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Editorial: computational Genomics and Molecular Medicine for Emerging COVID-19

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第4期18卷 1227-1229页

作者： Wei, Dong-Qing Kaushik, Aman Chandra Selvaraj, Gurudeeban Pan, Yi Shanghai Jiao Tong Univ State Key Lab Microbial Metab Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Life Sci & Biotechnol Shanghai 200240 Peoples R China Peng Cheng Lab Shenzhen 518055 Guangdong Peoples R China Jiangnan Univ Wuxi Sch Med Wuxi 214122 Jiangsu Peoples R China Concordia Univ Ctr Res Mol Modeling CERMM Montreal PQ H4B 1R6 Canada Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

The papers in this special section focus on computational genomics and molecular medicine for emerging COVID-19. In 2020, World Health Organization announced Coronavirus disease (COVID)-19 is a pandemic disease, which is devastated the socio-economic life around the world. The disease caused by the zoonotic single-strand RNA virus known as “SARS-CoV-2”. To overcome the pandemic, the diagnosis and therapeutics products needs to be developed in short term. Developing therapeutics for infectious diseases, especially viral diseases always a challenging task for the scientific community. However, the utility of high-performance computational resources, artificial intelligence, and machine-learning algorithms can make the process in an affordable way through the usage of genomics, proteomics, pharmacogenomics, and chemical data. Thus, the special section received potential research articles related to computational genomics, molecular medicine, and COVID-19 from reputed scientist around the world. Different articles were employed machine learning, molecular dynamics, computer aided drug design techniques, and emphasizing viral genomics, mutation, drug target, drug candidates, and patient data, were included in this special section.

关键词： Special Issues And Sections COVID 19 Pandemics Genomics Drugs bioinformatics Machine Learning RNA Proteomics Molecular biology Coronaviruses

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GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第1期19卷 491-500页

作者： Muslu, Oezlem Hoyt, Charles Tapley Lacerda, Mauricio Hofmann-Apitius, Martin Froehlich, Holger Fraunhofer Inst Algorithms & Sci Comp SCAI Business Area Bioinformat D-53754 Schloss Birlinghoven Sankt Augustin Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT D-53115 Bonn Germany UCB Biosci GmbH Alfred Nobel Str 1040789 Monheim Germany

The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potential targets into the context of a disease specific network reconstruction. The purpose of this work was to investigate whether network representation learning techniques could be used to allow for a machine learning based prioritization of putative targets. We propose a novel target prioritization approach, GuiltyTargets, which relies on attributed network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled (PU) machine learning for candidate ranking. We evaluated our approach on 12 datasets from six diseases of different type (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.97, significantly outperforming previous approaches that relied on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. An application of GuiltyTargets to Alzheimer's disease resulted in a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinson's disease, highlighting the potential for target repositioning with our method. The GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://***/GuiltyTargets. Attributed network representation learning techniques provide an interesting approach to effectively leverage the existing knowledge about the molecular mechanisms in different diseases. In this work, the combination with positive-unlabeled learning for target

关键词： Artificial intelligence neural networks bioinformatics

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Orienting Conflicted Graph Edges Using Genetic Algorithms to Discover Pathways in Protein-Protein Interaction Networks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1970-1985页

作者： Iqbal, Shahid Halim, Zahid Monash Univ Fac Informat Technol Data Sci & AI Grp Melbourne Vic 3800 Australia Ghulam Ishaq Khan Inst Engn Sci & Technol Machine Intelligence Res Grp MInG Fac Comp Sci & Engn Topi 23460 Khyber Pakhtunk Pakistan

Advanced computational techniques of the current era help to identify proteins from the complex biological network that interact with each other and with the cell's environment. Biological pathways are a chain of molecular actions that leads to a new molecular product creation or alters the cellular state. These pathways are helpful in the predication of many real-world issues. Rebuilding these pathways is a challenging task due to the fact that protein interactions are undirected, whereas pathways are directed. To discover these pathways in protein-protein interaction data from specified source and target, it is essential to orient protein interactions. Unfortunately, the edge orientation problem is NP-hard, which makes it challenging to develop effective algorithms. This work rebuilds biologically important pathways in a weighted network of protein interactions of yeast species. The proposed algorithm, pseudo-guided multi-objective genetic algorithm (PGMOGA) rebuilds pathways by assigning orientation to the edges of the weighted network. Extending the past research, mathematical modeling of single-objective and multi-objective functions is performed. The PGMOGA is compared with four state-of-the-art approaches, namely, random orientation plus local search (ROLS), single-objective genetic algorithm (SOGA), multi-objective genetic algorithm (MOGA), and multi random search (MRS). The comparison is based on three general and four path specific metrics. Results show that the current proposal performs better.

