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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Goo, Taewan Lee, Chanhee Shin, Sangyeon Kim, Haeyoung Park, Taesung Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of Seoul National University Department of Statistics Seoul Korea Republic of

ISBN: (纸本)9798350386226

In precision medicine, deep learning models play an important role in identifying relevant biomarkers and making predictions for the early diagnosis and prognosis of diseases. While successful in developing accurate prediction models for prognosis and diagnosis, the 'black box' nature of deep learning poses significant challenges, which makes it difficult to have a meaningful biological interpretation. Recently, several biologically informed neural networks (BINNs), such as the Visible Neural Network and P-NET, have been proposed to incorporate biological knowledge for improved interpretation. These models assess node importance using quantitative measures such as SHAP and DeepLIFT. However, these approaches have limitations because they typically provide a single estimate of node importance without any information on the uncertainty of these estimates. This study aims to assess uncertainty of node importance through a novel statistical framework. Bootstrapping is used to calculate confidence interval of node importance. Our findings validate that the proposed statistical framework quantifies the uncertainty of nodes in BINNs. By providing robust confidence intervals of pathways and genes, this framework enhances the interpretability and reliability of BINNs. © 2024 IEEE.

关键词： Neural network models

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On codeword design in metric DNA spaces

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Natural Computing 2008年第3期8卷 571-588页

作者： Vinhthuy Phan Max H. Garzon Department of Computer Science and the Bioinformatics Program University of Memphis Memphis USA

Finding a large set of single DNA strands that do not crosshybridize to themselves and/or to their complements is an important problem in DNA computing, self-assembly, and DNA memories. We describe a theoretical framework to analyze this problem, gauge its computational difficulty, and provide nearly optimal solutions. In this framework, codeword design is reduced to finding large sets of strands maximaly separated in a DNA space and the size of such sets depends on the geometry of these metric spaces. We show that codeword design is NP-complete using any single reasonable measure that approximates the Gibbs energy, thus practically excluding the possibility of finding any procedure to find maximal sets efficiently. Second, we extend a technique known as shuffling to provide a construction that yields provably nearly-maximal codes. Third, we propose a filtering process that removes strands creating pairs with low Gibbs energies, as approximated by the nearest-neighbor model. These two steps produce large codes of thermodynamic high quality. The proposed framework can be used to gain an understanding of the Gibbs energy landscapes for DNA strands on which much of DNA computing and self-assembly are based.

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Wilms’tumor gene(WT1)is strongly expressed in high-risk subsets of pediatric acute lymphoblastic leukemia

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Cancer Drug Resistance 2018年第4期1卷 250-265页

作者： Fatih M.Uckun Sanjive Qazi Biomarker Research Program AresMIT Biomedical Computational Strategies(ABCS)MinneapolisMinnesota 55110USA Biotherapy Program Ares PharmaceuticalsLLCSt.PaulMinnesota 55110USA Bioinformatics Program Gustavus Adolphus CollegeSt.PeterMinnesota 56082USA

Aim:The purpose of the present study was to perform a comprehensive analysis of WT1 gene expression in high-risk pediatric acute lymphoblastic leukemia(ALL).Methods:We performed a meta-analysis of WT1 gene expression for normal hematopoietic cells *** leukemia cells from 801 pediatric ALL samples deposited in the Oncomine database combined with an in-depth gene expression analysis using our in-house database of gene expression profiles of primary leukemia cells from 1416 pediatric ALL *** also examined the expression of WT1 in primary leukemic cells from 299 T-lineage ALL patients in the Oncomine database and 189 T-lineage ALL patients in the archived datasets GSE13159,GSE13351,and ***:Our data provide unprecedented evidence that primary leukemia cells from patients with MLL gene rearrangements(MLL-R)express highest levels of WT1 expression within the high-risk subsets of pediatric B-lineage ***,MLL-R^(+)patients exhibited>6-fold higher expression levels of the WT1 gene compared to the other B-lineage ALL subtypes combined(P<0.0001).Our findings in 97 MLL-R^(+)infant B-lineage ALL cases uniquely demonstrated that WT1 is expressed at 1.5-4.2-fold higher levels in MLL-R^(+)infant leukemia cells than in normal hematopoietic cells and revealed that WT1 expression level was substantially higher in steroid-resistant infant leukemia cells when compared to non-leukemic healthy bone marrow ***,our study demonstrates for the first time that the WT1-regulated EWSR1,TP53,U2AF2,and WTAP genes(i.e.,WT1 interactome)were differentially upregulated in MLL-R^(+)leukemia cells illustrating that the MLL-regulatory pathway is aberrantly upregulated in MLL-R^(+)pediatric B-lineage *** novel insights provide a compelling rationale for targeting WT1 in second line treatment of MLL-R^(+)pediatric B-lineage ALL,including MLL-R^(+)infant ***,our study is the first to demonstrate that leukemia cells from 370 Ph-like patients had

