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Machine Learning: An Analytical Approach for Pattern Detection in Diabetes 1

8th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Chauhan, Ritu Goel, Anika Kaur, Harleen Alankar, Bhavya Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Jamia Hamdard Delhi India

ISBN: (数字)9789811998584

ISBN: (纸本)9789811998577

In current times, the amount of data is increasing exponentially but generated data is not transformed into knowledge data which eventually serves no purpose. This issue can be resolved by applying machine learning approaches which prove to play a promising role in the field of prediction. Similarly, in healthcare industries, there is a supply of large amount of data for which it is necessary to extract information for prognosis, diagnosis, and medication development. In this paper, we have focused on diagnosis of diabetes which is a chronic disease with fastest global growth rate. The current study is carried out in two phases where in first phase data is pre-processed to remove the unwanted and missing values and in second phase the data is analyzed by model building using machine learning algorithm. Additionally, we have attempted machine learning techniques in our study such as decision tree classifier and logistic regression which can be used for diagnosis of diabetes, and we attain the accuracy of 75.9% for decision tree classifier and 74.6% for logistic regression. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Large-Scale Analyses of Glycosylation in Cellulases

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Genomics, Proteomics & bioinformatics 2009年第4期7卷 194-199页

作者： Fengfeng Zhou Victor Olman Ying Xu Computational Systems Biology Laboratory Department of Biochemistry and Molecular Biology/Institute of Bioinformatics University of Georgia Athens GA 30602-7229 USA BioEnergy Science Center Oak Ridge National Laboratory Oak Ridge TN 37830-8050 USA.

Cellulases are important glycosyl hydrolases （GHs） that hydrolyze cellulose polymers into smaller oligosaccharides by breaking the cellulose β （1→4） bonds, and they are widely used to produce cellulosic ethanol from the plant biomass. N-linked and O-linked glycosylations were proposed to impact the catalytic efficiency, cellulose binding affinity and the stability of cellulases based on observations of individual cellulases. As far as we know, there has not been any systematic analysis of the distributions of N-linked and O-linked glycosylated residues in cellulases, mainly due to the limited annotations of the relevant functional domains and the glycosylated residues. We have computationally annotated the functional domains and glycosylated residues in cellulases, and conducted a systematic analysis of the distributions of the N-linked and O-linked glycosylated residues in these enzymes. Many N-linked glycosylated residues were known to be in the GH domains of cellulases, but they are there probably just by chance, since the GH domain usually occupies more than half of the sequence length of a cellulase. Our analysis indicates that the O-linked glycosylated residues are significantly enriched in the linker re- gions between the carbohydrate binding module （CBM） domains and GH domains of cellulases. Possible mechanisms are discussed.

关键词： glycosylation cellulase large-scale analyses

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Uncertainty abounds, what now?

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science 2025年第6740期387卷 1261页

作者： Dov Greenbaum Mark Gerstein The reviewer is at the Zvi Meitar Institute for Legal Implications of Emerging Technologies The reviewer is at the Harry Radzyner Law School The reviewer is at the Dina Recanati School of Medicine Reichman University Herzliya Israel The reviewer is at the Department of Biomedical Informatics and Data Science The reviewer is at the Program in Computational Biology and Bioinformatics The reviewer is at the Department of Computer Science Yale University New Haven CT USA.

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Algorithms in bioinformatics 1

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丛书名： Lecture Notes in computer science

1000年

作者： Steven L. Salzberg Tandy Warnow

ISBN: (数字)9783642042416

ISBN: (纸本)9783642042409

These proceedings contain papers from the 2009 Workshop on Algorithms in bioinformatics (WABI), held at the University of Pennsylvania in Philadelphia, Pennsylvania during September 12–13, 2009. WABI 2009 was the ninth annual conference in this series, which focuses on novel algorithms that address imp- tantproblemsingenomics,molecularbiology,***- phasizes research that describes computationally e?cient algorithms and data structures that have been implemented and tested in simulations and on real data. WABI is sponsored by the European Association for Theoretical C- puter science (EATCS) and the International Society for computational Bi- ogy (ISCB). WABI 2009 was supported by the Penn Genome Frontiers Institute and the Penn Center for bioinformatics at the University of Pennsylvania. For the 2009 conference, 90 full papers were submitted for review by the Program Committee, and from this strong ?eld of submissions, 34 papers were chosen for presentation at the conference and publication in the proceedings. The ?nal programcovered a wide range of topics including gene interaction n- works, molecular phylogeny, RNA and protein structure, and genome evolution.

