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Target genes discovery through copy number alteration analysis in human hepatocellular carcinoma

World Journal of Gastroenterology 2013年第47期19卷 8873-8879页

作者： De-Leung Gu Yen-Hsieh Chen Jou-Ho Shih Chi-Hung Lin Yuh-Shan Jou Chian-Feng Chen Institute of Biomedical Sciences Academia Sinica Institute of Microbiology and Immunology School of Life ScienceNational Yang-Ming University Bioinformatics Program Taiwan International Graduate ProgramAcademia Sinica Genome and Systems Biology Degree Program National Taiwan University VYM Genome Research Center National Yang-Ming University

High-throughput short-read sequencing of exomes and whole cancer genomes in multiple human hepatocellular carcinoma(HCC)cohorts confirmed previously identified frequently mutated somatic genes,such as TP53,CTNNB1 and AXIN1,and identified several novel genes with moderate mutation frequencies,including ARID1A,ARID2,MLL,MLL2,MLL3,MLL4,IRF2,ATM,CDKN2A,FGF19,PIK3CA,RPS6KA3,JAK1,KEAP1,NFE2L2,C16orf62,LEPR,RAC2,and *** classification of these mutated genes suggested that alterations in pathways participating in chromatin remodeling,Wnt/β-catenin signaling,JAK/STAT signaling,and oxidative stress play critical roles in HCC ***,because there are few druggable genes used in HCC therapy,the identification of new therapeutic targets through integrated genomic approaches remains an important *** a large amount of HCC genomic data genotyped by high density single nucleotide polymorphism arrays is deposited in the public domain,copy number alteration(CNA)analyses of these arrays is a cost-effective way to reveal target genes through profiling of recurrent and overlapping amplicons,homozygous deletions and potentially unbalanced chromosomal translocations accumulated during HCC ***,integration of CNAs with other high-throughput genomic data,such as aberrantly coding transcriptomes and non-coding gene expression in human HCC tissues and rodent HCC models,provides lines of evidence that can be used to facilitate the identification of novel HCC target genes with the potential of improving the survival of HCC patients.

关键词： Copy number alteration High-density single nucleotide polymorphism arrays Driver genes Hepatocellular carcinoma

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Identification of novel TMEM16A blockers through integrated virtual screening, molecular dynamics, and experimental studies

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Scientific Reports 2025年第1期15卷 1-11页

作者： Sangkhawasi, Mattanun Pitaktrakul, Wichuda Khumjiang, Rungtiwa Shigeta, Yasuteru Muanprasat, Chatchai Hengphasatporn, Kowit Rungrotmongkol, Thanyada Center of Excellence in Biocatalyst and Sustainable Biotechnology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Chakri Naruebodindra Medical Institute Faculty of Medicine Ramathibodi Hospital Mahidol University Bang Pla Bang Pli Samut Prakan 10540 Thailand Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba 305-8577 Japan Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok 10330 Thailand Division of Biochemistry Department of Preclinical Science Faculty of Medicine Thammasat University Pathumthani 12120 Thailand

The calcium-activated chloride channel TMEM16A is a promising drug target for treating hypertension, secretory diarrheas, and various cancers, including head and neck cancer. Despite its potential, no FDA-approved drugs have provided the structural basis for directly inhibiting TMEM16A. This study aims to identify a novel pore-blocker of TMEM16A by integrating virtual screening, molecular dynamics simulations, and in vitro studies. Using the calcium-bound structure of TMEM16A with and without the pore-blocker 1PBC, we performed virtual screening on nearly 90,000 compounds from the ChemDiv database. Approximately 67% of these compounds demonstrated better binding affinity than 1PBC. Among the top 20 compounds selected for short-circuit current assays using human lung adenocarcinoma cells (Calu-3), compounds N066-0059, N066-0060, and N066-0067 inhibited TMEM16A activity with IC₅₀ values of 0.24 µM, 0.41 µM, and 0.48 µM, respectively, which was lower than that of a positive control Ani9 (9 µM). Due to its highest potency in electrophysiological assays, N066-0059 was subjected to mechanistic studies. Molecular dynamics simulations elucidated its binding stability and strength, showing superior performance to 1PBC over 500 ns with 3 replicates. This study advances TMEM16A-targeted drug development, offering new insights for anticancer therapies. © The Author(s) 2025.

