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Single-cell pair-wise relationships untangled by composite embedding model

iScience 2023年第2期26卷 106025页

作者： Subedi, Sishir Park, Yongjin P. Bioinformatics Graduate Program University of British Columbia Vancouver BC Canada BC Cancer Research Part of Provincial Health Care Authority Vancouver BC Canada Department of Pathology and Laboratory Medicine University of British Columbia Vancouver BC Canada Department of Statistics University of British Columbia Vancouver BC Canada

In multicellular organisms, cell identity and functions are primed and refined through interactions with other surrounding cells. Here, we propose a scalable machine learning method, termed SPRUCE, which is designed to systematically ascertain common cell-cell communication patterns embedded in single-cell RNA-seq data. We applied our approach to investigate tumor microenvironments consolidating multiple breast cancer datasets and found seven frequently observed interaction signatures and underlying gene-gene interaction networks. Our results implicate that a part of tumor heterogeneity, especially within the same subtype, is better understood by differential interaction patterns rather than the static expression of known marker genes. © 2023 The Author(s)

关键词： Cancer systems biology Machine learning Transcriptomics

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Continuity Equation of Left Invariant Gauge Field in Quantum Genotype Of Deltacron

SSRN

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SSRN 2022年

作者： Hannongbua, Supot Kanjamapornkul, Kabin Rungrotmongkol, Thanyada Department of Chemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Biocatalyst and Environmental Biotechnology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand

The continuity equation for the invariant gauge field of Deltacron was investigated from the replication and recombination of Delta with Omicron. The new definition of left translation of the invariant gauge field in the quantum genotype of virus and host cell was introduced. The Yang-Mills field in genetic code is an induced curvature in the parallel transport of a unified field between hydrogen bonding between three types of biomolecules. where we defined the invariant gauge field in quantum genotype. This issue allows us to identify the Poincare duality between the nteraction between the behavior field of gene expression in parasitism state and in the curvature of connection in fiber space of Spike protein folding molecule. The Chern Simons current is the anti-self-dual state of mutation between two types of interactive behavior fields in The recombination process of Deltacron was used to plot the Yang-Mills curvature directly from the underlying genetic codes. Further analysis shows that the difference between the tensor correlation with various times of Deltacron the change slope of Omicron BA.1 to the flat curve of the network correlation and closedness centrality of amino acids in receptor binding domain of Spike protein. © 2022, The Authors. All rights reserved.

关键词： Gene expression

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Deep Learning as a Vector Embedding Model for Customer Churn

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Procedia Computer Science 2021年 179卷 624-631页

作者： Tjeng Wawan Cenggoro Raditya Ayu Wirastari Edy Rudianto Mochamad Ilham Mohadi Dinne Ratj Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

To face the tight competition in the telecommunication industry, it is important to minimize the rate of customers stopping their service subscription, which is known as customer churn. For that goal, an explainable predictive customer churn model is an essential tool to be owned by a telecommunication provider. In this paper, we developed the explainable model by utilizing the concept of vector embedding in Deep Learning. We show that the model can reveal churning customers that can potentially be converted back to use the previous telecommunication service. The generated vectors are also highly discriminative between the churning and loyal customers, which enable the developed models to be highly predictive for determining whether a customer would cease his/her service subscription or not. The best model in our experiment achieved a predictive performance of 81.16%, measured by the F1 Score. Further analysis on the clusters similarity and t-SNE plot also confirmed that the generated vectors are discriminative for churn prediction.

关键词： customer churn customer behavior deep learning vector embedding representation learning

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Mutual dependency between lncRNA LETN and protein NPM1 in controlling the nucleolar structure and functions sustaining cell proliferation

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细胞研究（英文版） 2021年第6期31卷 664-683页

