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Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information

Genomics, Proteomics & bioinformatics 2020年第1期18卷 52-64页

作者： Fuyi Li Andre Leier Quanzhong Liu Yanan Wang Dongxu Xiang Tatsuya Akutsu Geoffrey IWebb A.Ian Smith Tatiana Marquez-Lago Jian Li Jiangning Song Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash UniversityMelbourneVIC 3800Australia Monash Centre for Data Science Faculty of Information TechnologyMonash UniversityMelbourneVIC 3800Australia School of Medicine University of Alabama at BirminghamBirminghamAL 35233USA College of Information Engineering Northwest A&F UniversityYangling 712100China Bioinformatics Center Institute for Chemical ResearchKyoto UniversityUjiKyoto 611-0011Japan ARC Centre of Excellence in Advanced Molecular Imaging Monash UniversityMelbourneVIC 3800Australia Biomedicine Discovery Institute and Department of Microbiology Monash UniversityMelbourneVIC 3800Australia

Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate ***-controlled proteolysis plays a key role in the degradation and recycling of proteins,which is essential for various physiological ***,solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases,as well as for therapeutic target identification and pharmaceutical ***,there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural *** this study,we present Procleave,a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural *** features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method,which turned out to be critical for the performance of *** optimal approximations of all structural parameter values were encoded in a conditional random field(CRF)computational framework,alongside sequence and chemical group-based ***,we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent *** is capable of correctly identifying most cleavage sites in the case ***,when applied to the human structural proteome encompassing 17,628 protein structures,Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different *** is implemented as a webserver and is freely accessible at http://***/.

关键词： Protease Cleavage site prediction Machine learning Conditional random field Structural determinants

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54341 Study Design and Recruitment into the North American Post-authorization Safety Study (PASS) to Monitor Pregnancy and Infant Outcomes Following Dupilumab Exposure During Pregnancy

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Journal of the American Academy of Dermatology 2024年第3期91卷 AB87-AB87页

作者： Johnson, Diana, MS Xu, Ronghui, PhD Luo, Yunjun, MS Jones, Kenneth Lyons, MD Chambers, Christina, PhD, MPH University of California San Diego Lead Teaching Division of Biostatistics and Bioinformatics Director Research Program of Quantitative Methods in Public Health Herbert Wertheim School of Public Health Department of Mathematics Halicioglu Data Science Institute Department of Pediatric Department of Pediatrics University of California San Diego La Jolla CA USA Department of Pediatrics University of California San Diego La Jolla CA USA

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Hybrid feature selection to identify important single nucleotide polymorphism for asthma classification

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AIP Conference Proceedings 2024年第1期2991卷

作者： Wan Ni Wong Weng Howe Chan Hui Wen Nies Kauthar Mohd Daud Artificial Intelligence and Bioinformatics Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Center for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia

Single nucleotide polymorphism (SNP), being the most common genetic variation, is found to be responsible for traits and complex diseases such as asthma. Polygenic risk scoring (PRS) and machine learning (ML) are the current two common types of genetic risk prediction models. PRS employs statistical association testing to identify alleles with risk. However, it was found not performing well in unraveling the SNP-SNP interactions and attaining low predictive value. In contrast, ML algorithms are more robust in detecting disease associated SNPs, resulting in an improvement in prediction performance. The challenge remains as the high dimensionality nature of SNP tends to cause overfitting problems and impact the classifier performance. This paper introduces a two-stage hybrid feature selection that integrates Mutual Information and Random Forest Recursive Feature Elimination for identifying informative SNPs from 176,287 SNPs in a dataset of 128 asthmatic and non-asthmatic samples. The reduced samples based on the identified informative SNPs are then used with Support Vector Machine to train a classification model. The model from this paper outperforms other ML models with an average accuracy, precision, recall and area under the curve of 98.91%, 98.36%, 99.72% and 0.99 respectively. Additional analysis shows that the genes associated with the top selected SNPs, namely Ring Finger Protein 217 (RNF217) and A-kinase-anchoring protein 6 (AKAP6) are correlated with respiratory diseases. This shows that the proposed method can identify important SNPs that are able to differentiate between asthmatic and non-asthmatic samples.