关键词： And electrophysiological assays Cytotoxicity Molecular dynamics Pore-blocker inhibitor TMEM16A Virtual screening

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Oryza CLIMtools:A genome–environment association resource reveals adaptive roles for heterotrimeric G proteins in the regulation of rice agronomic traits

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Plant Communications 2024年第4期5卷 145-165页

作者： Angel Ferrero-Serrano David Chakravorty Kobie J.Kirven Sarah M.Assmann Biology Department Pennsylvania State University208 Mueller LaboratoryUniversity ParkPA 16802USA Intercollege Graduate Degree Program in Bioinformatics and Genomics Pennsylvania State University208 Mueller LaboratoryUniversity ParkPA 16802USA

Modern crop varieties display a degree of mismatch between their current distributions and the suitability of the local climate for their *** address this issue,we present Oryza CLIMtools(https://***/CLIMtools/oryza_v1.0/),thefirst resource for pan-genome prediction of climate-associated genetic variants in a crop *** CLIMtools consists of interactive web-based databases that enable the user to(1)explore the local environments of traditional rice varieties(landraces)in South-East Asia and(2)investigate the environment by genome associations for 658 Indica and 283 Japonica rice landrace accessions collected from georeferenced local environments and included in the 3K Rice Ge-nomes *** demonstrate the value of these resources by identifying an interplay betweenflowering time and temperature in the local environment that is facilitated by adaptive natural variation in OsHD2 and disrupted by a natural variant in *** quantitative trait locus analysis has suggested the impor-tance of heterotrimeric G proteins in the control of agronomic ***,we analyzed the climate associations of natural variants in the different heterotrimeric G protein *** identified a coordi-nated role of G proteins in adaptation to the prevailing potential evapotranspiration gradient and revealed their regulation of key agronomic traits,including plant height and seed and panicle *** conclude by highlighting the prospect of targeting heterotrimeric G proteins to produce climate-resilient crops.

关键词： Oryza sativa rice G by E adaptation flowering time GWAS OsHD2 OsSOC1 heterotrimeric G pro-tein OsRGA1 d1 OsGS3 OsDEP1 OsGGC2

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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Evolving hypernetwork classifiers for microRNA expression profile analysis

Evolving hypernetwork classifiers for microRNA expression pr...

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2007 IEEE Congress on Evolutionary Computation, CEC 2007

作者： Kim, Sun Kim, Soo-Jin Zhang, Byoung-Tak Center for Bioinformatics Technology Graduate Program in Bioinformatics Seoul National University Seoul 151-742 Korea Republic of School of Computer Science and Engineering Seoul National University Seoul 151-744 Korea Republic of

ISBN: (纸本)1424413400

High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms. While computational analysis for the microarrays show good performance, it is still difficult to infer modules of multiple co-regulated genes. Here, we present a novel classification method to identify the gene modules associated with cancers from microarray data. The proposed approach is based on 'hypernetworks', a hypergraph model consisting of vertices and weighted hyperedges. The hypernetwork model is inspired by biological networks and its learning process is suitable for identifying interacting gene modules. Applied to the analysis of microRNA (miRNA) expression profiles on multiple human cancers, the hypernetwork classifiers identified cancer-related miRNA modules. The results show that our method performs better than decision trees and naive Bayes. The biological meaning of the discovered miRNA modules has been examined by literature search. © 2007 IEEE.

关键词： Microarrays

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Evolution Arrests Invasions of Cooperative Populations

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Physical Review Letters 2015年第20期115卷 208104-208104页

作者： Kirill S. Korolev Department of Physics and Graduate Program in Bioinformatics Boston University Boston Massachusetts 02215 USA

Population expansions trigger many biomedical and ecological transitions, from tumor growth to invasions of non-native species. Although population spreading often selects for more invasive phenotypes, we show that this outcome is far from inevitable. In cooperative populations, mutations reducing dispersal have a competitive advantage. Such mutations then steadily accumulate at the expansion front, bringing invasion to a halt. Our findings are a rare example of evolution driving the population into an unfavorable state, and they could lead to new strategies to combat unwelcome invaders.

关键词： Evolutionary dynamics Mutation

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Public Good Diffusion Limits Microbial Mutualism

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Physical Review Letters 2015年第16期114卷 168102-168102页

作者： Rajita Menon Kirill S. Korolev Department of Physics and Graduate Program in Bioinformatics Boston University Boston Massachusetts 02215 USA

Standard game theory cannot describe microbial interactions mediated by diffusible molecules. Nevertheless, we show that one can still model microbial dynamics using game theory with parameters renormalized by diffusion. Contrary to expectations, greater sharing of metabolites reduces the strength of cooperation and leads to species extinction via a nonequilibrium phase transition. We report analytic results for the critical diffusivity and the length scale of species intermixing. Species producing slower public good is favored by selection when fitness saturates with nutrient concentration.

