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Identification of Proteins and Genes Associated with Hedgehog Signaling Pathway Involved in Neoplasm Formation Using Text-Mining Approach

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Big Data Mining and Analytics 2024年第1期7卷 107-130页

作者： Nadezhda Yu.Biziukova Sergey M.Ivanov Oga A.Tarasova department of bioinformatics Institute of Biomedical ChemistryMoscow 119121Russia department of bioinformatics Pirogov Russian National Research Medical UniversityMoscow 117997Russia

Analysis of molecular mechanisms that lead to the development of various types of tumors is essential for biology and medicine,because it may help to find new therapeutic opportunities for cancer treatment and cure including personalized treatment *** of the pathways known to be important for the development of neoplastic diseases and pathological processes is the Hedgehog signaling pathway that normally controls human embryonic *** accumulation of various types of biological data,including interactions between proteins,regulation of genes transcription,proteomics,and metabolomics experiments results,allows the application of computational analysis of these big data for identification of key molecular mechanisms of certain diseases and pathologies and promising therapeutic *** aim of this study is to develop a computational approach for revealing associations between human proteins and genes interacting with the Hedgehog pathway components,as well as for identifying their roles in the development of various types of *** automatically collect sets of abstract texts from the NCBI PubMed bibliographic *** recognition of the Hedgehog pathway proteins and genes and neoplastic diseases we use a dictionary-based named entity recognition approach,while for all other proteins and genes machine learning method is *** association extraction,we develop a set of semantic *** complete the results of the text analysis with the gene set enrichment *** identified key pathways that may influence the Hedgehog pathway and their roles in tumor development are then verified using the information in the literature.

关键词： text-mining data mining Hedgehog pathway neoplastic processes enrichment analysis pathology molecularmechanisms

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An enhanced method for predicting and analysing forest fires using an attention-based CNN model

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Journal of Forestry Research 2024年第4期35卷 115-127页

作者： Shaifali Bhatt Usha Chouhan department of Mathematics Bioinformatics and Computer ApplicationsMANIT BhopalBhopalIndia

Prediction,prevention,and control of forest fires are crucial on at all *** effective fire detection systems can aid in their *** study proposes a novel CNN(convolutional neural network)using an attention blocks module which combines an attention module with numerous input layers to enhance the performance of neural *** suggested model focuses on predicting the damage affected/burned areas due to possible wildfires and evaluating the multilateral interactions between the pertinent *** results show the impacts of CNN using attention blocks for feature extraction and to better understand how ecosystems are affected by meteorological *** selected meteorological data,RMSE 12.08 and MAE 7.45 values provide higher predictive power for selecting relevant and necessary features to provide optimal performance with less operational and computational *** findings show that the suggested strategy is reliable and effective for planning and managing fire-prone regions as well as for predicting forest fire damage.

关键词： CNN Attention module Fire prediction Ecosystem Damage prediction

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Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

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Cancer Biology & medicine 2024年第4期21卷 312-330页

作者： Qian Zhang Mingran Yang Peng Zhang Bowen Wu Xiaosen Wei Shao Li Institute for TCM-X MOE Key Laboratory of BioinformaticsBioinformatics DivisionBNRISTDepartment of AutomationTsinghua UniversityBeijing 100084China

Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

关键词： Gastric cancer inflammation-induced tumorigenesis multi-omics artificial intelligence network-based methods

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NeoHunter:Flexible software for systematically detecting neoantigens from sequencing data

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Quantitative Biology 2024年第1期12卷 70-84页

作者： Tianxing Ma Zetong Zhao Haochen Li Lei Wei Xuegong Zhang MOE Key Lab of bioinformatics Bioinformatics Division of BNRIST and Department of AutomationTsinghua UniversityBeijingChina School of medicine Tsinghua UniversityBeijingChina School of Life Sciences Tsinghua UniversityBeijingChina

Complicated molecular alterations in tumors generate various mutant *** of these mutant peptides can be presented to the cell surface and then elicit immune responses,and such mutant peptides are called *** detection of neoantigens could help to design personalized cancer *** some computational frameworks for neoantigen detection have been proposed,most of them can only detect SNV-and indel-derived *** addition,current frameworks adopt oversimplified neoantigen prioritization *** factors hinder the comprehensive and effective detection of *** developed NeoHunter,flexible software to systematically detect and prioritize neoantigens from sequencing data in different *** can detect not only SNV-and indel-derived neoantigens but also gene fusion-and aberrant splicing-derived *** supports both direct and indirect immunogenicity evaluation strategies to prioritize candidate *** strategies utilize binding characteristics,existing biological big data,and T-cell receptor specificity to ensure accurate detection and *** applied NeoHunter to the TESLA dataset,cohorts of melanoma and non-small cell lung cancer *** achieved high performance across the TESLA cancer patients and detected 79%(27 out of 34)of validated neoantigens in ***-and indel-derived neoantigens accounted for 90%of the top 100 candidate neoantigens while neoantigens from aberrant splicing accounted for 9%.Gene fusion-derived neoantigens were detected in one *** is a powerful tool to‘catch all’neoantigens and is available for free academic use on Github(XuegongLab/NeoHunter).

