Construction and Comprehensive Analysis of a Molecular Associations Network via lncRNA-miRNA -disease-drug-protein Graph

The key issue in the post-genomic era is how to systematically describe the association between small molecule transcripts or translations inside cells. With the rapid development of high-throughput “omics” technologies, the achieved ability to detect and characterize molecules with other molecule targets opens up the possibility of investigating the relationships between different molecules from a global perspective. In this article, a Molecular Associations Network(MAN) is constructed and comprehensively analyzed by integrating the associations among miRNA, lncRNA, protein, drug, and disease, in which any kind of potential associations can be predicted. More specifically, each node in MAN can be represented as a vector by combining two kinds of information including the attributes of the node itself (e.g. sequences of ncRNAs and proteins, semantics of diseases and molecular fingerprints of drugs) and the manner of the node in the complex network (associations with other nodes). Random Forest classifier is trained to classify and predict new interactions or associations between biomolecules. In the experiment, the proposed method achieves a superb performance with 0.9735 AUC in 5-fold cross-validation, which show that the proposed method can provide new insight for exploration of the molecular mechanisms of disease and valuable clues for disease treatment.

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Construction and Comprehensive Analysis of a Molecular Association Network via lncRNA–miRNA –Disease–Drug–Protein Graph

Cells ◽

10.3390/cells8080866 ◽

2019 ◽

Vol 8 (8) ◽

pp. 866 ◽

Cited By ~ 18

Author(s):

Guo ◽

Yi ◽

You

Keyword(s):

Molecular Mechanisms ◽

Cross Validation ◽

Rapid Development ◽

Molecular Association ◽

Global Perspective ◽

Association Network ◽

Disease Treatment ◽

Molecular Fingerprints ◽

New Interactions ◽

Fold Cross Validation

One key issue in the post-genomic era is how to systematically describe the associations between small molecule transcripts or translations inside cells. With the rapid development of high-throughput “omics” technologies, the achieved ability to detect and characterize molecules with other molecule targets opens the possibility of investigating the relationships between different molecules from a global perspective. In this article, a molecular association network (MAN) is constructed and comprehensively analyzed by integrating the associations among miRNA, lncRNA, protein, drug, and disease, in which any kind of potential associations can be predicted. More specifically, each node in MAN can be represented as a vector by combining two kinds of information including the attribute of the node itself (e.g., sequences of ncRNAs and proteins, semantics of diseases and molecular fingerprints of drugs) and the behavior of the node in the complex network (associations with other nodes). A random forest classifier is trained to classify and predict new interactions or associations between biomolecules. In the experiment, the proposed method achieved a superb performance with an area under curve (AUC) of 0.9735 under a five-fold cross-validation, which showed that the proposed method could provide new insight for exploration of the molecular mechanisms of disease and valuable clues for disease treatment.

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Multi-Omics Analysis of Acute Lymphoblastic Leukemia Identified the Methylation and Expression Differences Between BCP-ALL and T-ALL

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2020.622393 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jin-Fan Li ◽

Xiao-Jing Ma ◽

Lin-Lin Ying ◽

Ying-hui Tong ◽

Xue-ping Xiang

Keyword(s):