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Semiparametric Sensitivity Analysis: Unmeasured Confounding In Observational Studies

arXiv

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arXiv 2021年

作者： Scharfstein, Daniel O. Nabi, Razieh Kennedy, Edward H. Huang, Ming-Yueh Bonvini, Matteo Smid, Marcela Department of Population Health Sciences University of Utah School of Medicine Salt Lake CityUT United States Department of Biostatistics and Bioinformatics Emory University AtlantaGA United States Department of Statistics & Data Science Carnegie Mellon University PittsburghPA United States Institute of Statistical Science Academia Sinica Taipei Taiwan Department of Statistics Rutgers University New BrunswickNJ United States Department of Obstetrics and Gynecology University of Utah School of Medicine Salt Lake CityUT United States

Establishing cause-effect relationships from observational data often relies on untestable assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from non-experimental studies are robust to potential unmeasured confounding. In this paper, we focus on the average causal effect (ACE) as our target of inference. We generalize the sensitivity analysis approach developed by Robins et al. (2000), Franks et al. (2020) and Zhou and Yao (2023).We use semiparametric theory to derive the non-parametric efficient influence function of the ACE, for fixed sensitivity parameters. We use this influence function to construct a one-step, split sample, truncated estimator of the ACE. Our estimator depends on semiparametric models for the distribution of the observed data;importantly, these models do not impose any restrictions on the values of sensitivity analysis parameters. We establish sufficient conditions ensuring that our estimator has √n asymptotics. We use our methodology to evaluate the causal effect of smoking during pregnancy on birth weight. We also evaluate the performance of estimation procedure in a simulation study. © 2021, CC BY.

关键词： Sensitivity analysis

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Maximum pairwise-rank-likelihood-based inference for the semiparametric transformation model

arXiv

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arXiv 2021年

作者： Yu, Tao Li, Pengfei Chen, Baojiang Yuan, Ao Qin, Jing Department of Statistics & Applied Probability National University of Singapore Singapore117546 Singapore Department of Statistics and Actuarial Science University of Waterloo WaterlooONN2L 3G1 Canada Department of Biostatistics and Data Science University of Texas Health Science Center at Houston School of Public Health in Austin AustinTX78701 United States Department of Biostatistics Bioinformatics and Biomathematics Georgetown University WashingtonDC20057 United States National Institute of Allergy and Infectious Diseases National Institutes of Health MD20892 United States

MSC Codes 62J02, 62J05, 62J86In this paper, we study the linear transformation model in the most general setup. This model includes many important and popular models in statistics and econometrics as special cases. Although it has been studied for many years, the methods in the literature are based on kernel-smoothing techniques or make use of only the ranks of the responses in the estimation of the parametric components. The former approach needs a tuning parameter, which is not easily optimally specified in practice;and the latter is computationally expensive and may not make full use of the information in the data. In this paper, we propose two methods: a pairwise rank likelihood method and a score-function-based method based on this pairwise rank likelihood. We also explore the theoretical properties of the proposed estimators. Via extensive numerical studies, we demonstrate that our methods are appealing in that the estimators are not only robust to the distribution of the random errors but also lead to mean square errors that are in many cases comparable to or smaller than those of existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Linear transformations

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O002 High strain evening exercise disrupts sleep: Insights from a real-world examination of ~7-million nights

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Sleep Advances 2023年第SUPPLEMENT_1期4卷 A1-A1页

作者： Leota, J Presby, D Czeisler, M Mascaro, L Capodilupo, E Drummond, S Rajaratnam, S Facer-Childs, E Turner Institute for Brain and Mental Health Monash University Melbourne Australia Department of Data Science and Research Whoop Inc. Boston United States of America Swiss Institute of Bioinformatics Lausanne Switzerland Department of Computational Biology University of Lausanne Lausanne Switzerland Francis Weld Peabody Society Harvard Medical School Boston United States of America

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What makes a reaction network "chemical"?

arXiv

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arXiv 2022年

作者： Müller, Stefan Flamm, Christoph Stadler, Peter F. Faculty of Mathematics University of Vienna Oskar-Morgenstern-Platz 1 WienA-1090 Austria Department of Theoretical Chemistry University of Vienna Währinger Straße 17 WienA-1090 Austria Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig & Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases University Leipzig LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Faculdad de Ciencias Universidad Nacional de Colombia Sede Bogotá Ciudad Universitaria DC BogotáCOL-111321 Colombia Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States

