better prediction results by ensemble learning; and (3) they construct the GO vectors by using the frequency of occurrences of GO terms in the typical homologous set instead of using 0/1 values. Experimental results show that Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. CONCLUSIONS: Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently improve prediction accuracy of subcellular localization of multi-location gram-positive and gram-negative bacterial proteins respectively. The online web servers for Gpos-ECC-mPLoc and Gneg-ECC-mPLoc predictors are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/gpos-ecc-mploc/ and http://biomed.zzuli.edu.cn/bioinfo/gneg-ecc-mploc/ respectively.

A hybrid imputation approach for microarray missing value estimation.

BACKGROUND: Missing data is an inevitable phenomenon in gene expression microarray experiments due to instrument failure or human error. It has a negative impact on performance of downstream analysis. Technically, most existing approaches suffer from this prevalent problem. Imputation is one of the frequently used methods for processing missing data. Actually many developments have been achieved in the research on estimating missing values. The challenging task is how to improve imputation accuracy for data with a large missing rate. METHODS: In this paper, induced by the thought of collaborative training, we propose a novel hybrid imputation method, called Recursive Mutual Imputation (RMI). Specifically, RMI exploits global correlation information and local structure in the data, captured by two popular methods, Bayesian Principal Component Analysis (BPCA) and Local Least Squares (LLS), respectively. Mutual strategy is implemented by sharing the estimated data sequences at each recursive process. Meanwhile, we consider the imputation sequence based on the number of missing entries in the target gene. Furthermore, a weight based integrated method is utilized in the final assembling step. RESULTS: We evaluate RMI with three state-of-art algorithms (BPCA, LLS, Iterated Local Least Squares imputation (ItrLLS)) on four publicly available microarray datasets. Experimental results clearly demonstrate that RMI significantly outperforms comparative methods in terms of Normalized Root Mean Square Error (NRMSE), especially for datasets with large missing rates and less complete genes. CONCLUSIONS: It is noted that our proposed hybrid imputation approach incorporates both global and local information of microarray genes, which achieves lower NRMSE values against to any single approach only. Besides, this study highlights the need for considering the imputing sequence of missing entries for imputation methods.

Cough event classification by pretrained deep neural network.

BACKGROUND: Cough is an essential symptom in respiratory diseases. In the measurement of cough severity, an accurate and objective cough monitor is expected by respiratory disease society. This paper aims to introduce a better performed algorithm, pretrained deep neural network (DNN), to the cough classification problem, which is a key step in the cough monitor. METHOD: The deep neural network models are built from two steps, pretrain and fine-tuning, followed by a Hidden Markov Model (HMM) decoder to capture tamporal information of the audio signals. By unsupervised pretraining a deep belief network, a good initialization for a deep neural network is learned. Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples. Three cough HMMs and one noncough HMM are employed to model coughs and noncoughs respectively. The final decision is made based on viterbi decoding algorihtm that generates the most likely HMM sequence for each sample. A sample is labeled as cough if a cough HMM is found in the sequence. RESULTS: The experiments were conducted on a dataset that was collected from 22 patients with respiratory diseases. Patient dependent (PD) and patient independent (PI) experimental settings were used to evaluate the models. Five criteria, sensitivity, specificity, F1, macro average and micro average are shown to depict different aspects of the models. From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average. They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI. CONCLUSIONS: In this paper, we tried pretrained deep neural network in cough classification problem. Our results showed that comparing with the conventional GMM-HMM framework, the HMM-DNN could get better overall performance on cough classification task.

ISMAC: An Intelligent System for Customized Clinical Case Management and Analysis.