作者： Xianteng Wang Xiaolin Hu Wanlu Song Hui Xu Zhengtao Xiao Rongyao Huang Qingran Bai Fan Zhang Yongzhen Chen Yu Liu Jianhuo Fang Xin Li Qin Shen Haitao Zhao Xuerui Yang MOE Key Laboratory of Bioinformatics Tsinghua UniversityBeijing 100084China Center for Synthetic & Systems Biology Tsinghua UniversityBeijing 100084China School of Life Sciences Tsinghua UniversityBeijing 100084China MOE Key Laboratory of Bioinformatics Tsinghua UniversityBeijing 100084China Center for Synthetic & Systems Biology Tsinghua UniversityBeijing 100084China School of Life Sciences Tsinghua UniversityBeijing 100084China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science Beijing 100084China Brain and Spinal Cord Innovative Research Center of Tongji Hospital School of Life Sciences and TechnologyTongji University and Frontier Science Research Center for Stem Cells of Ministry of EducationShanghai 200092China Department of Liver Surgery Peking Union Medical College HospitalChinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC)Beijing 100730China

Fundamental processes such as ribosomal RNA synthesis and chromatin remodeling take place in the nucleolus,which is hyperactive in fast-proliferating *** sophisticated regulatory mechanism underlying the dynamic nucleolar structure and functions is yet to be fully *** present study uncovers the mutual functional dependency between a previously uncharacterized human long non-coding RNA,which we renamed LETN,and a key nucleolar protein,***,being upregulated in multiple types of cancer,LETN resides in the nucleolus via direct binding with *** plays a critical role in facilitating the formation of NPM1 pentamers,which are essential building blocks of the nucleolar granular component and control the nucleolar *** of LETN or NPM1 led to similar and profound changes of the nucleolar morphology and arrest of the nucleolar functions,which led to proliferation inhibition of human cancer cells and neural progenitor ***,this inter-dependency between LETN and NPM1 is associated with the evolutionarily new variations of NPM1 and the coincidental emergence of LETN in higher *** propose that this human-specific protein-lncRNA axis renders an additional yet critical layer of regulation with high physiological relevance in both cancerous and normal developmental processes that require hyperactive nucleoli.

关键词： Cell division Long non-coding RNAs

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SAGE-Amine: Generative Amine design with multi-property optimization for efficient CO 2 capture

Carbon Capture Science & Technology

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Carbon Capture Science & Technology 2025年 16卷

作者： Hocheol Lim Hyein Cho Jeonghoon Kim Kyoung Tai No Bioinformatics and Molecular Design Research Center (BMDRC) Incheon 21983 Republic of Korea The Interdisciplinary Graduate Program in Integrative Biotechnology & Translational Medicine Yonsei University Incheon 21983 Republic of Korea

Efficient CO 2 capture is vital for mitigating climate change, with amine-based solvents being widely used due to their strong reactivity with CO 2 . However, optimizing key properties such as basicity, viscosity, and absorption capacity remains challenging, as traditional methods rely on labor-intensive experimentation and predefined chemical databases, limiting the exploration of novel solutions. Here, SAGE-Amine was introduced, a generative modeling approach that integrates Scoring-Assisted Generative Exploration (SAGE) with quantitative structure-property relationship models to design new amines tailored for CO 2 capture. Unlike conventional virtual screening restricted to existing compounds, SAGE-Amine generates novel amines by leveraging autoregressive natural language processing models trained on amine datasets. SAGE-Amine identified known amines for CO 2 capture from scratch and successfully performed single-property optimization, increasing basicity or reducing viscosity or vapor pressure. Furthermore, it facilitated multi-property optimization, simultaneously achieving high basicity with low viscosity and vapor pressure. The 10 top-ranked amines were suggested using SAGE-Amine and their thermodynamic properties were further assessed using COSMO-RS simulations, confirming their potential for CO 2 capture. These results highlight the potential of generative modeling in accelerating the discovery of amine solvents and expanding the possibilities for industrial CO 2 capture applications.

关键词： Material discovery De novo molecular design Quantitative structure-property relationship Solvent design Amine design

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Infigratinib is a reversible inhibitor and mechanism-based inactivator of cytochrome P450 3A4S

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Drug Metabolism and Disposition 2021年第9期49卷 856-868页

作者： Tang, Lloyd Wei Tat Teng, Jian Wei Verma, Ravi Kumar Koh, Siew Kwan Zhou, Lei Go, Mei Lin Fan, Hao Chan, Eric Chun Yong Department of Pharmacy Faculty of Science National University of Singapore Singapore Singapore Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Bioinformatics Institute Agency for Science Technology and Research Singapore Singapore Singapore Eye Research Institute Singapore Singapore Ophthalmology and Visual Sciences Academia Clinical Program Duke-National University of Singapore Medical School Singapore Singapore

