Article
Biochemistry & Molecular Biology
Mario Angel Lopez-Luis, Eva Elda Soriano-Perez, Jose Carlos Parada-Fabian, Javier Torres, Rogelio Maldonado-Rodriguez, Alfonso Mendez-Tenorio
Summary: CagY is a protein from Helicobacter pylori's T4SS that plays a critical role in gastric inflammation and cancer. Researchers used modeling techniques to study this complex protein and found that the MRR region of CagY may function as a contractile region and modulate tissue inflammation.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Biochemistry & Molecular Biology
Mobeen Ur Rehman, Hilal Tayara, Kil To Chong
Summary: The study presents a neural network-based tool, DCNN-4mC, for identifying DNA N4-methylcytosine (4mC) sites. By combining all available datasets of different species to create a single benchmark dataset for each species, the tool's performance was evaluated on 12 different species. DCNN-4mC achieved higher accuracy compared to state-of-the-art tools on various datasets of different species and demonstrated high performance on independent test datasets as well.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Article
Chemistry, Multidisciplinary
Jon Lundstrom, Emma Korhonen, Frederique Lisacek, Daniel Bojar
Summary: LectinOracle model, combining transformer-based representations for proteins and graph convolutional neural networks for glycans, is able to predict protein-glycan interactions accurately and generalize well to new glycans and lectins. It has various applications in improving lectin classification, accelerating lectin directed evolution, predicting epidemiological outcomes, and analyzing host-microbe interactions.
Article
Biochemistry & Molecular Biology
Yuzhi Guo, Jiaxiang Wu, Hehuan Ma, Sheng Wang, Junzhou Huang
Summary: The significance of protein secondary structure, successful models in protein sequence study area, and novel methods like CondGCNN and ASP network were discussed in this paper. Experimental results showed that the proposed method achieved higher performance in protein secondary structure prediction tasks.
Article
Biochemistry & Molecular Biology
Kaixuan Diao, Jing Chen, Tao Wu, Xuan Wang, Guangshuai Wang, Xiaoqin Sun, Xiangyu Zhao, Chenxu Wu, Jinyu Wang, Huizi Yao, Casimiro Gerarduzzi, Xue-Song Liu
Summary: Seq2Neo is a pipeline that predicts the immunogenicity of neoantigens by providing a solution for neoepitope feature prediction using raw sequencing data. It supports different types of genome DNA alterations and includes a CNN-based model that shows improved performance in immunogenicity prediction compared to currently available tools.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Review
Computer Science, Artificial Intelligence
M. M. Mohamed Mufassirin, M. A. Hakim Newton, Abdul Sattar
Summary: Protein structure prediction is a challenging task in bioinformatics and drug discovery. This review paper provides a comprehensive survey of template-free protein structure prediction research, highlighting the progress, challenges, and future directions.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Cell Biology
Muhammad Shujaat, Hoonjoo Kim, Hilal Tayara, Kil To Chong
Summary: This article presents a convolutional neural network (CNN) based prediction tool called iProm-Sigma54 for predicting s(54) promoters. By comparing with other methods, iProm-Sigma54 outperforms in predicting s(54) promoters and a publicly accessible web server was constructed.
Article
Biochemical Research Methods
Yuhang Liu, Zixuan Wang, Hao Yuan, Guiquan Zhu, Yongqing Zhang
Summary: HEAP is an explainable deep learning framework for predicting enhancers and exploring enhancer grammar. The algorithm accurately predicts enhancer activity in different cell types and provides explanations for the prediction mechanisms, leading to a better understanding of enhancer activity.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Multidisciplinary Sciences
Shaun M. Kandathil, Joe G. Greener, Andy M. Lau, David T. Jones
Summary: The study presents a deep learning-based method for predicting protein structure, which reduces preprocessing time and directly outputs main chain coordinates. The approach is fast, easy to use, and produces accurate structural models. It enables large-scale modeling of proteins on minimal hardware.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Neurosciences
Yanbu Wang, Linqing Liu, Chao Wang
Summary: This literature review focuses on the latest deep learning solutions for medical and healthcare prediction systems, with a specific emphasis on applications in the medical domain. The study categorizes the cutting-edge deep learning approaches and explores their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. The review highlights the forefront advancements in deep learning techniques and their practical applications in medical prediction systems, while addressing the challenges hindering widespread implementation in medical image segmentation. The evaluation metrics employed encompass a broad spectrum of features.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Biochemistry & Molecular Biology
Mihaly Varadi, Nicola Bordin, Christine Orengo, Sameer Velankar
Summary: The function of proteins can be inferred from their three-dimensional structures. The advent of deep learning-based protein structure prediction tools in the early 2020s has had a significant impact on the field of life sciences. These tools offer new opportunities and challenges to the scientific community, and there are potential directions for the future of computational protein structure prediction.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Ying Xia, Chunqiu Xia, Xiaoyong Pan, Hong-Bin Shen
Summary: Knowledge of protein-ligand interactions is crucial for biological process analysis and drug design. In this study, a web server called BindWeb is introduced for predicting ligand binding residues and pockets from protein structures. BindWeb benefits from the complementarity of two base methods, resulting in higher prediction accuracy.