Clinical cases are primary and vital evidence for Traditional Chinese Medicine (TCM) clinical research. A great deal of medical knowledge is hidden in the clinical cases of the highly experienced TCM practitioner. With a deep Chinese culture background and years of clinical experience, an experienced TCM specialist usually has his or her unique clinical pattern and diagnosis idea. Preserving huge clinical cases of experienced TCM practitioners as well as exploring the inherent knowledge is then an important but arduous task. The novel system ISMAC (Intelligent System for Management and Analysis of Clinical Cases in TCM) is designed and implemented for customized management and intelligent analysis of TCM clinical data. Customized templates with standard and expert-standard symptoms, diseases, syndromes, and Chinese Medince Formula (CMF) are constructed in ISMAC, according to the clinical diagnosis and treatment characteristic of each TCM specialist. With these templates, clinical cases are archived in order to maintain their original characteristics. Varying data analysis and mining methods, grouped as Basic Analysis, Association Rule, Feature Reduction, Cluster, Pattern Classification, and Pattern Prediction, are implemented in the system. With a flexible dataset retrieval mechanism, ISMAC is a powerful and convenient system for clinical case analysis and clinical knowledge discovery.

Syndrome Differentiation Analysis on Mars500 Data of Traditional Chinese Medicine.

Mars500 study was a psychological and physiological isolation experiment conducted by Russia, the European Space Agency, and China, in preparation for an unspecified future manned spaceflight to the planet Mars. Its intention was to yield valuable psychological and medical data on the effects of the planned long-term deep space mission. In this paper, we present data mining methods to mine medical data collected from the crew consisting of six spaceman volunteers. The synthesis of the four diagnostic methods of TCM, inspection, listening, inquiry, and palpation, is used in our syndrome differentiation. We adopt statistics method to describe the syndrome factor regular pattern of spaceman volunteers. Hybrid optimization based multilabel (HOML) is used as feature selection method and multilabel k-nearest neighbors (ML-KNN) is applied. According to the syndrome factor statistical result, we find that qi deficiency is a base syndrome pattern throughout the entire experiment process and, at the same time, there are different associated syndromes such as liver depression, spleen deficiency, dampness stagnancy, and yin deficiency, due to differences of individual situation. With feature selection, we screen out ten key factors which are essential to syndrome differentiation in TCM. The average precision of multilabel classification model reaches 80%.

Development and psychometric validation of the Chinese version of Skindex-29 and Skindex-16.

BACKGROUND: Dermatological disease significantly affects patient's health-related quality of life (HrQoL). Skindex is one of the most frequently used dermatology-specific HrQoL measures. Currently no Chinese version of Skindex is available. The aim of this study was to translate and culturally adapt Skindex-29 and Skindex-16 into Chinese, and to evaluate their reliability and validity. METHODS: Translation and cultural adaption were performed following guidelines for cross-cultural adaption of health-related quality of life measures. Subsequently, a cross-sectional study was conducted in which patients with dermatological disease (n = 225) were enrolled. The Chinese version of Skindex-29 and Skindex-16 and Dermatology Life Quality Index (DLQI) were completed. Reliability was evaluated with internal consistency using Cronbach's alpha. Validity was evaluated using known-groups validity, convergent validity and factor structure validity. RESULTS: There were both seven items of Skindex-29 and Skindex-16 requiring a second forward- and backward- translation to achieve the final satisfactory Chinese version. The internal consistency reliability was high (range of Cronbach's alpha for the scales of Skindex-29 0.85-0.97, Skindex-16 0.86-0.96). Known-group validity was demonstrated by higher scores from patients with inflammatory dermatosis than from patients with isolated skin lesions (P < 0.05). Evidence of factor structure validity of the Skindex-29 and Skindex-16 was demonstrated by both exploratory factor analysis that accounted for 68.66% and 77.78% of the total variance, respectively, and confirmatory factor analysis with acceptable fitness into the expected three-factor structure. CONCLUSION: This study has developed semantically equivalent translations of Skindex-29 and Skindex-16 into Chinese. The evaluation of the instruments' psychometric properties shows they have substantial evidence of reliability and validity for use as HrQoL instruments in Chinese patients with dermatological disease.

[Localization of cAMP-response element binding protein target gene Staufen in mouse hippocampus by situ hybridization].

