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Intelligent Computing in Biology and Medicine

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A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 22833

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Prof. Dr. De-Shuang Huang

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Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo 315201, China
Interests: bioinformatics; biological image processing; pattern recognition and neural network
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

As artificial intelligence (AI) is now a hot research area, intelligent computing technology is also a blooming field. Currently, intelligent computing technology is playing an increasingly important role in helping derive meaningful and logical conclusions in biology and medicine. Understanding the biological and medical data will help in answering important questions of life on earth and finding solutions for global health problems, and can even help in solving problems like drug design and disease diagnosis. The data generated from biology and medicine possess unique properties, such as low-quality data, big data size, different complex formats, high dimensionality, many duplications, high noise, etc.. All of these require a special skill set or unique tools for analysis and interpretation. Thus, research using intelligent computing technology on biological and medical data is becoming a very popular hot topic in the computer science research community.

In this Special Issue focused on Intelligent Computing in Biology and Medicine, we shall solicit the technical papers in the fields of proteomics, molecular recognition, protein folding, bioinformatics, etc., by intelligent computing technology. The aim of this Special Issue is to assemble a collection of manuscripts that showcase the latest research in the bioinformatics field.

Prof. Dr. De-Shuang Huang
Guest Editor

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Keywords

bioinformatics
genome
protein
omics
gene expression
interaction
disease
intelligent computing
data integration

Published Papers (11 papers)

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Research

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13 pages, 4932 KiB

Open AccessArticle

A Novel Automatic Quantification Protocol for Biomarkers of Tauopathies in the Hippocampus and Entorhinal Cortex of Post-Mortem Samples Using an Extended Semi-Siamese U-Net

by Luis A. Campero-Garcia, Jose A. Cantoral-Ceballos, Alejandra Martinez-Maldonado, Jose Luna-Muñoz, Miguel A. Ontiveros-Torres and Andres E. Gutierrez-Rodriguez

Biology 2022, 11(8), 1131; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11081131 - 28 Jul 2022

Viewed by 1676

Abstract

Efforts have been made to diagnose and predict the course of different neurodegenerative diseases through various imaging techniques. Particularly tauopathies, where the tau polypeptide is a key participant in molecular pathogenesis, have significantly increased their morbidity and mortality in the human population over the years. However, the standard approach to exploring the phenomenon of neurodegeneration in tauopathies has not been directed at understanding the molecular mechanism that causes the aberrant polymeric and fibrillar behavior of the tau protein, which forms neurofibrillary tangles that replace neuronal populations in the hippocampal and cortical regions. The main objective of this work is to implement a novel quantification protocol for different biomarkers based on pathological post-translational modifications undergone by tau in the brains of patients with tauopathies. The quantification protocol consists of an adaptation of the U-Net neural network architecture. We used the resulting segmentation masks for the quantification of combined fluorescent signals of the different molecular changes tau underwent in neurofibrillary tangles. The quantification considers the neurofibrillary tangles as an individual study structure separated from the rest of the quadrant present in the images. This allows us to detect unconventional interaction signals between the different biomarkers. Our algorithm provides information that will be fundamental to understanding the pathogenesis of dementias with another computational analysis approach in subsequent studies. Full article

(This article belongs to the Special Issue Intelligent Computing in Biology and Medicine)

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14 pages, 3219 KiB

Open AccessArticle

Identification of Colon Cancer-Related RNAs Based on Heterogeneous Networks and Random Walk

by Bolin Chen, Teng Wang, Jinlei Zhang, Shengli Zhang and Xuequn Shang

Biology 2022, 11(7), 1003; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11071003 - 02 Jul 2022

