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Ginger extract compounds in treatment of ischemic stroke: Mechanisms and applications

AIP Conference Proceedings 2022年第1期2589卷

作者： Ziwen Wang 1Department of Biology’s Program in Bioinformatics Northeastern University 360 Huntington Ave Boston MA United States of America 02115

Ischemic stroke is one of the two main forms of cerebrovascular accident that ranks the second highest leading killer worldwide. An attack of ischemic stroke would lead to rapid death of neurons accompanied with inflammatory events in the brain, causing acquired disabilities for the survivors. Yet better drugs for ischemic stroke treatment are still in great need. In this literature review, three major compounds are mentioned and concluded that 6-shogaol, 6-paradol, and zingerone have neuroprotective potential and discussed the possibility to make them promising drugs for ischemic stroke treatment.

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Count time series modelling of Twitter data topic modelling: A case of Indonesia flood events

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. A. Hidayat B Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Twitter data provide rich and powerful information to leverage the dynamics of public perception to establish situational awareness and disaster mitigation strategies during critical times. In this paper, we perform topic modeling via Latent Dirichlet Allocation to extract topics from a collection of tweets related to Indonesia flood events in February 2021 with the query: “banjir”. The extracted topics are used as one of the features to build a generalized linear count time series model with Negative Binomial distribution. We find seven major topics from the model in which tweets containing a topic about the government’s role in handling the situation dominate the conversation. Taking into account a simple intervention analysis, we demonstrate a statistically significant change in the users’ behavior before and after the severe Jakarta flood on 20 February 2021. Moreover, a metric evaluation demonstrates that a covariate that describes the turning point of the Jakarta flood event is convenient to build a more accurate count time series model of the tweets.

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Correction: NFκB activation by hypoxic small extracellular vesicles drives oncogenic reprogramming in a breast cancer microenvironment

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Oncogene 2023年第9期42卷 708页

作者： Irene Bertolini Michela Perego Jagadish C Ghosh Andrew V Kossenkov Dario C Altieri Immunology Microenvironment and Metastasis Program The Wistar Institute Philadelphia PA 19104 USA. Bioinformatics Shared Resource The Wistar Institute Philadelphia PA 19104 USA. Gene Expression and Regulation Program The Wistar Institute Philadelphia PA 19104 USA. Immunology Microenvironment and Metastasis Program The Wistar Institute Philadelphia PA 19104 USA. daltieri@***.

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Implementation of online learning during the COVID-19 pandemic in primary schools

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Anugrahana E. Liswantiani D. Sudigyo T. Suparyanto B. Pardamean Elementary School Teacher Education Study Program (PGSD) Faculty of Teacher Training and Education Sanata Dharma University Yogyakarta Indonesia 55281 Bioinformatics & 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

The COVID-19 pandemic requires face-to-face learning to shift to online or distance learning. Therefore, the elementary school, which is the most basic level of education affected by this phenomenon, where this research is carried out. This study aimed to analyze the process of implementing online learning, the supporting and inhibiting factors for teachers, students, and parents in implementing online learning during the COVID-19 pandemic. In addition, good practices for online learning were also analyzed in this study to understand these phenomena in elementary schools. The research method utilized descriptive qualitative research and was performed at a private primary school in Yogyakarta. The subjects consisted of teachers, students, and parents that were involved in the online learning process. Online questionnaires and interviews were used to collect data. The results of this study show the enormous impact of the COVID-19 pandemic on the learning process. Learning that is usually carried out face-to-face (conventional) has now been converted into online learning in elementary school.

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A Drug-Induced Liver Injury Prediction Model using Transcriptional Response Data with Graph Neural Network

A Drug-Induced Liver Injury Prediction Model using Transcrip...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Doyeong Hwang Minji Jeon Jaewoo Kang Department of Computer Science and Engineering Korea University Seoul Republic of Korea Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Republic of Korea

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Drug-Induced Liver Injury (DILI) is a major cause of failed drug candidates in clinical trials and withdrawal of approved drugs from the market. Therefore, machine learning-based DILI prediction can be key in increasing the success rate of drug discovery because drug candidates that are predicted to potentially induce liver injury can be rejected before clinical trials. However, existing DILI prediction models mainly focus on the chemical structures of drugs. Since we cannot determine whether a drug will cause liver injury based solely on its structure, DILI prediction based on the transcriptional effect of a drug on a cell is necessary. In this paper, we propose GLIT which is a model that uses transcriptional response data and chemical structures and can be used for drug-induced liver injury prediction. GLIT learns the embedding vectors of drug structures and drug-induced gene expression profiles using graph attention networks in a biological knowledge graph for predicting DILI. GLIT outperformed a baseline model that uses only drug structure information by 7% and 19.2% in terms of correct classification rate (CCR) and Matthews correlation coefficient (MCC), respectively. In addition, we conducted a literature survey to confirm whether the class labels of drugs, in the unknown DILI class, predicted by GLIT are correct.