OBJECTIVE: To search for an appropriate animal model that is more closely related to human to study cAMP-response element binding protein target gene Staufen and to identify its location. METHODS: The phylogenetic tree was constructed with Staufen protein (STAUFEN) sequences of different species, and the most suitable animal model was selected by analyzing relativity among them. The Staufen fragments were amplified with reverse transcription-PCR and inserted into a vector and then the sub-clone was transformed into bacteria, selected, amplified, extracted and sequenced. Staufen probes were in vitro transcribed and hybridized in situ on the cryosections of the mouse brain. The cryosections were stained and observed. RESULTS: The clustering patterns of the phylogenetic tree indicated that the mouse and human Staufen1 had 99.7% protein sequences similarity. The mRNA of Staufen was located in CA1, CA2, CA3 and DG hippocampus regions shown by in situ hybridization. CONCLUSION: The mouse is a preferable animal model for research on Staufen transcription in hippocampus.

Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion.

Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in "treatment based on ZHENG differentiation." From the view of data mining, ZHENG differentiation is modeled as a classification problem. In this paper, ML-kNN-a multilabel learning model-is used as the classification model for hypertension. Feature-level information fusion is also used for further utilization of all information. Experiment results show that ML-kNN can model the hypertension's ZHENG differentiation well. Information fusion helps improve models' performance.

Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines.

BACKGROUND: In Traditional Chinese Medicine (TCM), the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor's nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor's experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images. METHODS: A computer-assisted classification method is designed and applied for syndrome diagnosis based on the lip images. Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale. The proposed scheme consists of four steps including the lip image preprocessing, image feature extraction, feature selection and classification. The extracted 84 features contain the lip color space component, texture and moment features. Feature subset selection is performed by using SVM-RFE (Support Vector Machine with recursive feature elimination), mRMR (minimum Redundancy Maximum Relevance) and IG (information gain). Classification model is constructed based on the collected lip image features using multi-class SVM and Weighted multi-class SVM (WSVM). In addition, we compare SVM with k-nearest neighbor (kNN) algorithm, Multiple Asymmetric Partial Least Squares Classifier (MAPLSC) and Naïve Bayes for the diagnosis performance comparison. All displayed faces image have obtained consent from the participants. RESULTS: A total of 257 lip images are collected for the modeling of lip diagnosis in TCM. The feature selection method SVM-RFE selects 9 important features which are composed of 5 color component features, 3 texture features and 1 moment feature. SVM, MAPLSC, Naïve Bayes, kNN showed better classification results based on the 9 selected features than the results obtained from all the 84 features. The total classification accuracy of the five methods is 84%, 81%, 79% and 81%, 77%, respectively. So SVM achieves the best classification accuracy. The classification accuracy of SVM is 81%, 71%, 89% and 86% on Deep-red, Pale Purple, Red and lip image models, respectively. While with the feature selection algorithm mRMR and IG, the total classification accuracy of WSVM achieves the best classification accuracy. Therefore, the results show that the system can achieve best classification accuracy combined with SVM classifiers and SVM-REF feature selection algorithm. CONCLUSIONS: A diagnostic system is proposed, which firstly segments the lip from the original facial image based on the Chan-Vese level set model and Otsu method, then extracts three kinds of features (color space features, Haralick co-occurrence features and Zernike moment features) on the lip image. Meanwhile, SVM-REF is adopted to select the optimal features. Finally, SVM is applied to classify the four classes. Besides, we also compare different feature selection algorithms and classifiers to verify our system. So the developed automatic and quantitative diagnosis system of TCM is effective to distinguish four lip image classes: Deep-red, Purple, Red and Pale. This study puts forward a new method and idea for the quantitative examination on lip diagnosis of TCM, as well as provides a template for objective diagnosis in TCM.

Question Answering System Based on Knowledge Graph in Traditional Chinese Medicine Diagnosis and Treatment of Viral Hepatitis B.