关键词： Wilms’tumor gene WT1 gene leukemia chemotherapy resistance immunotherapy

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In vivo programming of adult pericytes aids axon regeneration by providing cellular bridges for SCI repair

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Molecular Therapy 2025年

作者： Sun, Wenjing Dion, Elliot Laredo, Fabio Okonak, Allyson Sepeda, Jesse A. Haykal, Esraa Zhou, Min El-Hodiri, Heithem M. Fischer, Andy J. Silver, Jerry Peng, Juan Sas, Andrew Tedeschi, Andrea Department of Neuroscience Wexner Medical Center The Ohio State University Columbus 43210 OH United States University of Nottingham Nottingham NG7 2QL United Kingdom Department of Neurology Wexner Medical Center The Ohio State University Columbus 43210 OH United States Department of Neurosciences Case Western Reserve University Cleveland 44106 OH United States Center for Biostatistics and Bioinformatics The Ohio State University Columbus 43210 OH United States Chronic Brain Injury Program The Ohio State University Columbus 43210 OH United States

Pericytes are contractile cells of the microcirculation that participate in wound healing after spinal cord injury (SCI). Thus far, the extent to which pericytes cause or contribute to axon growth and regeneration failure after SCI remains controversial. Here, we found that SCI leads to profound changes in vasculature architecture and pericyte coverage. We demonstrated that pericytes constrain sensory axons on their surface, causing detrimental structural and functional changes in adult dorsal root ganglion neurons that contribute to axon regeneration failure after SCI. Perhaps more excitingly, we discovered that in vivo programming of adult pericytes via local administration of platelet-derived growth factor BB (PDGF-BB) effectively promotes axon regeneration and recovery of hindlimb function by contributing to the formation of cellular bridges that span the lesion. Ultrastructural analysis showed that PDGF-BB induced fibronectin fibril alignment and extension, effectively converting adult pericytes into a permissive substrate for axon growth. In addition, PDGF-BB localized delivery positively affects the physical and chemical nature of the lesion environment, thereby creating more favorable conditions for SCI repair. Thus, therapeutic manipulation rather than wholesale ablation of pericytes can be exploited to prime axon regeneration and SCI repair. © 2025 The Author(s)

关键词： axon regeneration DRG neurons extracellular matrix functional recovery PDGF-BB pericytes spinal cord injury vasculature remodeling

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B-cell Ligand Processing Pathways Detected by Large-scale Comparative Analysis

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Genomics, Proteomics & bioinformatics 2012年第3期10卷 142-152页

作者： Fadi Towfic Shakti Gupta Vasant Honavar Shankar Subramaniam Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIA 50010USA Department of Bioengineering University of California at San DiegoLa JollaCA 92122USA