Acute Lymphoblastic Leukemia ◽

Molecular Mechanisms ◽

Cross Validation ◽

Lymphoblastic Leukemia ◽

Expression Data ◽

Expression Signature ◽

Signature Genes ◽

Common Cancer ◽

Monte Carlo Feature Selection ◽

Fold Cross Validation

Acute lymphoblastic leukemia (ALL) as a common cancer is a heterogeneous disease which is mainly divided into BCP-ALL and T-ALL, accounting for 80–85% and 15–20%, respectively. There are many differences between BCP-ALL and T-ALL, including prognosis, treatment, drug screening, gene research and so on. In this study, starting with methylation and gene expression data, we analyzed the molecular differences between BCP-ALL and T-ALL and identified the multi-omics signatures using Boruta and Monte Carlo feature selection methods. There were 7 expression signature genes (CD3D, VPREB3, HLA-DRA, PAX5, BLNK, GALNT6, SLC4A8) and 168 methylation sites corresponding to 175 methylation signature genes. The overall accuracy, accuracy of BCP-ALL, accuracy of T-ALL of the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) classifier using these signatures evaluated with 10-fold cross validation repeated 3 times were 0.973, 0.990, and 0.933, respectively. Two overlapped genes between 175 methylation signature genes and 7 expression signature genes were CD3D and VPREB3. The network analysis of the methylation and expression signature genes suggested that their common gene, CD3D, was not only different on both methylation and expression levels, but also played a key regulatory role as hub on the network. Our results provided insights of understanding the underlying molecular mechanisms of ALL and facilitated more precision diagnosis and treatment of ALL.

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The Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

10.26434/chemrxiv.6895646.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mahendra Awale ◽

Jean-Louis Reymond

Keyword(s):

Machine Learning ◽

Cross Validation ◽

Nearest Neighbor ◽

Target Prediction ◽

Molecular Shape ◽

Public Access ◽

Molecular Fingerprints ◽

Small Molecule Drug ◽

Fold Cross Validation

<div>Here we report PPB2 as a target prediction tool assigning targets to a query molecule based on ChEMBL data. PPB2 computes ligand similarities using molecular fingerprints encoding composition (MQN), molecular shape and pharmacophores (Xfp), and substructures (ECfp4), and features an unprecedented combination of nearest neighbor (NN) searches and Naïve Bayes (NB) machine learning, together with simple NN searches, NB and Deep Neural Network (DNN) machine learning models as further options. Although NN(ECfp4) gives the best results in terms of recall in a 10-fold cross-validation study, combining NN searches with NB machine learning provides superior precision statistics, as well as better results in a case study predicting off-targets of a recently reported TRPV6 calcium channel inhibitor, illustrating the value of this combined approach. PPB2 is available to assess possible off-targets of small molecule drug-like compounds by public access at ppb2.gdb.tools.</div>

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Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations

BMC Bioinformatics ◽

10.1186/s12859-021-04486-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Feng Zhou ◽

Meng-Meng Yin ◽

Cui-Na Jiao ◽

Zhen Cui ◽

Jing-Xiu Zhao ◽

...

Keyword(s):

Bipartite Graph ◽

Matrix Factorization ◽

Cross Validation ◽

Rapid Development ◽

Factorization Method ◽

Computational Method ◽

Human Diseases ◽

Simulation Experiments ◽

Disease Associations ◽

Fold Cross Validation

Abstract Background With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA–disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. Results By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. Conclusions Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.

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The Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

10.26434/chemrxiv.6895646 ◽

2018 ◽

Author(s):

Mahendra Awale ◽

Jean-Louis Reymond

Keyword(s):

Machine Learning ◽

Cross Validation ◽

Nearest Neighbor ◽

Target Prediction ◽

Molecular Shape ◽

Public Access ◽

Molecular Fingerprints ◽

Small Molecule Drug ◽

Fold Cross Validation

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Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach

Briefings in Bioinformatics ◽

10.1093/bib/bbaa228 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yu Zhang ◽

Cangzhi Jia ◽

Chee Keong Kwoh

Keyword(s):