This article uses the real medical records and web pages of Chinese medicine diagnosis and treatment of hepatitis B to extract structured medical knowledge, and obtains a total of 8,563 entities, 96,896 relationships, 32 entity types, and 40 relationship types. The structured data was stored in the Neo4j graph structure database, and a knowledge graph of Chinese medical diagnosis and treatment of hepatitis B was constructed. The knowledge map is used as a structured data source to provide high-quality knowledge information for the medical question and answer system based on hepatitis B disease. Applying the deep learning method to the question identification and knowledge response of the question answering system makes the hepatitis B medical intelligent question answering system has important research and application significance. The question-and-answer system takes aim at hepatitis B, a public health problem in the world and leverages the advantages of traditional Chinese medicine for diagnosis and treatment. It provides a reference for doctors' disease diagnosis, treatment, and patient self-care. Its value is important for the treatment of hepatitis B disease.

Leveraging a Joint learning Model to Extract Mixture Symptom Mentions from Traditional Chinese Medicine Clinical Notes.

This paper addresses the mixture symptom mention problem which appears in the structuring of Traditional Chinese Medicine (TCM). We accomplished this by disassembling mixture symptom mentions with entity relation extraction. Over 2,200 clinical notes were annotated to construct the training set. Then, an end-to-end joint learning model was established to extract the entity relations. A joint model leveraging a multihead mechanism was proposed to deal with the problem of relation overlapping. A pretrained transformer encoder was adopted to capture context information. Compared with the entity extraction pipeline, the constructed joint learning model was superior in recall, precision, and F1 measures, at 0.822, 0.825, and 0.818, respectively, 14% higher than the baseline model. The joint learning model could automatically extract features without any extra natural language processing tools. This is efficient in the disassembling of mixture symptom mentions. Furthermore, this superior performance at identifying overlapping relations could benefit the reassembling of separated symptom entities downstream.

Genome-wide long non-coding RNA association study on Han Chinese women identifies lncHSAT164 as a novel susceptibility gene for breast cancer.

BACKGROUND: Single-nucleotide polymorphisms (SNPs)-associated genes and long non-coding RNAs (lncRNAs) can contribute to human disease. To comprehensively investigate the contribution of lncRNAs to breast cancer, we performed the first genome-wide lncRNA association study on Han Chinese women. METHODS: We designed an lncRNA array containing >800,000 SNPs, which was incorporated into a 96-array plate by Affymetrix (CapitalBio Technology, China). Subsequently, we performed a two-stage genome-wide lncRNA association study on Han Chinese women covering 11,942 individuals (5634 breast cancer patients and 6308 healthy controls). Additionally, in vitro gain or loss of function strategies were performed to clarify the function of a novel SNP-associated gene. RESULTS: We identified a novel breast cancer-associated susceptibility SNP, rs11066150 (Pmeta = 2.34 × 10-8), and a previously reported SNP, rs9397435 (Pmeta = 4.32 × 10-38), in Han Chinese women. rs11066150 is located in NONHSAT164009.1 (lncHSAT164), which is highly expressed in breast cancer tissues and cell lines. lncHSAT164 overexpression promoted colony formation, whereas lncHSAT164 knockdown promoted cell apoptosis and reduced colony formation by regulating the cell cycle. CONCLUSIONS: Based on our lncRNA array, we identified a novel breast cancer-associated lncRNA and found that lncHSAT164 may contribute to breast cancer by regulating the cell cycle. These findings suggest a potential therapeutic target in breast cancer.

How much space of the spinal canal should be restored by hoisting the vertebrae-OPLL complex for sufficient decompression in anterior controllable antedisplacement and fusion? A multicenter clinical radiological study.