关键词： Proteins bioinformatics biology Genetic algorithms Task analysis Organisms Genomics Evolutionary algorithm gene network protein pathway identification

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A Prototype System Design for Identification and Classification of Stimulation Sites Inducing Hand Motor Function

A Prototype System Design for Identification and Classificat...

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ieee International Conference on bioinformatics and computational biology (ICBCB)

作者： Gong Chen Ning Tian Xueliang Bao Shiqin Liu key laboratory of artificial intelligence and information security for channeling computing resources from the east to the west School of Information Engineering Ningxia university Yinchuan Ningxia China

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

To address the challenges of surface electrical stimulation using large electrodes, which are not capable of selectively activating muscles and may induce varying muscle responses with variations in body posture, this study proposed a system including hardware platform and a PC software, which can identify and classify stimulation sites. The system's hardware platform is a surface functional electrical stimulation (FES) device with array electrodes, while the PC software is used for stimulation parameter configuration and limb movement trajectory data acquisition based on Leap Motion and ESP8266. To test the system's performance, wrist extension and flexion movements were performed. The results demonstrate that the system can establish parameters for stimulation sites in the array electrodes and capture the motion trajectories of wrist extension and wrist flexion in real-time during limb movement. This system establishes a platform for creating a database of limb movement trajectories and identifying and categorizing stimulation sites for target movements in various body postures using deep learning algorithms.

关键词： Electrodes Wrist Training Limbs Muscles Iron Hardware Trajectory Arrays Neuromuscular stimulation

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Multi-Scale Double-Layer Multiple Instance Learning Algorithm for Whole Slide Image Classification of Primary Gastric Lymphoma

Multi-Scale Double-Layer Multiple Instance Learning Algorith...

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ieee International Conference on bioinformatics and computational biology (ICBCB)

作者： Jiawei Quan Jingxuan Ye Xinlin Zhang Ziwei Hu Yongxin Mao Yuntao Liu Tong Tong Gang Chen College of Physics and Information Engineering Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Translational Cancer Medicine Fujian Medical University Fuzhou China

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Primary Gastric Lymphoma (PGL) is a type of malignant lymphoma that originates in the stomach. Timely and precise diagnosis and classification are crucial for effective treatment and predicting outcomes. Advances in artificial intelligence, especially deep learning, have ushered in a new era for medical diagnostics. Weakly supervised learning, in particular, is gaining traction for classifying histopathological whole slide images (WSIs). Nonetheless, there remain challenges, such as the classification of small sample cohorts. To address these issues, we propose a novel multi-scale double-layer multiple instance learning classification algorithm for accurate classification of PGL. This algorithm leverages pseudo-bags-a technique to effectively augment the sample size-and employs multiple instance learning to distill essential features. It then constructs an enhanced classification model utilizing a multi-scale, double-layer approach. Validation using clinical pathological data indicates that our method outperforms other latest methods in classifying primary gastric lymphoma, as well as obtains superior results on the CAMELYON-16 dataset. Our framework holds promise for broader adoption across various medical applications.

关键词： Deep learning Stomach Pathology Accuracy Weak supervision Learning (artificial intelligence) Feature extraction Classification algorithms Medical diagnosis Cancer

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Visualizing the knowledge structure and evolution of bioinformatics

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BMC bioinformatics 2022年第SUPPL 8期23卷 404-404页

作者： Wang, Jiaqi Li, Zeyu Zhang, Jiawan Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