Deep Learning ◽

Molecular Mechanisms ◽

Operating Characteristic ◽

Cross Validation ◽

Characteristic Curve ◽

Published Data ◽

Different Types ◽

Precision Recall Curve ◽

Deep Learning Model ◽

Fold Cross Validation

Abstract Long noncoding RNAs (lncRNAs) play significant roles in various physiological and pathological processes via their interactions with biomolecules like DNA, RNA and protein. The existing in silico methods used for predicting the functions of lncRNA mainly rely on calculating the similarity of lncRNA or investigating whether an lncRNA can interact with a specific biomolecule or disease. In this work, we explored the functions of lncRNA from a different perspective: we presented a tool for predicting the interaction biomolecule type for a given lncRNA. For this purpose, we first investigated the main molecular mechanisms of the interactions of lncRNA–RNA, lncRNA–protein and lncRNA–DNA. Then, we developed an ensemble deep learning model: lncIBTP (lncRNA Interaction Biomolecule Type Prediction). This model predicted the interactions between lncRNA and different types of biomolecules. On the 5-fold cross-validation, the lncIBTP achieves average values of 0.7042 in accuracy, 0.7903 and 0.6421 in macro-average area under receiver operating characteristic curve and precision–recall curve, respectively, which illustrates the model effectiveness. Besides, based on the analysis of the collected published data and prediction results, we hypothesized that the characteristics of lncRNAs that interacted with DNA may be different from those that interacted with only RNA.

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Adolescent age estimation using voice features

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2018-0082 ◽

2020 ◽

Vol 65 (4) ◽

pp. 429-434

Author(s):

Marcin D. Bugdol ◽

Monika N. Bugdol ◽

Maria J. Bieńkowska ◽

Anna Lipowicz ◽

Agata M. Wijata ◽

...

Keyword(s):

Age Estimation ◽

Cross Validation ◽

Rapid Development ◽

Absolute Error ◽

Regression Method ◽

Chronological Age ◽

Height Measurement ◽

Voice Signal ◽

Selection Operator ◽

Fold Cross Validation

AbstractIn this paper, a method for evaluating the chronological age of adolescents on the basis of their voice signal is presented. For every examined child, the vowels a, e, i, o and u were recorded in extended phonation. Sixty voice parameters were extracted from each recording. Voice recordings were supplemented with height measurement in order to check if it could improve the accuracy of the proposed solution. Predictor selection was performed using the LASSO (least absolute shrinkage and selection operator) algorithm. For age estimation, the random forest (RF) for regression method was employed and it was tested using a 10-fold cross-validation. The lowest absolute error (0.37 year ± 0.28) was obtained for boys only when all selected features were included into prediction. In all cases, the achieved accuracy was higher for boys than for girls, which results from the fact that the change of voice with age is larger for men than for women. The achieved results suggest that the presented approach can be employed for accurate age estimation during rapid development in children.

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A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique

BioMed Research International ◽

10.1155/2018/9364182 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Runtao Yang ◽

Chengjin Zhang ◽

Lina Zhang ◽

Rui Gao

Keyword(s):

Feature Selection ◽

Molecular Mechanisms ◽

Cross Validation ◽

Selection Process ◽

Feature Selection Method ◽

Imbalanced Data ◽

Computational Method ◽

Accurate Identification ◽

Comparison Results ◽

Fold Cross Validation

Cancerlectins have an inhibitory effect on the growth of cancer cells and are currently being employed as therapeutic agents. The accurate identification of the cancerlectins should provide insight into the molecular mechanisms of cancers. In this study, a new computational method based on the RF (Random Forest) algorithm is proposed for further improving the performance of identifying cancerlectins. Hybrid feature space before feature selection is developed by combining different individual feature spaces, CTD (Composition, Transition, and Distribution), PseAAC (Pseudo Amino Acid Composition), PSSM (Position-Specific Scoring Matrix), and disorder. The SMOTE (Synthetic Minority Oversampling Technique) is applied to solve the imbalanced data problem. To reduce feature redundancy and computation complexity, we propose a two-step feature selection process to select informative features. A 5-fold cross-validation technique is used for the evaluation of various prediction strategies. The proposed method achieves a sensitivity of 0.779, a specificity of 0.717, an accuracy of 0.748, and an MCC (Matthew’s Correlation Coefficient) of 0.497. The prediction results are also compared with other existing methods on the same dataset using 5-fold cross-validation. The comparison results demonstrate the high effectiveness of our method for predicting cancerlectins.