BACKGROUND CONTEXT: Anterior controllable antedisplacement and fusion (ACAF) is a novel surgical technique for the treatment of ossification of the posterior longitudinal ligament (OPLL). Its prognostic factors for decompression have not been well studied. Additionally, no detailed radiological standard has been set for hoisting the vertebrae-OPLL complex (VOC) in ACAF. PURPOSE: To identify the possible prognostic factors for decompression outcomes after ACAF for cervical OPLL, to determine the critical value of radiological parameters for predicting good outcomes, and to establish a radiological standard for hoisting the VOC in ACAF. STUDY DESIGN: This was a retrospective multicenter study. PATIENT SAMPLE: A total of 121 consecutive patients with OPLL who underwent ACAF at a point between January 2017 and June 2018 at any one of seven facilities and were monitored for at least 1 year afterward were enrolled in a multicenter study. OUTCOME MEASURES: Japanese Orthopedic Association (JOA) scores, recovery rate (RR) of neurologic function, and surgical complications were used to determine the effectiveness of ACAF. METHODS: Patients were divided into two groups according to their RR for neurologic function. Patients with an RR of ≥50% and an RR of <50% were designated as having good and poor decompression outcomes, respectively. The relationship between various possible prognostic factors and decompression outcomes was assessed by univariate and multivariate analysis. The receiver operating characteristic curve was used to determine the optimal cutoff value of the radiological parameters for prediction of good decompression outcomes. Next, the patients were redivided into three groups according to the cutoff value of the selected radiological parameter (postoperative anteroposterior canal diameter [APD] ratio). Patients with postoperative APD ratios of ≤80.7%, 80.7%-100%, and ≥100% were defined as members of the incomplete, optimal, and excessive antedisplacement groups, respectively. Differences in decompression outcomes among the three groups were compared to verify the reliability of the postoperative APD ratio and assess the necessity of excessive antedisplacement. RESULTS: Multivariate logistic regression analysis showed that patients' age at surgery (odds ratio [OR]=1.18; 95% confidence interval [CI]=1.08-1.29; p<.01) and postoperative APD ratio (OR=0.83; 95% CI=0.77-0.90; p<.01) were independently associated with decompression outcomes. The optimal cutoff point of the postoperative APD ratio was calculated at 80.7%, with 86.2% sensitivity and 73.5% specificity. There were no significant differences in the postoperative JOA scores and RRs between the excessive antedisplacement group and optimal antedisplacement group (p>.05). However, a lower incidence of cerebrospinal fluid leakage and screw slippage was observed in the optimal antedisplacement group (p<.05). CONCLUSIONS: Patients' age at surgery and their postoperative APD ratio are the two prognostic factors of decompression outcomes after ACAF. The postoperative APD ratio is also the most accurate radiological parameter for predicting good outcomes. Our findings suggest that it is essential for neurologic recovery to restore the spinal canal to more than 80.7% of its original size (postoperative APD ratio >80.7%), and restoration to less than its original size (postoperative APD ratio <100%) will help reduce the incidence of surgical complications. This may serve as a valuable reference for establishment of a radiological standard for hoisting the VOC in ACAF.

2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research.

Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT Austin), Dr. Aidong Zhang (Buffalo) and Dr. Zhi-Hua Zhou (Nanjing) for their significant contributions to the field of intelligent biological medicine.

Modelling of inquiry diagnosis for coronary heart disease in Traditional Chinese Medicine by using multi-label learning.

BACKGROUND: Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem. METHODS: Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets. RESULTS: A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained. CONCLUSIONS: The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.

End-to-End Models to Imitate Traditional Chinese Medicine Syndrome Differentiation in Lung Cancer Diagnosis: Model Development and Validation.

