Review
Biochemical Research Methods
Bernardina Scafuri, Anna Verdino, Nancy D'Arminio, Anna Marabotti
Summary: Pharmacological chaperones are compounds that can bind and stabilize proteins, preventing denaturation and degradation. Some of these compounds have been approved or are being investigated for the treatment of rare genetic metabolic disorders. Computational methods can assist in the search for these compounds and have been successfully applied in the discovery of promising molecules in this category.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Computer Science, Software Engineering
Devika Sondhi, Mayank Jobanputra, Divya Rani, Salil Purandare, Sakshi Sharma, Rahul Purandare
Summary: Developers may choose to implement different libraries based on various factors, and the study finds that there may be overlapping functionalities among different libraries, while mining test suites can help uncover defects in programs.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2021)
Review
Pharmacology & Pharmacy
Jing Yan, Ruobing Wang, Jianjun Tan
Summary: Mutations and dysregulation of lncRNAs play a significant role in the development of complex human diseases. Predicting new potential LDAs can aid in understanding disease pathogenesis, detecting disease markers, and improving disease diagnosis, prevention, and treatment. Computational methods have proven to be effective in narrowing down the screening scope of biological experiments, reducing their duration and cost. This review highlights recent advances in computational methods for predicting LDAs, including LDA databases, lncRNA/disease similarity calculations, and advanced computational models. The limitations of various computational models are analyzed, and future challenges and directions for development are discussed.
DRUG DISCOVERY TODAY
(2023)
Article
Chemistry, Analytical
Hoi-Ting Wu, Dylan L. Riggs, Yana A. Lyon, Ryan R. Julian
Summary: Isomeric molecules pose challenges for many analytical methods, including mass spectrometry. In this study, a framework is presented for comparing mass spectra that differ only in terms of peak intensity, allowing for confident identification of peptide isomers. The method can accommodate changes in instrumental settings and enable quantification of isomeric mixtures using calibration curves. This framework may also prove useful in other contexts where similar mass spectra are generated.
ANALYTICAL CHEMISTRY
(2023)
Review
Biochemical Research Methods
Qiu Xiao, Jianhua Dai, Jiawei Luo
Summary: This review discusses the relationship between circular RNAs (circRNAs) and complex human diseases, particularly their involvement in cancer progression. It highlights the functions and characteristics of circRNAs, introduces representative circRNAs related to tumorigenesis, and investigates available databases and tools for circRNA-disease studies. The review also comprehensively reviews computational methods for predicting circRNA-disease associations and discusses challenges and future research in the field.
BRIEFINGS IN BIOINFORMATICS
(2022)
Review
Biotechnology & Applied Microbiology
Yingxin Kan, Limin Jiang, Jijun Tang, Yan Guo, Fei Guo
Summary: Abnormal changes in driver genes have serious implications for human health and biomedical research. Computational methods prove to be more efficient than traditional biological experiments in distinguishing driver genes from massive data. This study summarizes eight common computational algorithms using only somatic mutation data, categorizes them by mutation features, and presents a general process for nominating candidate cancer driver genes. Evaluation of three representative methods on various cancers from different sources is conducted, comparing results with different parameters for a systematic view of mutation features and a foundation for future integration of mutation data with other types of information.
BRIEFINGS IN FUNCTIONAL GENOMICS
(2021)
Article
Multidisciplinary Sciences
Deisy Morselli Gysi, Italo do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Susan Dina Ghiassian, J. J. Patten, Robert A. Davey, Joseph Loscalzo, Albert-Laszlo Barabasi
Summary: The study utilized multiple algorithms to rank a large number of drugs, finding that combining the consensus of various predictive methods significantly improves the success rate of drug screening and proposes new medications for treating COVID-19. Furthermore, it was discovered that the majority of drugs successfully reducing viral infection do not bind to the target proteins of SARS-CoV-2, suggesting a network-based mechanism rather than docking strategies.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Environmental Sciences
Wei Hua, Miaole Hou, Yunfei Qiao, Xuesheng Zhao, Shishuo Xu, Songnian Li
Summary: Grottoes with statues often have similar geometric forms and artistic styles, and identifying these similarities can provide important references for value recognition, condition assessment, repair, and virtual restoration of statues. This research introduces a similarity index based approach to identify similarities between grotto statues, using hash values and SIFT operator matching similar feature points for repair and reconstruction support. Experimental results confirm the accuracy of the similarity index based approach in screening similar grotto statues.
Article
Biochemistry & Molecular Biology
Yuexu Jiang, Duolin Wang, Weiwei Wang, Dong Xu
Summary: Accurate annotation of protein localization plays a crucial role in understanding protein function. Computational prediction, especially with recent advancements in machine learning, has been a key research area for over two decades. This review paper categorizes main features and algorithms, summarizes existing prediction tools, evaluates their performance, and provides future outlook for protein localization methods.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Article
Chemistry, Physical
Manu Suvarna, Phil Preikschas, Javier Perez-Ramirez
Summary: A machine learning approach is used to predict and rationalize the performance of Rh-Mn-P/SiO2 catalysts, with cohesive energy and alloy formation energy of promoters revealed as significant descriptors.