Infigratinib (INF) is a promising selective inhibitor of fibroblast growth factor receptors 1-3 that has recently been accorded both orphan drug designation and priority review status by the US Food and Drug Administration for the treatment of advanced cholangiocarcinoma. Its propensity to undergo bioactivation to electrophilic species was recently expounded upon. However, other than causing aberrant idiosyncratic toxicities, these reactive intermediates may elicit mechanism-based inactivation of cytochrome P450 enzymes. In this study, we investigated the interactions between INF and the most abundant hepatic CYP3A. Our findings revealed that, apart from being a potent noncompetitive reversible inhibitor of CYP3A4, INF inactivated CYP3A4 in a time-, concentration- and NADPH-dependent manner with inactivator concentration at halfmaximum inactivation rate constant, maximum inactivation rate constant, and partition ratio of 4.17 μM, 0.068 minute-1, and 41, respectively, when rivaroxaban was employed as the probe substrate. Coincubation with testosterone (alternative CYP3A substrate) or ketoconazole (direct CYP3A inhibitor) attenuated the rate of inactivation, whereas the inclusion of glutathione and catalase did not confer such protection. The lack of enzyme activity recovery after dialysis for 4 hours and oxidation with potassium ferricyanide, coupled with the absence of the characteristic Soret peak signature collectively substantiated that inactivation of CYP3A4 by INF was not mediated by the formation of quasi-irreversible metabolite- intermediate complexes but rather through irreversible covalent adduction to the prosthetic heme and/or apoprotein. Finally, glutathione trapping and high-resolution mass spectrometry experimental results unraveled two plausible bioactivation mechanisms of INF arising from the generation of a p-benzoquinonediimine and epoxide reactive intermediate. SIGNIFICANCE STATEMENT The potential of INF to cause MBI of CYP3A4 was unknown. This st

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Comparing the prediction performance of machine learning and deep learning methods for 3c-like protease cleavage sites 23

Comparing the prediction performance of machine learning and...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Huang, Lan-Ying Chen, Chi-Wei Chang, Kai-Po Chu, Yen-Wei Medical Biotechnology National Chung Hsing University Taichung402 Taiwan Department of Pathology School of Medicine China Medical University Taichung402 Taiwan Department of Pathology China Medical University Hospital Taichung402 Taiwan Graduate Degree Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung82445 Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung82445 Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung402 Taiwan Institute of Molecular Biology National Chung Hsing University Taichung402 Taiwan Agricultural Biotechnology Center National Chung Hsing University Taichung402 Taiwan Biotechnology Center National Chung Hsing University Taichung402 Taiwan Translational Medicine National Chung Hsing University Taichung402 Taiwan Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung402 Taiwan Taichung402 Taiwan

ISBN: (纸本)9798400700712

The impact of COVID-19 on the human body after infection requires further investigation. The protease carried by the virus may interact with cellular proteins, thereby affecting the regulatory pathways. Machine learning can provide predictive information to aid the research design in such studies. This study examined the effectiveness of different encoding methods for training models using traditional machine learning and deep learning methods. Using the advantages of deep learning methods for high-dimensional data analysis, amino acid properties were extracted and classified. Viral polyprotein data were used as the training set and human protein substrates were collected from the literature as the independent test set. In the one-dimensional convolutional neural network model, the accuracy of the independent test set was the highest, at 0.832. © 2023 ACM.

关键词： Coronavirus

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BERT-based Topic Modeling Approach for Malaria Research Publication

BERT-based Topic Modeling Approach for Malaria Research Publ...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Arif Budiarto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665473286