关键词： Biochemistry, general Programming Techniques Algorithm Analysis and Problem Complexity computational biology/bioinformatics Computation by Abstract Devices Data Mining and Knowledge Discovery

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Population dynamics inside cancer biomass driven by repeated hypoxia-reoxygenation cycles

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Frontiers of Electrical and Electronic Engineering in China 2014年第3期9卷 85-99页

作者： Chi Zhang Sha Cao Ying Xu Computational Systems Biology Lab Department of Biochemistry and Molecular Biology Institute of Bioinformatics Universityof Georgia Athens GA 30602 USA Collegeof Computer Science and Technology Jilin University Changchun 130012 China

A computational analysis of genome-scale transcriptomic data collected on -1,700 tissue samples of three cancer types： breast carcinoma, colon adenocarcinoma and lung adenocarcinoma, revealed that each tissue consists of （at least） two major subpopulations of cancer cells with different capabilities to handle fluctuating Oz levels. The two populations have distinct genomic and transcriptomic characteristics, one accelerating its proliferation under hypoxic conditions and the other proliferating faster with higher O2 levels, referred to as the hypoxia and the reoxygenation subpopulations, respectively. The proportions of the two subpopulations within a cancer tissue change as the average 02 level changes. They both contribute to cancer development but in a complementary manner. The hypoxia subpopulation tends to have higher proliferation rates than the reoxygenation one as well as higher apoptosis rates; and it is largely responsible for the acidic environment that enables tissue invasion and provides protection against attacks from T-cells. In comparison, the reoxygenation subpopulation generates new extracellular matrices in support of further growth of the tumor and strengthens cell-cell adhesion to provide scaffolds to keep all the cells connected. This subpopulation also serves as the major source of growth factors for tissue growth. These data and observations strongly suggest that these two major subpopulations within each tumor work together in a conjugative relationship to allow the tumor to overcome stresses associated with the constantly changing Oz level due to repeated growth and angiogenesis. The analysis results not only reveal new insights about the population dynamics within a tumor but also have implications to our understanding of possible causes of different cancer phenotypes such as diffused versus more tightly connected tumor tissues.

关键词： cancer population dynamics intratumor heterogeneity cancer cell subpopulations hypoxia reoxygenation cancer evolution

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Cluster editing

Cluster editing

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9th Conference on Computability in Europe - The Nature of Computation: Logic, Algorithms, Applications, CiE 2013

作者： Böcker, Sebastian Baumbach, Jan Department of Bioinformatics Friedrich-Schiller-University Jena Germany Computational Biology Research Group Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark

ISBN: (纸本)9783642390524

The Cluster Editing problem asks to transform a graph into a disjoint union of cliques using a minimum number of edge modifications. Although the problem has been proven NP-complete several times, it has nevertheless attracted much research both from the theoretical and the applied side. The problem has been the inspiration for numerous algorithms in bioinformatics, aiming at clustering entities such as genes, proteins, phenotypes, or patients. In this paper, we review exact and heuristic methods that have been proposed for the Cluster Editing problem, and also applications of these algorithms for biological problems. © 2013 Springer-Verlag.