Background bioinformatics has gained much attention as a fast growing interdisciplinary field. Several attempts have been conducted to explore the field of bioinformatics by bibliometric analysis, however, such works did not elucidate the role of visualization in analysis, nor focus on the relationship between sub-topics of bioinformatics. Results First, the hotspot of bioinformatics has moderately shifted from traditional molecular biology to omics research, and the computational method has also shifted from mathematical model to data mining and machine learning. Second, DNA-related topics are bridge topics in bioinformatics research. These topics gradually connect various sub-topics that are relatively independent at first. Third, only a small part of topics we have obtained involves a number of computational methods, and the other topics focus more on biological aspects. Fourth, the proportion of computing-related topics hit a trough in the 1980s. During this period, the use of traditional calculation methods such as mathematical model declined in a large proportion while the new calculation methods such as machine learning have not been applied in a large scale. This proportion began to increase gradually after the 1990s. Fifth, although the proportion of computing-related topics is only slightly higher than the original, the connection between other topics and computing-related topics has become closer, which means the support of computational methods is becoming increasingly important for the research of bioinformatics. Conclusions The results of our analysis imply that research on bioinformatics is becoming more diversified and the ranking of computational methods in bioinformatics research is also gradually improving.

关键词： bioinformatics Visualization Knowledge structure Text-mining

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EpiMC: Detecting Epistatic Interactions Using Multiple Clusterings

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第1期19卷 243-254页

作者： Wang, Jun Zhang, Huiling Ren, Wei Guo, Maozu Yu, Guoxian Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res C F Jinan 250101 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China Shandong Univ Sch Software Jinan 250101 Peoples R China

Detecting single nucleotide polymorphisms (SNPs) interactions is crucial to identify susceptibility genes associated with complex human diseases in genome-wide association studies. Clustering-based approaches are widely used in reducing search space and exploring potential relationships between SNPs in epistasis analysis. However, these approaches all only use a single measure to filter out nonsignificant SNP combinations, which may be significant ones from another perspective. In this paper, we propose a two-stage approach named EpiMC (Epistatic Interactions detection based on Multiple Clusterings) that employs multiple clusterings to obtain more precise candidate sets and more comprehensively detect high-order interactions based on these sets. In the first stage, EpiMC proposes a matrix factorization based multiple clusterings algorithm to generate multiple diverse clusterings, each of which divide all SNPs into different clusters. This stage aims to reduce the chance of filtering out potential candidates overlooked by a single clustering and groups associated SNPs together from different clustering perspectives. In the next stage, EpiMC considers both the single-locus effects and interaction effects to select high-quality disease associated SNPs, and then uses Jaccard similarity to get candidate sets. Finally, EpiMC uses exhaustive search on the obtained small candidate sets to precisely detect epsitatic interactions. Extensive simulation experiments show that EpiMC has a better performance in detecting high-order interactions than state-of-the-art solutions. On the Wellcome Trust Case Control Consortium (WTCCC) dataset, EpiMC detects several significant epistatic interactions associated with breast cancer (BC) and age-related macular degeneration (AMD), which again corroborate the effectiveness of EpiMC.

关键词： Genome-wide association study epistatic interactions stepwise search multiple clusterings

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Supervised Graph Clustering for Cancer Subtyping Based on Survival Analysis and Integration of Multi-Omic Tumor Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第2期19卷 1193-1202页

作者： Liu, Cheng Cao, Wenming Wu, Si Shen, Wenjun Jiang, Dazhi Yu, Zhiwen Wong, Hau-San Shantou Univ Dept Comp Sci Shantou 515063 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China Shantou Univ Med Coll Shantou 515041 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Identifying cancer subtypes by integration of multi-omic data is beneficial to improve the understanding of disease progression, and provides more precise treatment for patients. Cancer subtypes identification is usually accomplished by clustering patients with unsupervised learning approaches. Thus, most existing integrative cancer subtyping methods are performed in an entirely unsupervised way. An integrative cancer subtyping approach can be improved to discover clinically more relevant cancer subtypes when considering the clinical survival response variables. In this study, we propose a Survival Supervised Graph Clustering (S2GC)for cancer subtyping by taking into consideration survival information. Specifically, we use a graph to represent similarity of patients, and develop a multi-omic survival analysis embedding with patient-to-patient similarity graph learning for cancer subtype identification. The multi-view (omic)survival analysis model and graph of patients are jointly learned in a unified way. The learned optimal graph can be unitized to cluster cancer subtypes directly. In the proposed model, the survival analysis model and adaptive graph learning could positively reinforce each other. Consequently, the survival time can be considered as supervised information to improve the quality of the similarity graph and explore clinically more relevant subgroups of patients. Experiments on several representative multi-omic cancer datasets demonstrate that the proposed method achieves better results than a number of state-of-the-art methods. The results also suggest that our method is able to identify biologically meaningful subgroups for different cancer types. (Our Matlab source code is available online at github: https://***/CLiu272/S2GC)