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DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels

BMC Bioinformatics ◽

10.1186/s12859-019-3093-x ◽

2019 ◽

Vol 20 (S15) ◽

Cited By ~ 1

Author(s):

Cheng Yan ◽

Guihua Duan ◽

Yi Pan ◽

Fang-Xiang Wu ◽

Jianxin Wang

Keyword(s):

Drug Interaction ◽

Drug Development ◽

Cross Validation ◽

De Novo ◽

Drug Effect ◽

State Of The Art ◽

Ensemble Method ◽

New Drugs ◽

Disease Treatment ◽

Fold Cross Validation

Abstract Background A drug-drug interaction (DDI) is defined as a drug effect modified by another drug, which is very common in treating complex diseases such as cancer. Many studies have evidenced that some DDIs could be an increase or a decrease of the drug effect. However, the adverse DDIs maybe result in severe morbidity and even morality of patients, which also cause some drugs to withdraw from the market. As the multi-drug treatment becomes more and more common, identifying the potential DDIs has become the key issue in drug development and disease treatment. However, traditional biological experimental methods, including in vitro and vivo, are very time-consuming and expensive to validate new DDIs. With the development of high-throughput sequencing technology, many pharmaceutical studies and various bioinformatics data provide unprecedented opportunities to study DDIs. Result In this study, we propose a method to predict new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Regularized Least Squares (RLS) classifier. In addition, we also use the k-nearest neighbors (KNN) to calculate the initial relational score in the presence of new drugs via the chemical, biological, phenotypic data of drugs. We compare the prediction performance of DDIGIP with other competing methods via the 5-fold cross validation, 10-cross validation and de novo drug validation. Conlusion In 5-fold cross validation and 10-cross validation, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better than state-of-the-art method (L1 Classifier ensemble method) of 0.9570 and 0.9599. Furthermore, for new drugs, the AUC value of DDIGIP in de novo drug validation reaches 0.9262 which also outperforms the other state-of-the-art method (Weighted average ensemble method) of 0.9073. Case studies and these results demonstrate that DDRGIP is an effective method to predict DDIs while being beneficial to drug development and disease treatment.

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LE-MDCAP: A Computational Model to Prioritize Causal miRNA–Disease Associations

International Journal of Molecular Sciences ◽

10.3390/ijms222413607 ◽

2021 ◽

Vol 22 (24) ◽

pp. 13607

Author(s):

Zhou Huang ◽

Yu Han ◽

Leibo Liu ◽

Qinghua Cui ◽

Yuan Zhou

Keyword(s):

Computational Models ◽

Operating Characteristic ◽

Cross Validation ◽

Characteristic Curve ◽

Levenshtein Distance ◽

Similarity Matrix ◽

Disease Treatment ◽

Independent Test ◽

Disease Associations ◽

Fold Cross Validation

MicroRNAs (miRNAs) are associated with various complex human diseases and some miRNAs can be directly involved in the mechanisms of disease. Identifying disease-causative miRNAs can provide novel insight in disease pathogenesis from a miRNA perspective and facilitate disease treatment. To date, various computational models have been developed to predict general miRNA–disease associations, but few models are available to further prioritize causal miRNA–disease associations from non-causal associations. Therefore, in this study, we constructed a Levenshtein-Distance-Enhanced miRNA–Disease Causal Association Predictor (LE-MDCAP), to predict potential causal miRNA–disease associations. Specifically, Levenshtein distance matrixes covering the sequence, expression and functional miRNA similarities were introduced to enhance the previous Gaussian interaction profile kernel-based similarity matrix. LE-MDCAP integrated miRNA similarity matrices, disease semantic similarity matrix and known causal miRNA–disease associations to make predictions. For regular causal vs. non-disease association discrimination task, LF-MDCAP achieved area under the receiver operating characteristic curve (AUROC) of 0.911 and 0.906 in 10-fold cross-validation and independent test, respectively. More importantly, LE-MDCAP prominently outperformed the previous MDCAP model in distinguishing causal versus non-causal miRNA–disease associations (AUROC 0.820 vs. 0.695). Case studies performed on diabetic retinopathy and hsa-mir-361 also validated the accuracy of our model. In summary, LE-MDCAP could be useful for screening causal miRNA–disease associations from general miRNA–disease associations.

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