The initiation of B-cell ligand recognition is a critical step for the generation of an immune response against foreign bodies. We sought to identify the biochemical pathways involved in the B-cell ligand recognition cascade and sets of ligands that trigger similar immunological responses. We utilized several comparative approaches to analyze the gene coexpression networks generated from a set of microarray experiments spanning 33 different ligands. First, we compared the degree distributions of the generated networks. Second, we utilized a pairwise network alignment algorithm, BiNA, to align the networks based on the hubs in the networks. Third, we aligned the networks based on a set of KEGG pathways. We summarized our results by constructing a consensus hierarchy of pathways that are involved in B cell ligand recognition. The resulting pathways were further validated through literature for their common physiological responses. Col- lectively, the results based on our comparative analyses of degree distributions, alignment of hubs, and alignment based on KEGG path- ways provide a basis for molecular characterization of the immune response states of B-cells and demonstrate the power of comparative approaches （e.g., gene coexpression network alignment algorithms） in elucidating biochemical pathways involved in complex signaling events in ceils.

关键词： Ligand recognition B-cells Gene coexpression network alignment

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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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Expression of matrix metalloproteinase 9 (MMP-9/gelatinase B) in adenocarcinomas strongly correlated with expression of immune response genes

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In Silico Biology 2003年第3期3卷 301-311页

作者： Creighton, Chad Hanash, Samir Bioinformatics Program University of Michigan Ann Arbor MI 48109 United States Department of Pediatrics University of Michigan Ann Arbor MI 48109 United States

Matrix metalloproteinases (MMPs) are endopeptidases considered to be important regulators of the microenvironment of cancer. While MMPs are traditionally associated with the extracellular matrix (ECM), here we provide new evidence from an analysis of gene expression profiles from human tumor tissue that MMP-9 (gelatinase B) is associated with elements of the immune system in a way analogous to the association of other MMPs, such as MMP-2 (gelatinase A), with components of the ECM. An analysis of three independent microarray datasets of lung adenocarcinomas from previous studies [Nat. Med. 8, 816-824 (2002);Proc. Natl. Acad. Sci. USA 98, 13790-13795 (2001);Proc. Natl. Acad. Sci. USA, 98, 13784-13789 (2001)] showed that, in each dataset, out of the set of genes with significant correlations in mRNA expression to the expression of MMP9 (P < 0.005), a highly disproportionate number were found to be annotated in the Locuslink database as having a role in the anti-pathogen response. By comparison, out of the set of genes significantly correlated with the expression of MMP2, a highly disproportionate number were known components of the ECM. The same patterns observed in the lung data for both MMP2 and MMP9 were found as well in an additional published dataset of colon and ovarian adenocarcinomas [Am. J. Pathol. 159, 1231-1238 (2001)]. The results of this study suggest a greater functional role for MMP-9 in the immune response to cancer than what may previously have been thought.

关键词： Data mining Immune response Matrix metalloproteinases Microarrays MMP9

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Confidence Interval Estimation for Machine Learning Models in Forecasting Infectious Diseases

Confidence Interval Estimation for Machine Learning Models i...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Goo, Taewan Han, Kyulhee Song, Hanbyul Park, Jiwon Liu, Zhe Oh, Jooha Jose, Sayooj Aby Park, Taesung Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of Seoul National University Department of Statistics Seoul Korea Republic of

ISBN: (纸本)9798350386226

Forecasting models have been instrumental in managing the COVID-19 pandemic by facilitating efficient resource distribution and proper interventions. Although various forecasting models - such as mathematical models, statistical models, and machine learning models - are available, a few models account for the uncertainty in their predictions. The lack of uncertainty information constrains the reliability of forecasting results, making them less applicable for use in decision-making. Confidence Intervals (CIs) are widely used in statistical inference to provide uncertainty information. In this study, we introduce a framework that provides bootstrap-based CIs readily applicable to various forecasting models. The key concept of this framework is repeated training on bootstrap data based on resampled residuals from the forecasting model. After fitting the forecasting model, bootstrap datasets are generated considering time series characteristics to retrain the forecasting models. Then, CIs are calculated based on the distribution of prediction values. This framework was applied to forecasting COVID-19 outcomes - such as daily confirmed COVID-19 cases, deaths, and ICU patients - in South Korea. Our results demonstrate that bootstrap-based CIs can be successfully applied to provide additional information on the reliability of prediction. In conclusion, our framework can provide uncertainty information without requiring complex assumptions, regardless of the forecasting model or response variable type. Therefore, this approach can support public health policymakers in gaining a deeper understanding of epidemic trends, thereby offering valuable insights for decision-making. © 2024 IEEE.