Cited by 1 | Viewed by 1427

Abstract

Colon cancer is considered as a complex disease that consists of metastatic seeding in early stages. Such disease is not simply caused by the action of a single RNA, but is associated with disorders of many kinds of RNAs and their regulation relationships. Hence, it is of great significance to study the complex regulatory roles among mRNAs, miRNAs and lncRNAs for further understanding the pathogenic mechanism of colon cancer. In this study, we constructed a heterogeneous network consisting of differentially expressed mRNAs, miRNAs and lncRNAs. This contains three kinds of vertices and six types of edges. All RNAs were re-divided into three categories, which were “related”, “irrelevant” and “unlabeled”. They were processed by dynamic excitation restart random walk (RW-DIR) for identifying colon cancer-related RNAs. Ten RNAs were finally obtained related to colon cancer, which were hsa-miR-2682-5p, hsa-miR-1277-3p, ANGPTL1, SLC22A18AS, FENDRR, PHLPP2, hsa-miR-302a-5p, APCDD1, MEX3A and hsa-miR-509-3-5p. Numerical experiments have indicated that the proposed network construction framework and the following RW-DIR algorithm are effective for identifying colon cancer-related RNAs, and this kind of analysis framework can also be easily extended to other diseases, effectively narrowing the scope of biological experimental research. Full article

(This article belongs to the Special Issue Intelligent Computing in Biology and Medicine)

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14 pages, 1211 KiB

Open AccessArticle

Predicting Protein–Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence

by Xinke Zhan, Mang Xiao, Zhuhong You, Chenggang Yan, Jianxin Guo, Liping Wang, Yaoqi Sun and Bingwan Shang

Biology 2022, 11(7), 995; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11070995 - 30 Jun 2022

Cited by 2 | Viewed by 1530

Abstract

Protein–protein interactions (PPIs) play an essential role in many biological cellular functions. However, it is still tedious and time-consuming to identify protein–protein interactions through traditional experimental methods. For this reason, it is imperative and necessary to develop a computational method for predicting PPIs efficiently. This paper explores a novel computational method for detecting PPIs from protein sequence, the approach which mainly adopts the feature extraction method: Locality Preserving Projections (LPP) and classifier: Rotation Forest (RF). Specifically, we first employ the Position Specific Scoring Matrix (PSSM), which can remain evolutionary information of biological for representing protein sequence efficiently. Then, the LPP descriptor is applied to extract feature vectors from PSSM. The feature vectors are fed into the RF to obtain the final results. The proposed method is applied to two datasets: Yeast and H. pylori, and obtained an average accuracy of 92.81% and 92.56%, respectively. We also compare it with K nearest neighbors (KNN) and support vector machine (SVM) to better evaluate the performance of the proposed method. In summary, all experimental results indicate that the proposed approach is stable and robust for predicting PPIs and promising to be a useful tool for proteomics research. Full article

(This article belongs to the Special Issue Intelligent Computing in Biology and Medicine)

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17 pages, 3876 KiB

Open AccessArticle

Divide-and-Attention Network for HE-Stained Pathological Image Classification

by Rui Yan, Zhidong Yang, Jintao Li, Chunhou Zheng and Fa Zhang

Biology 2022, 11(7), 982; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11070982 - 29 Jun 2022

Cited by 3 | Viewed by 1645

Abstract

Since pathological images have some distinct characteristics that are different from natural images, the direct application of a general convolutional neural network cannot achieve good classification performance, especially for fine-grained classification problems (such as pathological image grading). Inspired by the clinical experience that decomposing a pathological image into different components is beneficial for diagnosis, in this paper, we propose a Divide-and-Attention Network (DANet) for Hematoxylin-and-Eosin (HE)-stained pathological image classification. The DANet utilizes a deep-learning method to decompose a pathological image into nuclei and non-nuclei parts. With such decomposed pathological images, the DANet first performs feature learning independently in each branch, and then focuses on the most important feature representation through the branch selection attention module. In this way, the DANet can learn representative features with respect to different tissue structures and adaptively focus on the most important ones, thereby improving classification performance. In addition, we introduce deep canonical correlation analysis (DCCA) constraints in the feature fusion process of different branches. The DCCA constraints play the role of branch fusion attention, so as to maximize the correlation of different branches and ensure that the fused branches emphasize specific tissue structures. The experimental results of three datasets demonstrate the superiority of the DANet, with an average classification accuracy of 92.5% on breast cancer classification, 95.33% on colorectal cancer grading, and 91.6% on breast cancer grading tasks. Full article