关键词： Cancer Analytical models Adaptation models Optimization bioinformatics Silicon Multi-omic data Cancer subtype identification survival analysis data integration

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PDGNet: Predicting Disease Genes Using a Deep Neural Network With Multi-View Features

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第1期19卷 575-584页

作者： Yang, Kuo Zheng, Yi Lu, Kezhi Chang, Kai Wang, Ning Shu, Zixin Yu, Jian Liu, Baoyan Gao, Zhuye Zhou, Xuezhong Beijing Jiaotong Univ Sch Comp & Informat Technol Inst Med Intelligence Beijing 100044 Peoples R China Tsinghua Univ BNRIST Dept Automat Beijing 10084 Peoples R China Beijing Jiaotong Univer Sity Beijing Key Lab Traff Data Anal & Min Sch Comp & Informat Technol Beijing 100044 Peoples R China China Acad Chinese Med Sci Data Ctr Tradit Chinese Med Beijing 100700 Peoples R China China Acad China Acad Chinese Med Sci Xiyuan Hosp Beijing 100091 Peoples R China

The knowledge of phenotype-genotype associations is crucial for the understanding of disease mechanisms. Numerous studies have focused on developing efficient and accurate computing approaches to predict disease genes. However, owing to the sparseness and complexity of medical data, developing an efficient deep neural network model to identify disease genes remains a huge challenge. Therefore, we develop a novel deep neural network model that fuses the multi-view features of phenotypes and genotypes to identify disease genes (termed PDGNet). Our model integrated the multi-view features of diseases and genes and leveraged the feedback information of training samples to optimize the parameters of deep neural network and obtain the deep vector features of diseases and genes. The evaluation experiments on a large data set indicated that PDGNet obtained higher performance than the state-of-the-art method (precision and recall improved by 9.55 and 9.63 percent). The analysis results for the candidate genes indicated that the predicted genes have strong functional homogeneity and dense interactions with known genes. We validated the top predicted genes of Parkinson's disease based on external curated data and published medical literatures, which indicated that the candidate genes have a huge potential to guide the selection of causal genes in the 'wet experiment'. The source codes and the data of PDGNet are available at https://***/yangkuoone/PDGNet.

关键词： Disease gene prediction deep neural network multi-view features

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To Improve Prediction of Binding Residues With DNA, RNA, Carbohydrate, and Peptide Via Multi-Task Deep Neural Networks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第6期19卷 3735-3743页

作者： Sun, Zhe Zheng, Shuangjia Zhao, Huiying Niu, Zhangming Lu, Yutong Pan, Yi Yang, Yuedong Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510000 Guangdong Peoples R China Sun Yat Sen Univ Sun Yat Sen Mem Hosp Guangzhou 510000 Guangdong Peoples R China Aladdin Healthcare Technol Ltd London EC1Y 1AA England Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Sun Yat Sen Univ Key Lab Machine Intelligence & Adv Comp MOE Guangzhou 510000 Guangdong Peoples R China

Motivation: The interactions of proteins with DNA, RNA, peptide, and carbohydrate play key roles in various biological processes. The studies of uncharacterized protein-molecules interactions could be aided by accurate predictions of residues that bind with partner molecules. However, the existing methods for predicting binding residues on proteins remain of relatively low accuracies due to the limited number of complex structures in databases. As different types of molecules partially share chemical mechanisms, the predictions for each molecular type should benefit from the binding information with other molecule types. Results: In this study, we employed a multiple task deep learning strategy to develop a new sequence-based method for simultaneously predicting binding residues/sites with multiple important molecule types named MTDsite. By combining four training sets for DNA, RNA, peptide, and carbohydrate-binding proteins, our method yielded accurate and robust predictions with AUC values of 0.852, 0836, 0.758, and 0.776 on their respective independent test sets, which are 0.52 to 6.6% better than other state-of-the-art methods. To my best knowledge, this is the first method using multi-task framework to predict multiple molecular binding sites simultaneously.

关键词： Proteins RNA DNA Training Feature extraction Peptides Hidden Markov models Protein binding sites nucleic acid molecule carbohydrate peptide LSTM Multi-task Sequence-based approach

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