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Congress on Evolutionary Computation

作者： J. Schonfeld D.A. Ashlock Bioinformatics and Computational Biology Program Iowa State University Ames IA USA Mathematics Department Iowa State University Ames IA USA

Evolution and robustness are thought to be intimately connected. Are solutions to optimization problems produced by evolutionary algorithms more robust to mutation than those produced by other classes of search algorithms? We explore this question in a model system based on bivariate real functions. Bivariate real functions serve as a well understood model systems that are easy to visualize. Both the number and robustness of optimal solutions found in multiple trials with several typical optimization algorithms were compared. In the majority of the function landscapes explored the tournament selection evolutionary algorithm found optimal solutions which were significantly more robust to mutation than those discovered by the other algorithms.

关键词： Robustness Evolutionary computation Genetic mutations Evolution (biology) Biological systems Proteins Stochastic processes Systems engineering and theory Stochastic systems bioinformatics

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Yale at "Discharge Me!": Evaluating Constrained Generation of Discharge Summaries with Unstructured and Structured Information 23

Yale at "Discharge Me!": Evaluating Constrained Generation o...

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23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Socrates, Vimig Huang, Thomas Ai, Xuguang Fereydooni, Soraya Chen, Qingyu Taylor, R. Andrew Chartash, David Department of Biomedical Informatics and Data Science Yale University School of Medicine United States Department of Emergency Medicine Yale School of Medicine United States Program of Computational Biology and Bioinformatics Yale University United States School of Medicine University College Dublin National University of Ireland Dublin Ireland

ISBN: (纸本)9798891761308

In this work, we propose our top-ranking (2nd place) pipeline for the generation of discharge summary subsections as a part of the BioNLP 2024 Shared Task 2: "Discharge Me!". We evaluate both encoder-decoder and state-of-the-art decoder-only language models on the generation of two key sections of the discharge summary. To evaluate the ability of NLP methods to further alleviate the documentation burden on physicians, we also design a novel pipeline to generate the brief hospital course directly from structured information found in the EHR. Finally, we evaluate a constrained beam search approach to inject external knowledge about relevant patient problems into the text generation process. We find that a BioBART model fine-tuned on a larger fraction of the data without constrained beam search outperforms all other models.. ©2024 Association for computational Linguistics.

关键词： Decoding

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Improving protein-RNA interface prediction by combining sequence homology based method with a naive bayes classifier: Preliminary results 11

Improving protein-RNA interface prediction by combining sequ...

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2011 ACM Conference on bioinformatics, computational biology and Biomedicine, ACM-BCB 2011

作者： Xue, Li C. Walia, Rasna EL-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States Department of Computer Science Iowa State University Ames IA 50011 United States Department of Systems and Computer Engineering Al-Azhar University Cairo Egypt

ISBN: (纸本)9781450307963

Protein-RNA interactions play important roles in cellular processes like protein synthesis, RNA processing, and gene expression regulation. Reliable identification of the interfaces involved in RNA-protein interactions is essential for comprehending the mechanisms and the functional implications of these interactions and provides a valuable guide for rational drug discovery and design. Because the determination of 3D structures of protein-RNA complexes has various technical limitations and is typically costly, reliable in silico interface prediction methods that require only the sequence information are urgently needed. We present HomPRIP, a homologous sequence based method for predicting protein-RNA interfaces, based on our conservation analysis of protein-RNA interfaces. We test Hom-PRIP on a benchmark dataset of 199 proteins and compare it with the state-of-the-art protein-RNA interface prediction methods. Our results show that HomPRIP can reliably identify protein-RNA interface residues in 71% of test proteins with at least one putative sequence homolog passing the similarity thresholds of HomPRIP. Moreover, to facilitate predictions for proteins with no identified homologs, we develop HomPRIP-NB, a method combining the HomPRIP predictor and a Naive Bayes (NB) classifier trained using evolutionary information derived from alignments against the NCBI nr database. Our results suggest that HomPRIP-NB significantly outperforms the state-of-the-art machine learning methods for predicting protein-RNA interface residues. Copyright © 2011 ACM.

关键词： Proteins

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Identify Non-mutational p53 Functional Deficiency in Human Cancers

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Genomics, Proteomics & bioinformatics 2024年第5期22卷 85-98页

作者： Qianpeng Li Yang Zhang Sicheng Luo Zhang Zhang Ann L.Oberg David E.Kozono Hua Lu Jann N.Sarkaria Lina Ma Liguo Wang National Genomics Data Center China National Center for BioinformationBeijing 100101China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Division of Computational Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Radiation Oncology Dana-Farber Cancer Institute and Brigham and Women's HospitalBostonMA 02215USA Department of Biochemistry&Molecular Biology and Tulane Cancer Center Tulane University School of MedicineNew OrleansLA 70112USA Department of Radiation Oncology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Biochemistry and Molecular Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Bioinformatics and Computational Biology Graduate Program University of Minnesota RochesterRochesterMN 55904USA