(This article belongs to the Special Issue Intelligent Computing in Biology and Medicine)

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16 pages, 4504 KiB

Open AccessArticle

Detecting Drug–Target Interactions with Feature Similarity Fusion and Molecular Graphs

by Xiaoli Lin, Shuai Xu, Xuan Liu, Xiaolong Zhang and Jing Hu

Biology 2022, 11(7), 967; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11070967 - 27 Jun 2022

Cited by 6 | Viewed by 1679

Abstract

The key to drug discovery is the identification of a target and a corresponding drug compound. Effective identification of drug–target interactions facilitates the development of drug discovery. In this paper, drug similarity and target similarity are considered, and graphical representations are used to extract internal structural information and intermolecular interaction information about drugs and targets. First, drug similarity and target similarity are fused using the similarity network fusion (SNF) method. Then, the graph isomorphic network (GIN) is used to extract the features with information about the internal structure of drug molecules. For target proteins, feature extraction is carried out using TextCNN to efficiently capture the features of target protein sequences. Three different divisions (CVD, CVP, CVT) are used on the standard dataset, and experiments are carried out separately to validate the performance of the model for drug–target interaction prediction. The experimental results show that our method achieves better results on AUC and AUPR. The docking results also show the superiority of the proposed model in predicting drug–target interactions. Full article

(This article belongs to the Special Issue Intelligent Computing in Biology and Medicine)

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13 pages, 1130 KiB

Open AccessArticle

SMMDA: Predicting miRNA-Disease Associations by Incorporating Multiple Similarity Profiles and a Novel Disease Representation

by Bo-Ya Ji, Liang-Rui Pan, Ji-Ren Zhou, Zhu-Hong You and Shao-Liang Peng

Biology 2022, 11(5), 777; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11050777 - 20 May 2022

Cited by 1 | Viewed by 1576

Abstract

Increasing evidence has suggested that microRNAs (miRNAs) are significant in research on human diseases. Predicting possible associations between miRNAs and diseases would provide new perspectives on disease diagnosis, pathogenesis, and gene therapy. However, considering the intrinsic time-consuming and expensive cost of traditional Vitro studies, there is an urgent need for a computational approach that would allow researchers to identify potential associations between miRNAs and diseases for further research. In this paper, we presented a novel computational method called SMMDA to predict potential miRNA-disease associations. In particular, SMMDA first utilized a new disease representation method (MeSHHeading2vec) based on the network embedding algorithm and then fused it with Gaussian interaction profile kernel similarity information of miRNAs and diseases, disease semantic similarity, and miRNA functional similarity. Secondly, SMMDA utilized a deep auto-coder network to transform the original features further to achieve a better feature representation. Finally, the ensemble learning model, XGBoost, was used as the underlying training and prediction method for SMMDA. In the results, SMMDA acquired a mean accuracy of 86.68% with a standard deviation of 0.42% and a mean AUC of 94.07% with a standard deviation of 0.23%, outperforming many previous works. Moreover, we also compared the predictive ability of SMMDA with different classifiers and different feature descriptors. In the case studies of three common Human diseases, the top 50 candidate miRNAs have 47 (esophageal neoplasms), 48 (breast neoplasms), and 48 (colon neoplasms) are successfully verified by two other databases. The experimental results proved that SMMDA has a reliable prediction ability in predicting potential miRNA-disease associations. Therefore, it is anticipated that SMMDA could be an effective tool for biomedical researchers. Full article

(This article belongs to the Special Issue Intelligent Computing in Biology and Medicine)

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13 pages, 1816 KiB

Open AccessArticle

A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences

by Jie Pan, Shiwei Wang, Changqing Yu, Liping Li, Zhuhong You and Yanmei Sun

Biology 2022, 11(5), 775; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11050775 - 19 May 2022