关键词： GAME theory MICROBIAL diversity METABOLITES DIFFUSION -- Measurement MUTUALISM

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Advantages of two quantum programming platforms in quantum computing and quantum chemistry

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Journal of cheminformatics 2025年第1期17卷 77页

作者： Pei-Hua Wang Wei-Yeh Wu Che-Yu Lee Jia-Cheng Hong Yufeng Jane Tseng Undergraduate Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 200 Zhongbei Road Taoyuan 320314 Taiwan. Quantum Information Center Chung Yuan Christian University No. 200 Zhongbei Road Taoyuan 320314 Taiwan. Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Physics Division National Center for Theoretical Sciences No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Center for Quantum Science and Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw.

Quantum computing is at the forefront of technological advancement and has the potential to revolutionize various fields, including quantum chemistry. Choosing an appropriate quantum programming language becomes critical as quantum education and research increase. In this paper, we comprehensively compare two leading quantum programming languages, Qiskit and PennyLane, focusing on their suitability for teaching and research. We delve into their basic and advanced usage, examine their learning curves, and evaluate their capabilities in quantum computing experiments. We also demonstrate using a quantum programming language to build a half adder and a machine learning model. Our study reveals that each language has distinct advantages. While PennyLane excels in research applications due to its flexibility to adjust parameters in detail and access multiple sources of real quantum devices, Qiskit stands out in education because of its web-based graphical user interface and smaller code size. The codes and the dataset used in the studies are available at https://***/wangpeihua1231/quantum-programming-platform .

关键词： PennyLane Qiskit Quantum chemistry Quantum computing Quantum programming language

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Noise response in monomolecular closed systems

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Journal of the Chinese Chemical Society 2023年第3期70卷 747-758页

作者： Fajiculay, Erickson Hsu, Chao-Ping Institute of Chemistry Academia Sinica Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Bioinformatics and Structure Biology National Tsinghua University Hsinchu City Taiwan Physics Division National Center for Theoretical Sciences Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University Taipei Taiwan

One of the primary interests in understanding biological systems is the interaction between noise and cellular regulation. Noise levels can be altered by rate constant perturbations, which may result in an imbalance of important biological functions. Although the theory of noise localization can estimate noise perturbation response, it is currently limited to open systems, where the majority of biological networks belong. However, during their lifetime, some components of biological systems participate in reactions that can be categorized under a closed system. Therefore, a counterpart theory for closed systems is desirable. In this work, steady-state perturbations of monomolecular closed systems and ways to estimate the response of noise as (co)variances are explored. We extend the structural sensitivity analysis for analyzing noise response patterns by using additional clues derived from multinomial assumptions. We identified scenarios in which this combination might fail in noise prediction, which we call noise flip, arising from the nonlinear dependence of variance upon composition. Nevertheless, theoretical intuition and simulation show that noise flip diminishes with increasing system size. This work provides an efficient and scalable means to estimate noise responses with potential applications in model discrimination, network-wide response scanning, and reconstruction. © 2023 The Chemical Society Located in Taipei & Wiley-VCH GmbH.

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Genome-scale metabolic networks in time and space

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Current Opinion in Systems Biology 2018年 8卷 51-58页

作者： Øyås, Ove Stelling, Jörg Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics ETH Zurich Basel Switzerland Systems Biology PhD Program Life Science Zurich Graduate School Zurich Switzerland

Constraint-based models (CBMs) are key tools for elucidating the behavior of genome-scale metabolic networks, but the assumption of steady state hinders their application to spatiotemporally varying and multicellular systems. Models that integrate CBMs with kinetics to allow dynamic simulation through dynamic flux balance analysis (DFBA) can circumvent this problem as well as the limitations of purely kinetic models. With many technical barriers for DFBA overcome in recent years, applications traditionally focused on metabolic engineering have expanded to address problems such as evolution of microbial communities, functions of biomedically relevant biofilms, and diet effects on Parkinson's disease. By addressing substantial computational challenges, we expect that such hybrid metabolic models will pave the way towards whole-cell modeling. © 2017 Elsevier Ltd

关键词： Constraint-based models Dynamic flux balance analysis Genome-scale models Metabolic networks Multi-scale models

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