An accurate assessment of p53's functional statuses is critical for cancer genomic ***,there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA *** undetected cases are often misclassified as p53-normal,leading to inaccurate prognosis and downstream association *** address this issue,we built the support vector machine(SVM)models to systematically reassess p53's functional statuses in TP53 wild-type(TP53^(WT))tumors from multiple The Cancer Genome Atlas(TCGA)***-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve(AUROC)of 0.9822,precision of 0.9747,and recall of *** study revealed that a significant proportion(87%-99%)of TP53^(WT) tumors actually had compromised p53 *** analyses uncovered that these genetically intact but functionally impaired(termed as predictively reduced function of p53 or TP53^(WT)-pRF)tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors:heightened genomic instability and elevated levels of ***,patients with TP53^(WT)-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53(TP53^(WT)-pN)tumors,and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

关键词： Cancer Composite expression DNA mutation Machine learning p53 deficiency

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Tracking SARS-CoV-2 Omicron diverse spike gene mutations identifies multiple inter-variant recombination events

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Signal Transduction and Targeted Therapy 2022年第5期7卷 1808-1816页

作者： Junxian Ou Wendong Lan Xiaowei Wu Tie Zhao Biyan Duan Peipei Yang Yi Ren Lulu Quan Wei Zhao Donald Seto James Chodosh Zhen Luo Jianguo Wu Qiwei Zhang Guangdong Provincial Key Laboratory of Virology Institute of Medical MicrobiologyJinan University510632 GuangzhouChina BSL-3 Laboratory(Guangdong) Guangdong Provincial Key Laboratory of Tropical Disease ResearchSchool of Public HealthSouthern Medical University510515 GuangzhouChina Bioinformatics and Computational Biology Program School of Systems BiologyGeorge Mason UniversityManassasVA 20110USA Department of Ophthalmology Howe Laboratory Massachusetts Eye and EarHarvard Medical SchoolBostonMA 02114USA Foshan Institute of Medical Microbiology 528315 FoshanChina

The current pandemic of COVID-19 is fueled by more infectious emergent Omicron *** concerns of emergent variants include possible recombinants,as genome recombination is an important evolutionary mechanism for the emergence and re-emergence of human viral *** this study,we identified diverse recombination events between two Omicron major subvariants(BA.1 and BA.2)and other variants of concern(VOCs)and variants of interest(VOIs),suggesting that co-infection and subsequent genome recombination play important roles in the ongoing evolution of *** scanning high-quality completed Omicron spike gene sequences.

关键词： subsequent diverse recombination

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Distributions and statistical power of optimal signal-detection methods in finite cases

arXiv

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arXiv 2017年

作者： Zhang, Hong Jin, Jiashun Wu, Zheyang Worcester Polytechnic Institute Carnegie Mellon University Department of Mathematical Sciences Program of Bioinformatics and Computational Biology Program of Data Science 100 Institute Road Worcester01609 Morocco

In big data analysis for detecting rare and weak signals among n features, some grouping-test methods such as Higher Criticism test (HC), Berk-Jones test (B-J), and φ-divergence test share the similar asymptotical optimality when n → ∞. However, in practical data analysis n is frequently small and moderately large at most. In order to properly apply these optimal tests and wisely choose them for practical studies, it is important to know how to get the p-values and statistical power of them. To address this problem in an even broader context, this paper provides analytical solutions for a general family of goodness-of-fit (GOF) tests, which covers these optimal tests. For any given i.i.d. and continuous distributions of the input test statistics of the n features, both p-value and statistical power of such a GOF test can be calculated. By calculation we compared the finite-sample performances of asymptotically optimal tests under the normal mixture alternative. Results show that HC is the best choice when signals are rare, while B-J is more robust over various signal patterns. In the application to a real genome-wide association study, results illustrate that the p-value calculation works well, and the optimal tests have potentials for detecting novel disease genes with weak genetic effects. The calculations have been implemented in an R package SetTest and published on the CRAN. Copyright © 2017, The Authors. All rights reserved.

关键词： Information analysis

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Minimization-Aware Recursive K∗ (MARK∗ ): A Novel, Provable Algorithm that Accelerates Ensemble-Based Protein Design and Provably Approximates the Energy Landscape 1

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23rd International Conference on Research in computational Molecular biology, RECOMB 2019

作者： Jou, Jonathan D. Holt, Graham T. Lowegard, Anna U. Donald, Bruce R. Department of Computer Science Duke University DurhamNC United States Computational Biology and Bioinformatics Program Duke University DurhamNC United States Department of Biochemistry Duke University Medical Center DurhamNC United States Department of Chemistry Duke University DurhamNC United States