关键词： cancer vaccine molecular alteration neoantigen neoantigen prioritization

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Primary lateral sclerosis:more than just an upper motor neuron disease

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Neural Regeneration Research 2024年第9期19卷 1881-1882页

作者： Ee Ling Tan Jasmin Lope Peter Bede computational Neuroimaging Group(CNG) School of MedicineTrinity College DublinIreland department of Neurology St James’s HospitalDublinIreland

Advances in clinical care and recent research achievements:Primary lateral sclerosis(PLS)has traditionally been regarded as a pure upper motor neuron condition,a view perpetuated by most medical textbooks.

关键词： sclerosis clinical traditionally

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DIProT:A deep learning based interactive toolkit for efficient and effective Protein design

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Synthetic and Systems Biotechnology 2024年第2期9卷 217-222页

作者： Jieling He Wenxu Wu Xiaowo Wang Ministry of Education Key Laboratory of bioinformatics Center for Synthetic and Systems BiologyBioinformatics DivisionBeijing National Research Center for Information Science and TechnologyDepartment of AutomationTsinghua UniversityBeijingChina

The protein inverse folding problem,designing amino acid sequences that fold into desired protein structures,is a critical challenge in biological *** numerous data-driven and knowledge-driven methods,there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein *** this paper,we present DIProT,an interactive protein design *** leverages a non-autoregressive deep generative model to solve the inverse folding problem,combined with a protein structure prediction *** integration allows users to incorporate prior knowledge into the design process,evaluate designs in silico,and form a virtual design loop with human *** inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets,with promising sequence recovery and inference *** studies further illustrate how DIProT can facilitate user-guided protein design.

关键词： computational Protein design Interactive design toolkit Protein inverse folding Non-autoregressive decoding

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Graph based recurrent network for context specific synthetic lethality prediction

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Science China Life Sciences 2024年

作者： Yuyang Jiang Jing Wang Yixin Zhang ZhiWei Cao Qinglong Zhang Jinsong Su Song He Xiaochen Bo Academy of Medical Engineering and Translational medicine Tianjin University department of bioinformatics Institute of Health Service and Transfusion MedicineBeijing School of medicine Tsinghua University School of Informatics Xiamen University

The concept of synthetic lethality (SL) has been successfully used for targeted therapies. To further explore SL for cancer therapy, identifying more SL interactions with therapeutic potential are essential. Recently, graph neural network-based deep learning methods have been proposed for SL prediction, which reduce the SL search space of wet-lab based methods. However, these methods ignore that most SL interactions depend strongly on genetic context, which limits the application of the predicted results. In this study, we proposed a graph recurrent network-based model for specific context-dependent SL prediction (SLGRN). In particular, we introduced a Graph Recurrent Network-based encoder to acquire a context-specific, low-dimensional feature representation for each node, facilitating the prediction of novel SL. SLGRN leveraged gate recurrent unit (GRU) and it incorporated a context-dependent-level state to effectively integrate information from all nodes. As a result, SLGRN outperforms the state-of-the-arts models for SL prediction. We subsequently validate novel SL interactions under different contexts based on combination therapy or patient survival analysis. Through in vitro experiments and retrospective clinical analysis, we emphasize the potential clinical significance of this context-specific SL prediction model.

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APIR:Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control

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Genomics, Proteomics & bioinformatics 2024年第2期22卷 171-187页

作者： Yiling Elaine Chen Xinzhou Ge Kyla Woyshner MeiLu McDermott Antigoni Manousopoulou Scott B.Ficarro Jarrod A.Marto Kexin Li Leo David Wang Jingyi Jessica Li department of Statistics and Data Science University of CaliforniaLos AngelesCA 90095USA department of Immuno-Oncology Beckman Research InstituteCity of Hope National Medical CenterDuarteCA 91010USA department of Quantitative and computational Biology University of Southern CaliforniaLos AngelesCA 90089USA department of Cancer Biology and Blais Proteomics Center Dana-Farber Cancer InstituteDepartment of PathologyBrigham and Women’s Hospital and Harvard Medical SchoolBostonMA 02215USA department of Pediatrics City of Hope National Medical CenterDuarteCA 91010USA bioinformatics Interdepartmental Program University of CaliforniaLos AngelesCA 90095USA department of Human Genetics University of CaliforniaLos AngelesCA 90095USA department of computational medicine University of CaliforniaLos AngelesCA 90095USA department of Biostatistics University of CaliforniaLos AngelesCA 90095USA