关键词： Heuristic methods

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KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection

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science (New York, N.Y.) 2025年第6735期387卷 eadn2337页

作者： Fagerberg, Eric Attanasio, John Dien, Christine Singh, Jaiveer Kessler, Emily A. Abdullah, Leena Shen, Jian Hunt, Brian G. Connolly, Kelli A. De Brouwer, Edward He, Jiaming Iyer, Nivedita R. Buck, Jessica Borr, Emily R. Damo, Martina Foster, Gena G. Giles, Josephine R. Huang, Yina H. Tsang, John S. Krishnaswamy, Smita Cui, Weiguo Joshi, Nikhil S. Department of Immunobiology Yale University School of Medicine New Haven CT United States Program in Computational Biology & Bioinformatics Yale University New Haven CT United States Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth NH United States Department of Pathology Feinberg School of Medicine at Northwestern University Chicago IL United States Department of Genetics and Computer Science Yale University School of Medicine New Haven CT United States Department of Medicine University of Chicago Chicago IL United States Section of Hematology Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Philadelphia PA United States Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Parker Institute for Cancer Immunotherapy Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Yale Center for Systems and Engineering Immunology Yale University School of Medicine New Haven CT United States New Haven CT United States Department of Biomedical Engineering Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University CT USA New Haven United States Applied Math Program Yale University New Haven CT United States

Naïve CD8 T cells have the potential to differentiate into a spectrum of functional states during an immune response. How these developmental decisions are made and what mechanisms exist to suppress differentiation toward alternative fates remains unclear. We employed in vivo CRISPR-Cas9-based perturbation sequencing to assess the role of ~40 transcription factors (TFs) and epigenetic modulators in T cell fate decisions. Unexpectedly, we found that knockout of the TF Klf2 resulted in aberrant differentiation to exhausted-like CD8 T cells during acute infection. KLF2 was required to suppress the exhaustion-promoting TF TOX and to enable the TF TBET to drive effector differentiation. KLF2 was also necessary to maintain a polyfunctional tumor-specific progenitor state. Thus, KLF2 provides effector CD8 T cell lineage fidelity and suppresses the exhaustion program.

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First passage time and first passage percolation models for analysing network resilience and effective strategies in strategic information warfare research: A brief survey and perspective

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International Journal of Information and computer Security 2013年第4期5卷 334-358页

作者： Ma, Zhanshan Computational Biology and Medical Ecology Lab. Kunming Institute of Zoology Chinese Academy of Sciences Kunming 650223 China Department of Computer Science Institute of Bioinformatics and Evolutionary Studies University of Idaho Moscow ID 83843 United States

We argue for the applications of first passage time (FPT) and first passage percolation (FPP) problems to the modelling and analysis of network resilience as well as effective strategies in strategic information warfare (SIW). Several intuitive arguments can be readily made to justify our attempt. First, winning a confrontation can be abstracted as an FPT or FPP problem. Secondly, information warfare is very similar to the directed percolation. Thirdly, three essential metrics in the FPP theory, percolation critical probability (Pc), first passage percolation time, and shape set B(t) can be mapped to the probability to win (or lose) the war, network resilience, and effective strike range, respectively. Fourthly, non-linearity is prevalent in SIW;percolation theory, and complexity science in large, are particularly effective in tackling this critical issue. To the best of our knowledge, this should be the first review on applying FPT and FPP theories to the SIW research. Copyright © 2013 Inderscience Enterprises Ltd.

关键词： Solvents

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Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers

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Clinical Breast Cancer 2025年

作者： Lin, Neil Abbas-Aghababazadeh, Farnoosh Su, Jie Wu, Alison J. Lin, Cherie Shi, Wei Xu, Wei Haibe-Kains, Benjamin Liu, Fei-Fei Kwan, Jennifer Y.Y. Institute of Medical Science Temerty Faculty of Medicine University of Toronto Toronto Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Princess Margaret Bioinformatics and Computational Genomics Laboratory University Health Network Toronto Canada Biostatistics Division Princess Margaret Cancer Centre Toronto Canada Research Institute Princess Margaret Cancer Centre Toronto Canada Department of Computer Science University of Toronto Toronto Canada Ontario Institute for Cancer Research Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada Department of Radiation Oncology Temerty Faculty of Medicine University of Toronto Toronto Canada Department of Medical Biophysics Temerty Faculty of Medicine University of Toronto Toronto Canada Radiation Medicine Program Princess Margaret Cancer Centre Toronto Canada