ISBN: (数字)9783030170837

ISBN: (纸本)9783030170820

Protein design algorithms that model continuous sidechain flexibility and conformational ensembles better approximate the in vitro and in vivo behavior of proteins. The previous state of the art, iMinDEE- A∗ - K∗, computes provable Ε -approximations to partition functions of protein states (e.g., bound vs. unbound) by computing provable, admissible pairwise-minimized energy lower bounds on protein conformations and using the A∗ enumeration algorithm to return a gap-free list of lowest-energy conformations. iMinDEE-A ∗ - K∗ runs in time sublinear in the number of conformations, but can be trapped in loosely-bounded, low-energy conformational wells containing many conformations with highly similar energies. That is, iMinDEE- A∗ - K∗ is unable to exploit the correlation between protein conformation and energy: similar conformations often have similar energy. We introduce two new concepts that exploit this correlation: Minimization-Aware Enumeration and Recursive K∗. We combine these two insights into a novel algorithm, Minimization-Aware Recursive K∗ (MARK∗ ), that tightens bounds not on single conformations, but instead on distinct regions of the conformation space. We compare the performance of iMinDEE- A∗ - K∗ vs. MARK∗ by running the BBK∗ algorithm, which provably returns sequences in order of decreasing K∗ score, using either iMinDEE- A∗ - K∗ or MARK∗ to approximate partition functions. We show on 200 design problems that MARK∗ not only enumerates and minimizes vastly fewer conformations than the previous state of the art, but also runs upÂ to two orders of magnitude faster. Finally, we show that MARK∗ not only efficiently approximates the partition function, but also provably approximates the energy landscape. To our knowledge, MARK∗ is the first algorithm to do so. We use MARK∗ to analyze the change in energy landscape of the bound and unbound states of the HIV-1 capsid protein C-terminal domain in complex with camelid V H H, and measure the change in conformati

关键词： Proteins

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Hyperbolic procrustes analysis using riemannian geometry 21

Hyperbolic procrustes analysis using riemannian geometry

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Ya-Wei Eileen Lin Yuval Kluger Ronen Talmon Viterbi Faculty of Electrical and Computer Engineering Technion Program in Applied Mathematics Yale University and Interdepartmental Program in Computational Biology and Bioinformatics Yale University and Department of Pathology Yale University

ISBN: (纸本)9781713845393

Label-free alignment between datasets collected at different times, locations, or by different instruments is a fundamental scientific task. Hyperbolic spaces have recently provided a fruitful foundation for the development of informative representations of hierarchical data. Here, we take a purely geometric approach for label-free alignment of hierarchical datasets and introduce hyperbolic Procrustes analysis (HPA). HPA consists of new implementations of the three prototypical Procrustes analysis components: translation, scaling, and rotation, based on the Riemannian geometry of the Lorentz model of hyperbolic space. We analyze the proposed components, highlighting their useful properties for alignment. The efficacy of HPA, its theoretical properties, stability and computational efficiency are demonstrated in simulations. In addition, we showcase its performance on three batch correction tasks involving gene expression and mass cytometry data. Specifically, we demonstrate high-quality unsupervised batch effect removal from data acquired at different sites and with different technologies that outperforms recent methods for label-free alignment in hyperbolic spaces.

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Investigating the Efficacy of Data Mining Techniques for Crowdsourced Biological Datasets

Investigating the Efficacy of Data Mining Techniques for Cro...

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2019 IEEE MIT Undergraduate Research Technology Conference, URTC 2019

作者： Regan, Ilona Alkurdi, Dany Paisarnsrisomsuk, Sarun Ruiz, Carolina Ryder, Elizabeth F. Gegear, Rob Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA United States Princeton University Department of Near Eastern Studies PrincetonNJ United States Worcester Polytechnic Institute Department of Computer Science WorcesterMA United States Worcester Polytechnic Institute Department of Biology and Biotechnology WorcesterMA United States University of Massachusetts Dartmouth Department of Biology DartmouthMA United States

ISBN: (纸本)9781728158181

The rapid worldwide decline of wild pollinators over recent years poses a significant environmental threat due to the critical keystone role that pollinators play in terrestrial ecosystems. In order to gain insight into the major anthropogenic factors causing these declines, researchers have collected large amounts of ecological data. Yet, they often lack the computational tools needed to analyze the information contained in such datasets. We investigate various data analysis techniques for the Beecology Project, which is a citizen science based effort to rapidly collect data on foraging habits of bumblebee species native to Massachusetts. Different data mining approaches were explored, including association analysis, trend analysis, classification, regression, and clustering. It was found that different techniques were more suitable depending on the biological research question. Future work will focus on making tools utilizing these approaches available online through the Beecology website, where they can be used by the public to determine how best to protect our native pollinators and the diverse ecosystems they support. © 2019 IEEE.

关键词： Data mining

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Predicting Protein Interaction Network Perturbation by Alternative Splicing with Semi-Supervised Learning

SSRN

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SSRN 2020年

作者： Narykov, Oleksandr Johnson, Nathan Korkin, Dmitry Department of Computer Science Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA United States Harvard Program in Therapeutic Sciences Harvard Medical School Breast Tumor Immunology Laboratory Dana-Farber Cancer Institute BostonMA United States Department of Computer Science Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA United States

Alternative splicing introduces a new layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a cell, or disease phenotype. In particular, recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions, yet the extent to which the macromolecular interactions are affected by alternative splicing has to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here we develop an accurate feature-based machine learning approach that predicts if a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method called ALTernatively Spliced INteractions prediction (ALT-IN) Tool was compared with the state-of-the-art PPI prediction tools and showed superior performance, achieving 0.92 in precision and recall values. © 2020, The Authors. All rights reserved.

关键词： Forecasting

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