Article
Computer Science, Information Systems
Kai Yao, Lijun Chang, Jeffrey Xu Yu
Summary: This paper introduces the concept of similar-bicliques and proposes a backtracking algorithm to directly enumerate them, addressing the shortcomings of existing models. The algorithm is empowered by vertex reduction and optimization techniques, and a novel index structure is designed for efficient operation and vertex reduction. Extensive experiments validate the effectiveness and efficiency of the model and algorithms.
PROCEEDINGS OF THE VLDB ENDOWMENT
(2022)
Article
Multidisciplinary Sciences
Satoshi Osaga, Takeshi Kimura, Yasuyuki Okumura, Rina Chin, Makoto Imori, Machiko Minatoya
Summary: The performance of algorithms for identifying cases of severe hypoglycemia in Japanese hospital administrative data was evaluated in this study. By using health insurance claims data and Diagnosis Procedure Combination data, 61 different algorithms were developed to define severe hypoglycemia. The results showed that certain algorithms had moderate performance in identifying severe hypoglycemia cases.
Article
Biochemical Research Methods
Leonardo Castorina, Rokas Petrenas, Kartic Subr, Christopher W. Wood
Summary: With the increasing amount of protein structure data and advances in machine learning, the number of available protein sequence design methods is rapidly expanding. To effectively utilize a design method, understanding its performance nuances and how it varies by design target is crucial. PDBench is introduced as a collection of proteins and standard tests to assess the performance of sequence design methods. Compared to previous benchmarking sets, PDBench aims to maximize the structural diversity of the benchmark to provide valuable biological insights into the behavior of sequence design methods, which is essential for evaluating their performance and practical utility. We believe these tools are valuable for guiding the development of novel sequence design algorithms and assisting users in choosing a method that best suits their design target.
Article
Biochemical Research Methods
Xifang Sun, Donglin Wang, Jiaqiang Zhu, Shiquan Sun
Summary: In this paper, a completely nonparametric, statistically straightforward, and interpretable method for detecting differentially methylated regions (DMR) is proposed. Compared with existing methods, this method does not rely on model assumptions and is easy to implement, making it a competitive alternative for defining DMR.
BMC BIOINFORMATICS
(2022)
Article
Immunology
Wei-bo Gao, Li-juan Hu, Xiao-lu Ma, Mao-jing Shi, Chun-yu Wang, Yong Ma, Xiao-jing Song, Ji-hong Zhu, Tian-bing Wang
Summary: This study aimed to establish a predictive model for the timely diagnosis of the original disease resulting in secondary HLH by validating clinical and laboratory findings. The results showed that lower levels of hemoglobin and platelets, lower levels of ferritin, splenomegaly, and Epstein-Barr virus positivity were associated with hematologic disease, while young age and female sex were associated with rheumatic disease. The established predictive model can assist clinicians in diagnosing the underlying disease leading to secondary HLH during routine practice.
FRONTIERS IN IMMUNOLOGY
(2023)
Letter
Multidisciplinary Sciences
Zijun Zhu, Xinyu Chen, Chao Wang, Liang Cheng
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Biochemical Research Methods
Junjie Wang, Jie Hu, Huiting Sun, MengDie Xu, Yun Yu, Yun Liu, Liang Cheng
Summary: In this study, we propose a multigranularity protein-ligand interaction model, which utilizes the Transformer model and convolutional neural network to accurately predict the binding affinity between drugs and protein targets.
Article
Genetics & Heredity
Zhenlei Liu, Huakang Du, Hengqiang Zhao, Siyi Cai, Sen Zhao, Yuchen Niu, Xiaoxin Li, Bowen Liu, Yingzhao Huang, Jiashen Shao, Lian Liu, Ye Tian, Zhihong Wu, Hao Wu, Yue Hu, Terry Jianguo Zhang, Fengzeng Jian, Nan Wu
Summary: In this study, the genetic basis of craniovertebral junction (CVJ) malformation was comprehensively dissected using exome sequencing and copy number variation analysis. Several disease-associated genes and biological pathways, including extracellular matrix and RHO GTPase pathways, were identified to be involved in the pathogenesis of CVJ malformation.
Article
Biochemistry & Molecular Biology
Changlu Qi, Yiting Cai, Kai Qian, Xuefeng Li, Jialiang Ren, Ping Wang, Tongze Fu, Tianyi Zhao, Liang Cheng, Lei Shi, Xue Zhang
Summary: The gut microbiota plays a crucial role in maintaining health, and disruptions can lead to disorders. The gutMDisorder database provides a valuable resource for studying dysbiosis, and the latest version offers expanded data and improved features.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Genetics & Heredity
Ji Li, Jiayue Qiu, Junwei Han, Xiangmei Li, Ying Jiang
Summary: This study found a significant association between tumor microenvironment (TME) infiltration and survival rates and treatment outcomes in breast cancer patients. By developing a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer patients, it was discovered that patients in the IPRS-low group had significantly better overall survival rates compared to the IPRS-high group. Furthermore, IPRS-low patients exhibited a strong immune response and were enriched in multiple immune-associated signaling pathways.