Background: Reaction networks (RNs) comprise a set X of species and a set R of reactions Y → Y′, each converting a multiset of educts Y ⊆ X into a multiset Y′ ⊆ X of products. RNs are equivalent to directed hypergraphs. However, not all RNs necessarily admit a chemical interpretation. Instead, they might contradict fundamental principles of physics such as the conservation of energy and mass or the reversibility of chemical reactions. The consequences of these necessary conditions for the stoichiometric matrix S ∈ X×R have been discussed extensively in the chemical literature. Here, we provide sufficient conditions for S that guarantee the interpretation of RNs in terms of balanced sum formulas and structural formulas, respectively. Results: Chemically plausible RNs allow neither a perpetuum mobile, i.e., a "futile cycle" of reactions with non-vanishing energy production, nor the creation or annihilation of mass. Such RNs are said to be thermodynamically sound and conservative. For finite RNs, both conditions can be expressed equivalently as properties of the stoichiometric matrix S. The first condition is vacuous for reversible networks, but it excludes irreversible futile cycles and – in a stricter sense – futile cycles that even contain an irreversible reaction. The second condition is equivalent to the existence of a strictly positive reaction invariant. Furthermore, it is sufficient for the existence of a realization in terms of sum formulas, obeying conservation of "atoms". In particular, these realizations can be chosen such that any two species have distinct sum formulas, unless S implies that they are "obligatory isomers". In terms of structural formulas, every compound is a labeled multigraph, in essence a Lewis formula, and reactions comprise only a rearrangement of bonds such that the total bond order is preserved. In particular, for every conservative RN, there exists a Lewis realization, in which any two compounds are realized by pairwisely distinct mu

关键词： Energy conservation

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Translation dysregulation in cancer as a source for targetable antigens

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Cancer Cell 2025年第5期43卷 823-840.e18,20页

作者： Weller, Chen Bartok, Osnat McGinnis, Christopher S. Palashati, Heyilimu Chang, Tian-Gen Malko, Dmitry Shmueli, Merav D. Nagao, Asuteka Hayoun, Deborah Murayama, Ayaka Sakaguchi, Yuriko Poulis, Panagiotis Khatib, Aseel Erlanger Avigdor, Bracha Gordon, Sagi Cohen Shvefel, Sapir Zemanek, Marie J. Nielsen, Morten M. Boura-Halfon, Sigalit Sagie, Shira Gumpert, Nofar Yang, Weiwen Alexeev, Dmitry Kyriakidou, Pelgia Yao, Winnie Zerbib, Mirie Greenberg, Polina Benedek, Gil Litchfield, Kevin Petrovich-Kopitman, Ekaterina Nagler, Adi Oren, Roni Ben-Dor, Shifra Levin, Yishai Pilpel, Yitzhak Rodnina, Marina Cox, Jürgen Merbl, Yifat Satpathy, Ansuman T. Carmi, Yaron Erhard, Florian Suzuki, Tsutomu Buskirk, Allen R. Olweus, Johanna Ruppin, Eytan Schlosser, Andreas Samuels, Yardena Department of Molecular Cell Biology Weizmann Institute of Science Rehovot 7610001 Israel Department of Pathology Stanford University Stanford 94305 CA United States Parker Institute for Cancer Immunotherapy San Francisco 94129 CA United States Department of Cancer Immunology Institute for Cancer Research Oslo University Hospital Oslo 0379 Norway Precision Immunotherapy Alliance University of Oslo Oslo Norway Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda 20892 MD United States Department of Systems Immunology Weizmann Institute of Science Rehovot 7610001 Israel Department of Chemistry and Biotechnology of Engineering The University of Tokyo Tokyo 113-8656 Japan Department of Physical Biochemistry Max Planck Institute for Multidisciplinary Sciences Göttingen 37077 Germany Department of Pathology Sackler School of Medicine Tel Aviv University Tel Aviv 69978 Israel Department of Immunology and Regenerative Biology Weizmann Institute of Science Rehovot 7610001 Israel Computational Systems Biochemistry Research Group Max-Planck Institute of Biochemistry Martinsried 82152 Germany Department of Veterinary Resources Weizmann Institute of Science Rehovot 7610001 Israel Tissue Typing and Immunogenetics Unit Hadassah Hebrew University Hospital Jerusalem 9112102 Israel CRUK Lung Cancer Centre of Excellence University College London Cancer Institute London WC1E 6DD United Kingdom Tumour Immunogenomics and Immunosurveillance Laboratory University College London Cancer Institute London WC1E 6DD United Kingdom Department of Life Science Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel Bioinformatics Unit Department of Life Sciences Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel de Botton Institute for Protein Profiling the Nancy and Stephen Grand Israel National Center for Personalized Medicine Weizmann Institute of Science Rehovot