关键词： Drugs Gene expression Neural networks Injuries Liver Predictive models Biological system modeling

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Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records

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BioData Mining 2024年第1期17卷 1页

作者： Chung, Chih-Wei Chou, Seng-Cho Hsiao, Tzu-Hung Zhang, Grace Joyce Chung, Yu-Fang Chen, Yi-Ming Department of Information Management National Taiwan University Taipei Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Public Health Fu Jen Catholic University New Taipei Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Department of Cellular and Physiological Sciences The University of British Columbia Vancouver BC Canada Department of Electrical Engineering Tunghai University Taichung Taiwan Division of Allergy Immunology and Rheumatology Department of Internal Medicine Taichung Veterans General Hospital 1650 Section 4 Boulevard Xitun Dist. Taichung 407 Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Rong Hsing Research Center for Translational Medicine & Ph.D. Program in Translational Medicine National Chung Hsing University Taichung Taiwan Precision Medicine Research Center College of Medicine National Chung Hsing University Taichung Taiwan

Background: Although the 2019 EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) has required at least a positive anti-nuclear antibody (ANA) titer (≥ 1:80), it remains challenging for clinicians to identify patients with SLE. This study aimed to develop a machine learning (ML) approach to assist in the detection of SLE patients using genomic data and electronic health records. Methods: Participants with a positive ANA (≥ 1:80) were enrolled from the Taiwan Precision Medicine Initiative cohort. The Taiwan Biobank version 2 array was used to detect single nucleotide polymorphism (SNP) data. Six ML models, Logistic Regression, Random Forest (RF), Support Vector Machine, Light Gradient Boosting Machine, Gradient Tree Boosting, and Extreme Gradient Boosting (XGB), were used to identify SLE patients. The importance of the clinical and genetic features was determined by Shapley Additive Explanation (SHAP) values. A logistic regression model was applied to identify genetic variations associated with SLE in the subset of patients with an ANA equal to or exceeding 1:640. Results: A total of 946 SLE and 1,892 non-SLE controls were included in this analysis. Among the six ML models, RF and XGB demonstrated superior performance in the differentiation of SLE from non-SLE. The leading features in the SHAP diagram were anti-double strand DNA antibodies, ANA titers, AC4 ANA pattern, polygenic risk scores, complement levels, and SNPs. Additionally, in the subgroup with a high ANA titer (≥ 1:640), six SNPs positively associated with SLE and five SNPs negatively correlated with SLE were discovered. Conclusions: ML approaches offer the potential to assist in diagnosing SLE and uncovering novel SNPs in a group of patients with autoimmunity. © 2024, The Author(s).

关键词： Anti-nuclear antibody Machine learning Polygenic risk score Single nucleotide polymorphism Systemic lupus erythematosus

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Plant scent and plant-insect interactions—Review and outlook from a macroevolutionary perspective

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植物分类学报 2023年第3期61卷 465-486页

作者： Orlando Schwery Breanna N.Sipley Mariana P.Braga Yan Yang Roberto Rebollo Pengjuan Zu New Mexico Consortium 4200 W.Jemez RdSuite 301Los AlamosNM 87544USA Department of Biological Sciences University of Idaho875 Perimeter Dr.MS 3051MoscowID 83844USA Department of Biological Sciences Southeastern Louisiana University2400 N.Oak StHammondLA 70402USA Program for Bioinformatics and Computational Biology University of IdahoMoscowID 83844USA Department of Biology Washington University in St.LouisSt.LouisMO 63130USA Department of Ecology Swedish University of Agricultural SciencesUppsalaSweden Helsinki Life Science Institute University of HelsinkiHelsinkiFinland Key Laboratory of Genetics and Germplasm Innovation of Tropical Special Forest Trees and Ornamental Plants Ministry of EducationCollege of ForestryHainan UniversityHaikou 570228P.R.China Department of Environmental Systems Science ETH ZurichSchmelzbergstrasse 9CH-8092 ZurichSwitzerland Department of Environmental Systems Science ETH ZurichSchmelzbergstrasse 9CH-8092 ZurichSwitzerland Swiss Federal Institute of Aquatic Science and Technology(Eawag) Seestrasse 79CH-6047 KastanienbaumSwitzerland

The astonishing diversity of plants and insects and their entangled interactions are cornerstones in terrestrial ***-occurring with species diversity is the diversity of plant secondary metabolites(PSMs).So far,their estimated number is more than 200 000 compounds,which are not directly involved in plant growth and development but play important roles in helping plants handle their environment including the mediation of plant-insect ***,we use plant volatile organic compounds(VOCs),a key olfactory communication channel that mediates plant-insect interactions,as a showcase of *** spite of the cumulative knowledge of the functional,ecological,and microevolutionary roles of VOCs,we still lack a macroevolutionary understanding of how they evolved with plant-insect interactions and contributed to species diversity throughout the long coevolutionary history of plants and *** first review the literature to summarize the current state-of-the-art research on this *** then present various relevant types of phylogenetic methods suitable to answer macroevolutionary questions on plant VOCs and suggest future directions for employing phylogenetic approaches in studying plant VOCs and plant-insect ***,we found that current studies in this field are still very limited in their macroevolutionary ***,with the fast-growing development of metabolome analysis techniques and phylogenetic methods,it is becoming increasingly feasible to integrate the advances of these two *** highlight promising approaches to generate new testable hypotheses and gain a mechanistic understanding of the macroevolutionary roles of chemical communication in plant-insect interactions.