BACKGROUND: Traditional Chinese medicine (TCM) has been shown to be an efficient mode to manage advanced lung cancer, and accurate syndrome differentiation is crucial to treatment. Documented evidence of TCM treatment cases and the progress of artificial intelligence technology are enabling the development of intelligent TCM syndrome differentiation models. This is expected to expand the benefits of TCM to lung cancer patients. OBJECTIVE: The objective of this work was to establish end-to-end TCM diagnostic models to imitate lung cancer syndrome differentiation. The proposed models used unstructured medical records as inputs to capitalize on data collected for practical TCM treatment cases by lung cancer experts. The resulting models were expected to be more efficient than approaches that leverage structured TCM datasets. METHODS: We approached lung cancer TCM syndrome differentiation as a multilabel text classification problem. First, entity representation was conducted with Bidirectional Encoder Representations from Transformers and conditional random fields models. Then, five deep learning-based text classification models were applied to the construction of a medical record multilabel classifier, during which two data augmentation strategies were adopted to address overfitting issues. Finally, a fusion model approach was used to elevate the performance of the models. RESULTS: The F1 score of the recurrent convolutional neural network (RCNN) model with augmentation was 0.8650, a 2.41% improvement over the unaugmented model. The Hamming loss for RCNN with augmentation was 0.0987, which is 1.8% lower than that of the same model without augmentation. Among the models, the text-hierarchical attention network (Text-HAN) model achieved the highest F1 scores of 0.8676 and 0.8751. The mean average precision for the word encoding-based RCNN was 10% higher than that of the character encoding-based representation. A fusion model of the text-convolutional neural network, text-recurrent neural network, and Text-HAN models achieved an F1 score of 0.8884, which showed the best performance among the models. CONCLUSIONS: Medical records could be used more productively by constructing end-to-end models to facilitate TCM diagnosis. With the aid of entity-level representation, data augmentation, and model fusion, deep learning-based multilabel classification approaches can better imitate TCM syndrome differentiation in complex cases such as advanced lung cancer.

Analyzing hedyotis diffusa mechanisms of action from the genomics perspective.

BACKGROUND AND OBJECTIVE: Hedyotis diffusa is an herb used for anti-cancer, anti-oxidant, anti-inflammatory, and anti-fibroblast treatment in the clinical practice of Traditional Chinese Medicine. However, its pharmacological mechanisms have not been fully established and there is a lack of modern scientific verification. One of the best ways to further understand Hedyotis diffusa's mechanisms of action is to analyze it from the genomics perspective. METHODS: In this study, we used network pharmacology approaches to infer the herb-gene interactions, the herb-pathway interactions, and the gene families. We then analyzed Hedyotis diffusa's mechanisms of action using the genomics context combined with the Traditional Chinese Medicine clinical practice and the pharmacological research. RESULTS: The results obtained in the pathway and gene family analysis were consistent with the Traditional Chinese Medicine clinical experience and the pharmacological activities of Hedyotis diffusa. CONCLUSIONS: Our approach can identify related genes and pathways correctly with little a priori knowledge, and provide potential directions to facilitate further research.

Asymmetric bagging and feature selection for activities prediction of drug molecules.

BACKGROUND: Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. RESULTS: Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. CONCLUSION: Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.

Dimension reduction with redundant gene elimination for tumor classification.

BACKGROUND: Analysis of gene expression data for tumor classification is an important application of bioinformatics methods. But it is hard to analyse gene expression data from DNA microarray experiments by commonly used classifiers, because there are only a few observations but with thousands of measured genes in the data set. Dimension reduction is often used to handle such a high dimensional problem, but it is obscured by the existence of amounts of redundant features in the microarray data set. RESULTS: Dimension reduction is performed by combing feature extraction with redundant gene elimination for tumor classification. A novel metric of redundancy based on DIScriminative Contribution (DISC) is proposed which estimates the feature similarity by explicitly building a linear classifier on each gene. Compared with the standard linear correlation metric, DISC takes the label information into account and directly estimates the redundancy of the discriminative ability of two given features. Based on the DISC metric, a novel algorithm named REDISC (Redundancy Elimination based on Discriminative Contribution) is proposed, which eliminates redundant genes before feature extraction and promotes performance of dimension reduction. Experimental results on two microarray data sets show that the REDISC algorithm is effective and reliable to improve generalization performance of dimension reduction and hence the used classifier. CONCLUSION: Dimension reduction by performing redundant gene elimination before feature extraction is better than that with only feature extraction for tumor classification, and redundant gene elimination in a supervised way is superior to the commonly used unsupervised method like linear correlation coefficients.