Aberrant peptides presented by major histocompatibility complex (MHC) molecules are targets for tumor eradication, as these peptides can be recognized as foreign by T cells. Protein synthesis in malignant cells is dysregulated, which may result in the generation and presentation of aberrant peptides that can be exploited for T cell-based therapies. To investigate the role of translational dysregulation in immunological tumor control, we disrupt translation fidelity by deleting tRNA wybutosine (yW)-synthesizing protein 2 (TYW2) in tumor cells and characterize the downstream impact on translation fidelity and immunogenicity using immunopeptidomics, genomics, and functional assays. These analyses reveal that TYW2 knockout (KO) cells generate immunogenic out-of-frame peptides. Furthermore, Tyw2 loss increases tumor immunogenicity and leads to anti-programmed cell death 1 (PD-1) checkpoint blockade sensitivity in vivo. Importantly, reduced TYW2 expression is associated with increased response to checkpoint blockade in patients. Together, we demonstrate that defects in translation fidelity drive tumor immunogenicity and may be leveraged for cancer immunotherapy. © 2025 The Author(s)

关键词： cancer immunopeptidome immune checkpoint blockade neoantigens non-canonical peptides translation fidelity

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Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

arXiv

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arXiv 2021年

作者： Xie, Feng Yuan, Han Ning, Yilin Ong, Marcus Eng Hock Feng, Mengling Hsu, Wynne Chakraborty, Bibhas Liu, Nan Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Singapore School of Computing National University of Singapore Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Health Services Research Centre Singapore Health Services Singapore Singapore

Objective: Temporal electronic health records (EHRs) can be a wealth of information for secondary uses, such as clinical events prediction or chronic disease management. However, challenges exist for temporal data representation. We therefore sought to identify these challenges and evaluate novel methodologies for addressing them through a systematic examination of deep learning solutions. Methods: We searched five databases (PubMed, EMBASE, the Institute of Electrical and Electronics Engineers [IEEE] Xplore Digital Library, the Association for Computing Machinery [ACM] digital library, and Web of science) complemented with hand-searching in several prestigious computer science conference proceedings. We sought articles that reported deep learning methodologies on temporal data representation in structured EHR data from January 1, 2010, to August 30, 2020. We summarized and analyzed the selected articles from three perspectives: nature of time series, methodology, and model implementation. Results: We included 98 articles related to temporal data representation using deep learning. Four major challenges were identified, including data irregularity, data heterogeneity, data sparsity, and model opacity. We then studied how deep learning techniques were applied to address these challenges. Finally, we discuss some open challenges arising from deep learning. Conclusion: Temporal EHR data present several major challenges for clinical prediction modeling and data utilization. To some extent, current deep learning solutions can address these challenges. Future studies can consider designing comprehensive and integrated solutions. Moreover, researchers should incorporate additional clinical domain knowledge into study designs and enhance the interpretability of the model to facilitate its implementation in clinical practice. © 2021, CC BY.

关键词： Records management

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Anomaly Detection in Climate data Using Stacked and Densely Connected Long Short-Term Memory Model

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Journal of Computers (Taiwan) 2020年第4期31卷 42-53页

作者： Saleh Abbas, Bahtiar Wahyono, Teguh Heryadi, Yaya Soeparno, Haryono Doctor of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Industrial Engineering Bina Nusantara University Jakarta Indonesia

Climate anomalies are considered as an important factor closely related to many disasters causing many human losses, such as airline crash, wildfires, drought and flooding in many areas. Many researchers have projected that the rising global temperature will increase the draught, especially in the mid-latitude areas. Taking those problems into account, studies on anomaly detection in climate are crucial. While climate prediction aims to analyze and model regular pattern of climate, climate anomaly studies aim to model climate deviation from its previous general patterns. Long Short Term Memory (LSTM) is a method employed in this research because it has been proven to work effectively in several anomaly detection studies, especially for data with similar characteristics. This paper presents empirical results of using Basic LSTM, Densely Connected (DC) LSTM, and Stacked-DC LSTM models to detect temperature anomaly as a manifestation of climate change in Semarang City. The results show that Stacked DC LSTM produced higher accuracy in detecting anomaly than the other two methods. © 2020 Computer Society of the Republic of China. All rights reserved.

关键词： Long short-term memory

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