关键词： coevolution

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Propensity score weighting under limited overlap and model misspecification

arXiv

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arXiv 2020年

作者： Zhou, Yunji Matsouaka, Roland A. Thomas, Laine Department of Biostatistics and Bioinformatics Duke Global Health Institute Duke University DurhamNC United States Department of Biostatistics and Bioinformatics & Program for Comparative Effectiveness Methodology Duke Clinical Research Institute Duke University DurhamNC United States

Propensity score (PS) weighting methods are often used in non-randomized studies to adjust for confounding and assess treatment effects. The most popular among them, the inverse probability weighting (IPW), assigns weights that are proportional to the inverse of the conditional probability of a specific treatment assignment, given observed covariates. A key requirement for IPW estimation is the positivity assumption, i.e., the PS must be bounded away from 0 and 1. In practice, violations of the positivity assumption often manifest by the presence of limited overlap in the PS distributions between treatment groups. When these practical violations occur, a small number of highly influential IPW weights may lead to unstable IPW estimators, with biased estimates and large variances. To mitigate these issues, a number of alternative methods have been proposed, including IPW trimming, overlap weights (OW), matching weights (MW), and entropy weights (EW). Because OW, MW, and EW target the population for whom there is equipoise (and with adequate overlap) and their estimands depend on the true PS, a common criticism is that these estimators may be more sensitive to misspecifications of the PS model. In this paper, we conduct extensive simulation studies to compare the performances of IPW and IPW trimming against those of OW, MW, and EW under limited overlap and misspecified propensity score models. Across the wide range of scenarios we considered, OW, MW, and EW consistently outperform IPW in terms of bias, root mean squared error, and coverage probability. Copyright © 2020, The Authors. All rights reserved.

关键词： Trimming

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How Retroactivity Affects the Behavior of Incoherent Feed-Forward Loops

arXiv

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arXiv 2020年

作者： Wang, Junmin Belta, Calin Isaacson, Samuel A. Bioinformatics Graduate Program Boston University BostonMA United States Department of Mathematics and Statistics Boston University BostonMA United States

An incoherent feed-forward loop (IFFL) is a network motif known for its ability to accelerate responses and generate pulses. Though functions of IFFLs are well studied, most previous computational analysis of IFFLs used ordinary differential equation (ODE) models where retroactivity, the effect downstream binding sites exert on the dynamics of an upstream transcription factor (TF), was not considered. It remains an open question to understand the behavior of IFFLs in contexts with high levels of retroactivity, e.g., in cells transformed/transfected with high-copy plasmids, or in eukaryotic cells where a TF binds to numerous high-affinity binding sites in addition to one or more functional target sites. Here we study the behavior of IFFLs by simulating and comparing ODE models with different levels of retroactivity. We find that increasing retroactivity in an IFFL can increase, decrease, or keep the network’s response time and pulse amplitude constant. This suggests that increasing retroactivity, traditionally considered as an impediment to designing robust synthetic systems, could be exploited to improve the performance of IFFLs. We compare the behaviors of IFFLs to negative autoregulatory loops, another sign-sensitive response-accelerating network motif, and find that increasing retroactivity in a negative autoregulated circuit can only slow the response. The inability of a negative autoregulatory loop to flexibly handle retroactivity may have contributed to its lower abundance in eukaryotic relative to bacterial regulatory networks, a sharp contrast to the significant abundance of IFFLs in both cell types. Copyright © 2020, The Authors. All rights reserved.

关键词： Transcription

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AI solutions for drafting in Magic: The gathering

arXiv

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arXiv 2020年

作者： Ward, Henry N. Brooks, Daniel J. Troha, Dan Mills, Bobby Khakhalin, Arseny S. Bioinformatics and Computational Biology Graduate Program University of Minnesota MinneapolisMN United States *** San DiegoCA United States Biology Program Bard College Annandale-on-HudsonNY United States

Drafting in Magic the Gathering is a sub-game within a larger trading card game, where several players progressively build decks by picking cards from a common pool. Drafting poses an interesting problem for game and AI research due to its large search space, mechanical complexity, multiplayer nature, and hidden information. Despite this, drafting remains understudied, in part due to a lack of high-quality, public datasets. To rectify this problem, we present a dataset of over 100,000 simulated, anonymized human drafts collected from ***. We also propose four diverse strategies for drafting agents, including a primitive heuristic agent, an expert-tuned complex heuristic agent, a Naive Bayes agent, and a deep neural network agent. We benchmark their ability to emulate human drafting, and show that the deep neural network agent outperforms other agents, while the Naive Bayes and expert-tuned agents outperform simple heuristics. We analyze the accuracy of AI agents across the timeline of a draft, and describe unique strengths and weaknesses for each approach. This work helps to identify next steps in the creation of humanlike drafting agents, and can serve as a benchmark for the next generation of drafting bots. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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