Article
Biochemical Research Methods
Richa Dhanuka, Jyoti Prakash Singh, Anushree Tripathi
Summary: Protein function prediction is a challenging task in bioinformatics, aiming to predict the functions of known proteins. Various forms of protein data, such as sequences, structures, interaction networks, and microarray representations, are used for this purpose. Advanced deep learning techniques have been proposed to utilize the abundant protein sequence data generated in recent decades. A survey is needed to comprehend and present the progress of these techniques in a systematic manner. This survey provides comprehensive details of the latest methodologies, their pros and cons, predictive accuracy, and suggests a new direction for interpretability in protein function prediction systems.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Muhammad Muneeb, Samuel Feng, Andreas Henschel
Summary: This paper investigates the feasibility of using transfer learning for genotype-phenotype prediction. By transferring knowledge from large populations to small populations, the accuracy of prediction can be significantly improved. The results show that transfer learning can create powerful models for genotype-phenotype predictions and apply them to populations with sparse data.
BMC BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Joao Schapke, Anderson Tavares, Mariana Recamonde-Mendoza
Summary: Identifying essential genes and proteins is crucial for understanding human biology and pathology. Existing methods have limitations in predicting gene essentiality, so we propose a graph attention network-based approach that integrates protein-protein interaction networks and multiomics data for accurate prediction.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Zhibin Lv, Hui Ding, Lei Wang, Quan Zou
Summary: N6-methyladenine (m(6)A) is a crucial epigenetic modification related to the control of various DNA processes. The iRicem6A-CNN protocol, using machine learning, achieved high accuracy in identifying m(6)A sites in the rice genome, outperforming other predictors.
Article
Biochemical Research Methods
Zhibin Lv, Pingping Wang, Quan Zou, Qinghua Jiang
Article
Biochemical Research Methods
Zhibin Lv, Feifei Cui, Quan Zou, Lichao Zhang, Lei Xu
Summary: The study introduced a computational method named iACP-DRLF for identifying anticancer peptides, utilizing light gradient boosting machine algorithm and two sequence embedding technologies. Results showed that deep representation learning features significantly enhanced the models' ability to differentiate anticancer peptides.
BRIEFINGS IN BIOINFORMATICS
(2021)
Editorial Material
Biotechnology & Applied Microbiology
Ni Yan, Zhibin Lv, Wenjing Hong, Xue Xu
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Qian Zhao, Jiaqi Ma, Yu Wang, Fang Xie, Zhibin Lv, Yaoqun Xu, Hua Shi, Ke Han
Summary: SNO is crucial for plant immune response and human disease treatment, with the efficient prediction tool Mul-SNO showing promising results.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Jici Jiang, Xinxu Lin, Yueqi Jiang, Liangzhen Jiang, Zhibin Lv
Summary: This study presents the development of a machine learning prediction method called iBitter-DRLF, based on deep learning techniques, to accurately identify bitter peptides. By utilizing deep representation learning, this method can make accurate predictions solely based on peptide sequence data. This is of significant importance for improving the palatability of peptide therapeutics and dietary supplements.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Genetics & Heredity
Mingxin Li, Yu Fan, Yiting Zhang, Zhibin Lv
Summary: The research focused on the impact of different feature information of miRNA sequences on the relationship between miRNA and disease. It found that a better graph neural network prediction model of miRNA-disease relationship could be built using CKSNAP feature, and the predicted miRNAs related to lung tumors, esophageal tumors, and kidney tumors were consistent with the wet experiment validation database.