Cited by 2 | Viewed by 1732

Abstract

Protein–protein interactions (PPIs) are crucial for understanding the cellular processes, including signal cascade, DNA transcription, metabolic cycles, and repair. In the past decade, a multitude of high-throughput methods have been introduced to detect PPIs. However, these techniques are time-consuming, laborious, and always suffer from high false negative rates. Therefore, there is a great need of new computational methods as a supplemental tool for PPIs prediction. In this article, we present a novel sequence-based model to predict PPIs that combines Discrete Hilbert transform (DHT) and Rotation Forest (RoF). This method contains three stages: firstly, the Position-Specific Scoring Matrices (PSSM) was adopted to transform the amino acid sequence into a PSSM matrix, which can contain rich information about protein evolution. Then, the 400-dimensional DHT descriptor was constructed for each protein pair. Finally, these feature descriptors were fed to the RoF classifier for identifying the potential PPI class. When exploring the proposed model on the Yeast, Human, and Oryza sativa PPIs datasets, it yielded excellent prediction accuracies of 91.93, 96.35, and 94.24%, respectively. In addition, we also conducted numerous experiments on cross-species PPIs datasets, and the predictive capacity of our method is also very excellent. To further access the prediction ability of the proposed approach, we present the comparison of RoF with four powerful classifiers, including Support Vector Machine (SVM), Random Forest (RF), K-nearest Neighbor (KNN), and AdaBoost. We also compared it with some existing superiority works. These comprehensive experimental results further confirm the excellent and feasibility of the proposed approach. In future work, we hope it can be a supplemental tool for the proteomics analysis. Full article

(This article belongs to the Special Issue Intelligent Computing in Biology and Medicine)

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21 pages, 3121 KiB

Open AccessArticle

BioChemDDI: Predicting Drug–Drug Interactions by Fusing Biochemical and Structural Information through a Self-Attention Mechanism

by Zhong-Hao Ren, Chang-Qing Yu, Li-Ping Li, Zhu-Hong You, Jie Pan, Yong-Jian Guan and Lu-Xiang Guo

Biology 2022, 11(5), 758; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11050758 - 16 May 2022

Cited by 3 | Viewed by 2526

Abstract

During the development of drug and clinical applications, due to the co-administration of different drugs that have a high risk of interfering with each other’s mechanisms of action, correctly identifying potential drug–drug interactions (DDIs) is important to avoid a reduction in drug therapeutic activities and serious injuries to the organism. Therefore, to explore potential DDIs, we develop a computational method of integrating multi-level information. Firstly, the information of chemical sequence is fully captured by the Natural Language Processing (NLP) algorithm, and multiple biological function similarity information is fused by Similarity Network Fusion (SNF). Secondly, we extract deep network structure information through Hierarchical Representation Learning for Networks (HARP). Then, a highly representative comprehensive feature descriptor is constructed through the self-attention module that efficiently integrates biochemical and network features. Finally, a deep neural network (DNN) is employed to generate the prediction results. Contrasted with the previous supervision model, BioChemDDI innovatively introduced graph collapse for extracting a network structure and utilized the biochemical information during the pre-training process. The prediction results of the benchmark dataset indicate that BioChemDDI outperforms other existing models. Moreover, the case studies related to three cancer diseases, including breast cancer, hepatocellular carcinoma and malignancies, were analyzed using BioChemDDI. As a result, 24, 18 and 20 out of the top 30 predicted cancer-related drugs were confirmed by the databases. These experimental results demonstrate that BioChemDDI is a useful model to predict DDIs and can provide reliable candidates for biological experiments. The web server of BioChemDDI predictor is freely available to conduct further studies. Full article

(This article belongs to the Special Issue Intelligent Computing in Biology and Medicine)

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13 pages, 2379 KiB

Open AccessArticle

RoFDT: Identification of Drug–Target Interactions from Protein Sequence and Drug Molecular Structure Using Rotation Forest

by Ying Wang, Lei Wang, Leon Wong, Bowei Zhao, Xiaorui Su, Yang Li and Zhuhong You