Malaria is a communicable disease with half of the global population at risk due to its high morbidity and mortality rates. A massive number of studies are dedicated to malaria research, so it plays a key role in formulating the proper prevention strategy and effective malaria treatment. With the overwhelming number of updated publications in the field, an unsupervised text mining approach such as topic modeling may provide an alternative method for the malaria researcher to keep pace with new insights. In this work, we collect metadata of malaria publications from the PubMed database to perform BERT-based topic modeling to find well-defined topics regarding malaria research. The method is largely based on the popular BERTopic pipeline. We compare the performance of three different language models to generate document embeddings from the data. The dimension reduction and the density-based clustering algorithm are used to cluster the embeddings. The topic representation is computed based on the semantic similarity of the class TF-IDF representation. The substance of the resulting topics is then manually annotated based on the top words of each topic. We demonstrate that by merging initial topics into larger topics using hierarchical clustering and manual content-based examination, the evaluated coherence measure can be further improved, thus enhancing the topic's interpretability. Our modeling result is able to extract ten major topics recurring in the malaria research publication published from 2017–2022. The result provides preliminary insight to understand the dynamics and patterns of malaria research over the years

关键词： Text mining Heuristic algorithms Multimedia systems Time series analysis Sociology Semantics Pipelines

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Insights into the binding interactions of Macitentan to bovine serum Albumin: A Spectroscopic, electrochemical and computational study

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Journal of Molecular Liquids 2025年 429卷

作者： Sadak, Selenay Dindar, Çiğdem Kanbeş Kabir, Md.Zahirul Rehman, Fazal Mohamad, Saharuddin B. Yildirim, Sercan Uslu, Bengi Ankara University Faculty of Pharmacy Department of Analytical Chemistry Ankara06560 Turkey Ankara University The Graduate School of Health Sciences Ankara06110 Turkey Biochemistry Program Institute of Biological Sciences Faculty of Science University Malaya Kuala Lumpur Malaysia Faculty of Science Bioinformatics Programme Institute of Biological Sciences Malaysia Centre of Research for Computational Sciences and Informatics for Biology Bioindustry Environment Agriculture and Healthcare University of Malaya Kuala Lumpur Malaysia Karadeniz Technical University Faculty of Pharmacy Department of Analytical Chemistry Trabzon Turkey

Macitentan (MACI) is an endothelial receptor antagonist used to treat pulmonary arterial hypertension (PAH). The pharmacokinetics and therapeutic efficacy of MACI are significantly influenced by its interaction with plasma proteins, especially bovine serum albumin (BSA). In this study, spectroscopic (fluorescence and UV–visible absorption spectroscopy), electrochemical, and computational techniques were applied to explore the binding properties and interaction mechanisms between MACI and BSA. The results indicated that the MACI quenched BSA fluorescence through a static mechanism. The interaction possessed a moderate binding affinity with Ka values ranging from 1.27 × 104 to 7.25 × 104 M−1 at different temperatures. Hydrophobic forces and hydrogen bonds were identified as critical factors in stabilizing the complex based on thermodynamic parameters. The local environment of tryptophan residues in BSA was found to be altered following the addition of MACI, as revealed by synchronous and three-dimensional fluorescence spectra. The spectroscopic studies were supported by electrochemical analyses, which indicated the formation of an electro-inactive complex between MACI and BSA. From the molecular docking experiments, the most likely binding site for MACI was found to be site I of BSA. Several hydrogen bonds and hydrophobic contacts were observed during the interaction of MACI with site I of BSA. These findings provided detailed insight to understand the binding properties of MACI to BSA, which may be beneficial for medical and pharmaceutical applications in the future. © 2025 Elsevier B.V.

关键词： Hydrogen bonds

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A Design of Childhood Stunting Assessment Feature with Agile UX Approach

A Design of Childhood Stunting Assessment Feature with Agile...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Reza Rahutomo Anzaludin Samsinga Perbangsa Faisal Asadi Rudi Nirwantono Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665476652

multidisciplinary collaboration between public health, system engineering, and UX is able to generate a solution in healthcare problem like stunting. The principle of Agile UX gathers requirements to generate an application design. Currently, various website and mobile applications are available to be utilized for stunting research. StuntingDB is one of many web-based applications that specialized in stunting data management. The application required several types of users to operate hierarchically. This research focused on Public Health Officers’ workflow that require a solution to in the form of assessment module for stunting DBMS by using Agile UX principle. The method collaborates agile team and UX team to solve Public Health Officer's difficulty in changing stunting status at pre-saved child patient's profile. Collaboration of both agile team and UX team successfully proposed an application design that system design.

关键词： Human computer interaction Pediatrics Collaboration Companies Mobile applications Public healthcare System analysis and design

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