Background: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare costs. Early identification of patients at risk for severe RD can facilitate preventive management and reduce severe symptoms. This study evaluated the utility of subjective and objective factors, such as patient-reported outcomes (PROs) and serum cytokines, for predicting RD in breast cancer patients. The performance of machine learning (ML) and logistic regression-based models were compared. Patients and Methods: Data from 147 breast cancer patients who underwent radiotherapy was analyzed to develop prognostic models. ML algorithms, including neural networks, random forest, XGBoost, and logistic regression, were employed to predict clinically significant Grade 2+ RD. Clinical factors, PROs, and cytokine biomarkers were incorporated into the risk models. Model performance was evaluated using nested cross-validation with separate loops for hyperparameter tuning and calculating performance metrics. Results: Feature selection identified 18 predictors of Grade 2+ RD including smoking, radiotherapy boost, reduced motivation, and the cytokines interleukin-4, interleukin-17, interleukin-1RA, interferon-gamma, and stromal cell-derived factor-1a. Incorporating these predictors, the XGBoost model achieved the highest performance with an area under the curve (AUC) of 0.780 (95% CI: 0.701-0.854). This was not significantly improved over the logistic regression model, which demonstrated an AUC of 0.714 (95% CI: 0.629-0.798). Conclusion: Clinical risk factors, PROs, and serum cytokine levels provide complementary prognostic information for predicting severe RD in breast cancer patients undergoing radiotherapy. ML and logistic regression models demonstrated comparable performance for predicting clinically significant RD with AUC>0.70. ©

关键词： Cancer survivorship Oncology Predictive modeling Radiotherapy Skin toxicity

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DNA-guided assembly of nanocellulose meshes 7th

DNA-guided assembly of nanocellulose meshes

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7th International Conference on the Theory and Practice of Natural Computing, TPNC 2018

作者： Amărioarei, Alexandru Barad, Gefry Czeizler, Eugen Dobre, Ana-Maria Iţcuş, Corina Mitrana, Victor Păun, Andrei Păun, Mihaela Spencer, Frankie Trandafir, Romică Tuşa, Iris Department of Bioinformatics National Institute of Research and Development for Biological Sciences Bucharest Romania Computational Biomodeling Laboratory Turku Centre for Computer Science and Department of Computer Science Åbo Akademi University Turku Finland

ISBN: (纸本)9783030040697

Nanoengineered materials are a product of joint collaboration of theoreticians and experimentalists, of physicists, (bio-)chemists, and recently, of computer scientists. In the field of Nanotechnology and Nanoengineering, DNA (algorithmic) self-assembly has an acknowledged leading position. As a fabric, DNA is a rather inferior material;as a medium for shape, pattern, and dynamic behavior reconstruction, it is one of the most versatile nanomaterials. This is why the prospect of combining the physical properties of known high performance nanomaterials, such as cellulose, graphene, or fibroin, with the assembly functionality of DNA scaffolds is a very promising prospect. In this work we analyze the dynamical and structural properties of a would-be DNA-guided assembly of nanocellulose meshes. The aim is to generate pre-experimental insights on possible ways of manipulating structural properties of such meshes. The mechanistic principles of these systems, implemented through the DNA assembly apparatus, ensure the formation of 2D nanocellulose mesh structures. A key desired feature for such an engineered synthetic material, e.g. with applications in bio-medicine and nano-engineering, would be to control the size of the openings (gaps) within these meshes, a.k.a. its aperture. However, in the case of this composite material, this is not a direct engineered feature. Rather, we assert it could be indirectly achieved through varying several key parameters of the system. We consider here several experimentally tunable parameters, such as the ratio between nanocellulose fibrils and the DNA guiding elements, i.e., aptamer-functionalized DNA origamis, as well as the assumed length of the nanocellulose fibrils. To this aim, we propose a computational model of the mesh-assembly dynamical system, which we subject to numerical parameter scan and analysis. © 2018, Springer Nature Switzerland AG.

关键词： DNA

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