Advances in mass spectrometry(MS)have enabled high-throughput analysis of proteomes in biological *** state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide–spectrum matches(PSMs),which convert mass spectra to peptide *** database search algorithms use distinct search strategies and thus may identify unique ***,no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate(FDR).To fill in this gap,we proposed a statistical framework,Aggregation of Peptide Identification Results(APIR),that is universally compatible with all database search ***,under an FDR threshold,APIR is guaranteed to identify at least as many,if not more,peptides as individual database search algorithms *** of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the *** data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search *** APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis,e.g.,differential gene expression analysis on RNA sequencing *** APIR R package is available at https://***/yiling0210/APIR.

关键词： Shotgun proteomics Peptide–spectrum match Peptide identification Aggregation of lists FDR control

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Genetic landscape and clinical significance of cuproptosis-related genes in liver hepatocellular carcinoma

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Genes & Diseases 2024年第2期11卷 516-519页

作者： Dingtao Hu Yichuan Wang Xu Shen Tiantian Mao Xijun Liang Tengjiao Wang Weifeng Shen Yugang Zhuang Jin Ding Clinical Cancer Institute Center for Translational MedicineNaval Medical UniversityShanghai 200433China department of Emergency Shanghai Tenth People's HospitalTongji UniversitySchool of MedicineShanghai 200072China bioinformatics Analysis Laboratory Center for Translational MedicineNaval Medical UniversityShanghai 200433China The department of Hepatic Surgery Eastern Hepatobiliary Surgery HospitalNaval Medical UniversityShanghai 200433China

Recently,Peter et al reported a novel form of cell death(cuproptosis)that was different from other known death mechanisms.1 They found that accumulation of copper ions could induce destabilization of Fe-S cluster proteins and aggregation of mitochondrial lipoylated proteins,ultimately resulting in cuproptosis.1 of note,ten genes were identified as cuproptosis-related genes(CRGs)in copper ion-induced cell death,including PDHB,PDHA1,DLAT,DLD,LIPT1,LIAS,FDX1,CDKN2A,GLS,and MTF1.1 Since dysregulation of copper metabolism is involved in many cancers,cuproptosis and CRGs may play vital roles in cancer development and treatment.1.2 Herein,our pancancer analysis revealed the coordinated upregulation of 9 of 10 CRGs in human liver hepatocellular carcinoma(LIHC)across 33 solid tumors(Fig.S1A;Fig.1A),suggesting the distinct role of CRGs in *** then explored the genetic landscape,biological function,and clinical significance of CRGs in LIHC,and validated our bioinformatic findings by in vitro experiments and clinical *** detailed methods were described in the supplementary material.

关键词： clinical metabolism hepatocellular

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Investigating the Potential Mechanism of Huazhi Rougan Granules Against Alcoholic Liver Disease by Network Pharmacology and Experiment

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World Journal of Traditional Chinese medicine 2024年第2期10卷 191-203页

作者： Ying-Ying Tan Ying-Ying Liu Yi-Yan Zhai Yi-Xuan Wang Zhi-Shan Wu Antony Stalin Guo-Liang Cheng Bing Li Chao Wu Zhi-Hong Huang Shan Lu Xiao-Tian Fan Zheng Zhao Jia-Rui Wu department of Clinical Chinese Pharmacy School of Chinese Materia MedicaBeijing University of Chinese MedicineBeijingChina department of bioinformatics Institute of Fundamental and Frontier SciencesUniversity of Electronic Science and Technology of ChinaChengduChina department of Traditional Chinese medicine State Key Laboratory of Generic Manufacture Technology of Chinese Traditional MedicineLinyiChina department of Clinical medicine Beijing Zest Bridge Medical Technology Inc.BeijingChina

Objective: Huazhi Rougan granules(HRGs) are a promising drug to improve alcoholic liver disease(ALD), but its mechanism remains unclear. Materials and Methods: Network pharmacology, molecular docking, and animal experiments were used to reveal the potential mechanism of HRG against ALD. Results: A total of 245 potential targets of HRG against ALD were obtained. Functional enrichment analysis suggests that HRG could synergistically regulate various biological pathways to exert therapeutic effects on ALD. Molecular docking showed that the key targets exhibited the good binding ability with the key compounds. The in vivo experiments showed that HRG can effectively alleviate pathological changes in liver tissue, improve blood lipid levels, antioxidant stress ability, and liver function, and reduce the release of inflammatory cytokines in the liver. Conclusions: This study reflects that HRG is an effective strategy for treating ALD, providing a basis for revealing the prevention and treatment mechanisms of ALD.

关键词： Alcoholic liver disease Huazhi Rougan granules inflammatory response network pharmacology

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