Article
Genetics & Heredity
Liangzhen Jiang, Changying Liu, Yu Fan, Qi Wu, Xueling Ye, Qiang Li, Yan Wan, Yanxia Sun, Liang Zou, Dabing Xiang, Zhibin Lv
Summary: - This study assessed the transcriptional dynamics of filling stage Tartary buckwheat seeds and identified key genes related to seed development through RNA sequencing. Phytohormones ABA, AUX, ET, BR and CTK, along with related TFs, were found to substantially regulate seed development by targeting downstream expansin genes and structural starch biosynthetic genes. The transcriptome data could serve as a theoretical basis for improving the yield of Tartary buckwheat.
FRONTIERS IN GENETICS
(2022)
Editorial Material
Genetics & Heredity
Zhibin Lv, Mingxin Li, Yansu Wang, Quan Zou
FRONTIERS IN GENETICS
(2023)
Article
Food Science & Technology
Liangzhen Jiang, Jici Jiang, Xiao Wang, Yin Zhang, Bowen Zheng, Shuqi Liu, Yiting Zhang, Changying Liu, Yan Wan, Dabing Xiang, Zhibin Lv
Summary: This study developed a peptide sequence-based umami peptide predictor, iUP-BERT, using a deep learning pretrained neural network feature extraction method. After optimization, the model showed improved performance compared to existing methods. The built iUP-BERT web server can aid in improving the palatability of dietary supplements.
Article
Chemistry, Multidisciplinary
Hongdi Pei, Jiayu Li, Shuhan Ma, Jici Jiang, Mingxin Li, Quan Zou, Zhibin Lv
Summary: Thermophilic proteins have the potential to be used as biocatalysts in biotechnology. BertThermo, a model using BERT as an automatic feature extraction tool, achieved high accuracy in identifying thermophilic proteins. It outperformed previous predictive algorithms and demonstrated robustness in various datasets.+
APPLIED SCIENCES-BASEL
(2023)
Article
Food Science & Technology
Jici Jiang, Jiayu Li, Junxian Li, Hongdi Pei, Mingxin Li, Quan Zou, Zhibin Lv
Summary: A deep learning method called iUmami-DRLF was developed to identify umami peptides based solely on peptide sequence information. The results show that deep learning significantly improved the capability of models in identifying umami peptides. This method can be used to further enhance the umami flavor of food for a satisfying umami-flavored diet.
Article
Biochemistry & Molecular Biology
Yiting Deng, Shuhan Ma, Jiayu Li, Bowen Zheng, Zhibin Lv
Summary: Anticancer peptides (ACPs) are a promising new therapeutic approach in cancer treatment, as they can selectively target cancer cells. This study utilized machine learning algorithms to predict potential ACP sequences based on physicochemical features extracted from peptide sequences. By using feature selection methods, 19 key amino acid physicochemical properties were identified that can predict the likelihood of a peptide sequence functioning as an ACP. The study aims to enhance the efficiency of designing peptide sequences for cancer treatment.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Chemistry, Multidisciplinary
Jiayu Li, Jici Jiang, Hongdi Pei, Zhibin Lv
Summary: A new IL-10-induced peptide recognition method called IL10-Stack was introduced in this research, which utilized unified deep representation learning and a stacking algorithm. Feature extraction from peptide sequences was done using two approaches, Amino Acid Index (AAindex) and sequence-based unified representation (UniRep). The IL10-Stack model, constructed using a 1900-dimensional UniRep feature vector, demonstrated excellent performance in IL-10-induced peptide recognition with an accuracy of 0.910 and MCC of 0.820. Compared to existing methods, IL-10Pred and ILeukin10Pred, the IL10-Stack approach showed improved accuracy by 12.1% and 2.4% respectively. The IL10-Stack method has the potential to identify IL-10-induced peptides, aiding in the development of immunosuppressive drugs.
APPLIED SCIENCES-BASEL
(2023)
Review
Multidisciplinary Sciences
Jiayu Li, Shuhan Ma, Hongdi Pei, Jici Jiang, Quan Zou, Zhibin Lv
Summary: This review focuses on the development of Tcprs for solid tumor therapy and prognostic prediction, and proposes strategies to enhance CAR-T cells through targeting different Tcprs, which may lead to the development of a new generation of cell therapies.