Letter
Gastroenterology & Hepatology
Zijun Zhu, Xinyu Chen, Chao Wang, Sainan Zhang, Liang Cheng
Article
Medicine, Research & Experimental
Jiayue Qiu, Xiangmei Li, Yalan He, Qian Wang, Ji Li, Jiashuo Wu, Ying Jiang, Junwei Han
Summary: This study aimed to identify biomarkers for predicting the efficacy of immune checkpoint blockade therapy. The researchers found that the comutation of the Spliceosome pathway and Hedgehog signaling pathway, in combination with PD-L1 expression, could effectively predict the therapeutic benefit of immunotherapy.
JOURNAL OF TRANSLATIONAL MEDICINE
(2022)
Article
Genetics & Heredity
Ji Li, Jiashuo Wu, Junwei Han
Summary: Through extensive analysis of the multi-omics dataset of breast cancer from the METABRIC cohort, we found that different breast cancer subtypes exhibit different tumor microenvironment heterogeneity. Basal-like and HER2-enriched subtypes are associated with high immune scores, expression of most immune regulatory targets, and immune cell infiltration, suggesting that these subtypes could be defined as immune hot tumors and suitable for immune checkpoint blockade therapy. In contrast, Luminal A and Luminal B subtypes are associated with low immune scores and immune cell infiltration, suggesting that these subtypes could be defined as immune cold tumors. Additionally, the Normal-like subtype has relatively high levels of both immune and stromal features, which indicates that the Normal-like subtype may be suitable for more diverse treatment strategies.
Article
Biochemistry & Molecular Biology
Wei Wang, Haiyan Yuan, Junwei Han, Wei Liu
Summary: Risk gene identification has been a significant focus in the past two decades. This study proposes a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes, which extends the understanding of the molecular mechanism of cancer. Experimental results show that PCLassoLog outperforms other models and identifies risk protein complexes with individual risk proteins and synergistic partners.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Biochemical Research Methods
Yuqi Sheng, Jiashuo Wu, Xiangmei Li, Jiayue Qiu, Ji Li, Qinyu Ge, Liang Cheng, Junwei Han
Summary: Researchers developed a novel computational method called iATMEcell to identify abnormal tumor microenvironment (TME) cells associated with biological outcomes. They manually collected TME cell types and their corresponding gene signatures, constructed a weighted cell-cell crosstalk network, and used network propagation algorithm to identify significantly dysregulated TME cells.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biology
Yinchun Su, Jiashuo Wu, Xiangmei Li, Ji Li, Xilong Zhao, Bingyue Pan, Junling Huang, Qingfei Kong, Junwei Han
Summary: In order to identify potential drugs against COVID-19, researchers have developed a computational approach called DTSEA. This method effectively ranks genes and performs enrichment analysis on drug target sets to predict candidate drugs. The DTSEA method has shown high accuracy and reliability in predicting potential drugs for COVID-19.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Virology
Zijun Zhu, Xinyu Chen, Chao Wang, Sainan Zhang, Rui Yu, Yubin Xie, Shuofeng Yuan, Liang Cheng, Lei Shi, Xue Zhang
Summary: This study conducted a genome-wide association study (GWAS) to identify host genetic factors associated with COVID-19. The correlation between genetic variations and gene expression was assessed using expression quantitative trait locus (eQTL) analysis. The findings revealed 20 genes significantly associated with immunity and neurological disorders. Single-cell datasets were used to validate these findings and to explore the causal relationship between COVID-19 and neurological disorders. Cell experiments were conducted to investigate the effects of COVID-19-related protein-coding genes. This study provides important insights into the genetic architecture underlying the pathophysiology of COVID-19.
JOURNAL OF MEDICAL VIROLOGY
(2023)
Article
Biochemistry & Molecular Biology
Qian Wang, Xiangmei Li, Jiayue Qiu, Yalan He, Jiashuo Wu, Ji Li, Wei Liu, Junwei Han
Summary: Immune checkpoint inhibitor therapy is effective for melanoma, but gene-based predictive biomarkers are unstable. This study proposes a novel pathway mutation signature (PMS) model that predicts the survival and efficacy of ICI therapy based on accumulated gene mutations in biological pathways.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Biochemistry & Molecular Biology
Wei Wang, Haiyan Yuan, Junwei Han, Wei Liu
Summary: Risk gene identification has been a focus of attention in the past two decades. This study proposes a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes, which yields superior predictive performance and identifies close partners that synergize with individual risk proteins. Additionally, selection probabilities are calculated and other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Medicine, Research & Experimental
Xiangmei Li, Yalan He, Ying Jiang, Bingyue Pan, Jiashuo Wu, Xilong Zhao, Junling Huang, Qian Wang, Liang Cheng, Junwei Han
Summary: Immunotherapy is a promising cancer therapy, but effective biomarkers are needed to identify responsive patients. This study developed a pathway analysis method, PathwayTMB, to identify genomic mutation pathways as potential biomarkers for immunotherapy. The method showed promising results in predicting clinical outcome and had superior predictive effect compared to TMB.
MOLECULAR THERAPY-NUCLEIC ACIDS
(2023)