Biology 2022, 11(5), 741; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11050741 - 13 May 2022

Cited by 4 | Viewed by 2567

Abstract

As the basis for screening drug candidates, the identification of drug–target interactions (DTIs) plays a crucial role in the innovative drugs research. However, due to the inherent constraints of small-scale and time-consuming wet experiments, DTI recognition is usually difficult to carry out. In the present study, we developed a computational approach called RoFDT to predict DTIs by combining feature-weighted Rotation Forest (FwRF) with a protein sequence. In particular, we first encode protein sequences as numerical matrices by Position-Specific Score Matrix (PSSM), then extract their features utilize Pseudo Position-Specific Score Matrix (PsePSSM) and combine them with drug structure information-molecular fingerprints and finally feed them into the FwRF classifier and validate the performance of RoFDT on Enzyme, GPCR, Ion Channel and Nuclear Receptor datasets. In the above dataset, RoFDT achieved 91.68%, 84.72%, 88.11% and 78.33% accuracy, respectively. RoFDT shows excellent performance in comparison with support vector machine models and previous superior approaches. Furthermore, 7 of the top 10 DTIs with RoFDT estimate scores were proven by the relevant database. These results demonstrate that RoFDT can be employed to a powerful predictive approach for DTIs to provide theoretical support for innovative drug discovery. Full article

(This article belongs to the Special Issue Intelligent Computing in Biology and Medicine)

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14 pages, 1329 KiB

Open AccessArticle

MSPEDTI: Prediction of Drug–Target Interactions via Molecular Structure with Protein Evolutionary Information

by Lei Wang, Leon Wong, Zhan-Heng Chen, Jing Hu, Xiao-Fei Sun, Yang Li and Zhu-Hong You

Biology 2022, 11(5), 740; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11050740 - 13 May 2022

Cited by 4 | Viewed by 3573

Abstract

The key to new drug discovery and development is first and foremost the search for molecular targets of drugs, thus advancing drug discovery and drug repositioning. However, traditional drug–target interactions (DTIs) is a costly, lengthy, high-risk, and low-success-rate system project. Therefore, more and more pharmaceutical companies are trying to use computational technologies to screen existing drug molecules and mine new drugs, leading to accelerating new drug development. In the current study, we designed a deep learning computational model MSPEDTI based on Molecular Structure and Protein Evolutionary to predict the potential DTIs. The model first fuses protein evolutionary information and drug structure information, then a deep learning convolutional neural network (CNN) to mine its hidden features, and finally accurately predicts the associated DTIs by extreme learning machine (ELM). In cross-validation experiments, MSPEDTI achieved 94.19%, 90.95%, 87.95%, and 86.11% prediction accuracy in the gold-standard datasets enzymes, ion channels, G-protein-coupled receptors (GPCRs), and nuclear receptors, respectively. MSPEDTI showed its competitive ability in ablation experiments and comparison with previous excellent methods. Additionally, 7 of 10 potential DTIs predicted by MSPEDTI were substantiated by the classical database. These excellent outcomes demonstrate the ability of MSPEDTI to provide reliable drug candidate targets and strongly facilitate the development of drug repositioning and drug development. Full article

(This article belongs to the Special Issue Intelligent Computing in Biology and Medicine)

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Review

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20 pages, 2673 KiB

Open AccessReview

by Bahaj Adil, Safae Lhazmir, Mounir Ghogho and Houda Benbrahim

Biology 2022, 11(8), 1221; https://0-doi-org.brum.beds.ac.uk/10.3390/biology11081221 - 16 Aug 2022

Cited by 1 | Viewed by 1608

Abstract

The urgency of the COVID-19 pandemic caused a surge in the related scientific literature. This surge made the manual exploration of scientific articles time-consuming and inefficient. Therefore, a range of exploratory search applications have been created to facilitate access to the available literature. [...] Read more.

(This article belongs to the Special Issue Intelligent Computing in Biology and Medicine)

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