关键词： COVID-19

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Mapping Emergency Medicine Data to the Observational Medical Outcomes Partnership Common Data Model: A Gap Analysis of the American College of Emergency Physicians Clinical Emergency Data Registry

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JACEP Open 2025年第1期6卷 100016-100016页

作者： Cohen, Inessa Diao, Zihan Goyal, Pawan Gupta, Aarti Hawk, Kathryn Malcom, Bill Malicki, Caitlin Sharma, Dhruv Sweeney, Brian Weiner, Scott G. Venkatesh, Arjun Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States American College of Emergency Physicians Washington DC United States Department of Emergency Medicine Brigham and Women's Hospital Boston MA United States Center for Outcomes Research and Evaluation (CORE) Section of Cardiovascular Medicine Yale School of Medicine New Haven CT United States

Objectives: This study aims to conduct a gap analysis to determine the feasibility of mapping electronic health record data from the Clinical Emergency Data Registry (CEDR) to the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM). Methods: We employed a structured approach using a custom-built comparison matrix. This matrix facilitated the alignment of CEDR data fields with the corresponding elements in the OMOP-CDM schema. Each field was evaluated for compatibility, with categorization into 3 distinct types: direct matches, fields requiring transformation, and fields with no OMOP-CDM equivalent. The mapping process was informed by consultations with the Observational Health Data Sciences and Informatics community forums and was guided by existing documentation and best practices in data harmonization. We performed descriptive analyses, quantifying the extent of direct matches and identifying the specific transformations needed for each CEDR-CDM field to ensure compliance with the OMOP-CDM model. Results: Our analysis indicates a high degree of compatibility between CEDR and OMOP, with over 90% (244/269) of CEDR fields being successfully mapped. Specifically, 173 fields had direct matches, whereas 71 required transformations. Challenges identified include addressing fields unique to CEDR with no OMOP-CDM equivalent and managing the transformations required for proper alignment. Conclusion: The OMOP-CDM presents a promising framework for standardizing emergency medicine data, thereby enhancing future query automation, analytics, and cross-institutional collaboration. Despite the potential challenges in capturing unique CEDR fields and addressing necessary transformations, most emergency department data can be standardized within the OMOP-CDM, fostering broader insights and applications in research and public health. © 2024 The Author(s)

关键词： databases electronic health records emergency service informatics opioid/adverse effects quality of health care registries

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Fast and accurate calculation of protein depth by Euclidean distance transform

Fast and accurate calculation of protein depth by Euclidean ...

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17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013

作者： Xu, Dong Li, Hua Zhang, Yang Bioinformatics and Systems Biology Program Sanford-Burnham Medical Research Institute United States Department of Computational Medicine and Bioinformatics University of Michigan United States Integration Application Center Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9783642371943

The depth of each atom/residue in a protein structure is a key attribution that has been widely used in protein structure modeling and function annotation. However, the accurate calculation of depth is time consuming. Here, we propose to use the Euclidean distance transform (EDT) to calculate the depth, which conveniently converts the protein structure to a 3D gray-scale image with each pixel labeling the minimum distance of the pixel to the surface of the molecule (i.e. the depth). We tested the proposed EDT method on a set of 261 non-redundant protein structures. The data show that the EDT method is 2.6 times faster than the widely used method by Chakravarty and Varadarajan. The depth value by EDT method is also highly accurate, which is almost identical to the depth calculated by exhaustive search (Pearson's correlation coefficient≈1). We believe the EDT-based depth calculation program can be used as an efficient tool to assist the studies of protein fold recognition and structure-based function annotation. © 2013 Springer-Verlag.

关键词： Proteins

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