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International conference on Distributed Computing Systems Workshop

作者： Marco Aldinucci Maurizio Drocco Guilherme Peretti Pezzi Claudia Misale Fabio Tordini Massimo Torquati Computer Science Department University of Torino Italy Computer Science Department University of Pisa Italy

The stochastic modelling of biological systems, coupled with Monte Carlo simulation of models, is an increasingly popular technique in bioinformatics. The simulation-analysis workflow may result into a computationally expensive task reducing the interactivity required in the model tuning. In this work, we advocate high-level software design as a vehicle for building efficient and portable parallel simulators for a variety of platforms, ranging from multi-core platforms to GPGPUs to cloud. In particular, the Calculus of Wrapped Compartments (CWC) parallel simulator for systems biology equipped with on-line mining of results, which is designed according to the Fast Flow pattern-based approach, is discussed as a running example. In this work, the CWC simulator is used as a paradigmatic example of a complex C++ application where the quality of results is correlated with both computation and I/O bounds, and where high-quality results might turn into big data. The Fast Flow parallel programming framework, which advocates C++ pattern-based parallel programming makes it possible to develop portable parallel code without relinquish neither run-time efficiency nor performance tuning opportunities. Performance and effectiveness of the approach are validated on a variety of platforms, inter-alia cache-coherent multi-cores, cluster of multi-core (Ethernet and Infiniband) and the Amazon Elastic Compute Cloud.

关键词： Analytical models computational modeling Biological system modeling Pipelines Trajectory Multicore processing Graphics processing units

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Classification on Compressed DNA Sequences: Correlation-Based Feature Extraction Using Self-Supervised Learning

Classification on Compressed DNA Sequences: Correlation-Base...

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ieee EMBS conference on Biomedical Engineering and Sciences (IECBES)

作者： Sheena K. S. Madhu S. Nair Department of Computer Science Artificial Intelligence & Computer Vision Lab Cochin University of Science and Technology Kochi Kerala India

ISBN: (数字)9798350383409

ISBN: (纸本)9798350383416

Next-generation sequencing is producing data at an exponential rate, which creates immense computation hurdles for reliably and efficiently storing, transmitting, and analysing the data. Compression plays a vital role in managing the challenges associated with the growing volume of genomic data. Direct analysis over the compressed representations of DNA sequences can facilitate scalability, enabling the analysis of larger datasets within reasonable computational resources. The proposed scheme develops a methodology to extract meaningful features from compressed DNA sequences that maintain a high correlation with features from the original sequences. We employed deep learning techniques to maximize feature correlation between original and compressed sequence representations. This approach ensures that the features derived from compressed sequences are as informative and representative as those from the original sequences. We evaluated the proposed method with various state-of-the-art approaches for genomic sequence analysis using a dataset that includes genome sequences of different viruses. According to the results, the features extracted from compressed sequences are discriminative for the classification task; thus, downstream analysis of genomic sequences is possible without decompression.

关键词： Sequential analysis Correlation Sequences Computer viruses Scalability Genomics Self-supervised learning Feature extraction Reliability bioinformatics

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Probing glioblastoma and its microenvironment using single-nucleus and single-cell sequencing

Probing glioblastoma and its microenvironment using single-n...

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ieee International conference on bioinformatics and Biomedicine

作者： Sen Peng Sanhita Rath Connor Vuong Saumya Bollam Jenny Eschbacher Xishuang Dong Shwetal Mehta Nader Sanai Michael Berens Seungchan Kim Harshil Dhruv Cancer and Cell Biology Division The Translational Genomics Research Institute Division of Neurobiology Barrow Brain Tumor Research Center Barrow Neurological Institute St. Joseph's Hospital and Medical Center Center for Computational Systems Biology and Electrical and Computer Engineering Roy G. Perry College of Engineering Prairie View A&M University

ISBN: (纸本)9781728118680

Single-cell (scSeq) and single-nucleus sequencing (snSeq) are powerful tools to investigate cancer genomics at single cell resolution. Multiple studies have recently illuminated intratumoral heterogeneity in glioblastoma, however, the majority focused on molecular complexity of tumor cells, without considering unexplored host cell types that contribute to the microenvironment around tumor. To address the glioblastoma microenvironment composition and potential tumor-host interactions, we performed deep coverage sequencing of freshly resected primary GBM patient tissue without implementing any tumor enrichment strategies. The sequencing resulted in 902 cells and 1186 nuclei, respectively, passing quality control and with low mitochondrial gene percentage. We customized reference transcriptome by listing gene transcript loci as exons to take into account immature RNA, which greatly improved the alignment rate for single-nucleus data. We applied Cell Ranger pipelines (Version 3.0.2) and Seurat package (Version 2.3.1) and discovered 10 clusters in both scSeq and snSeq. Pathway analysis of each cluster signature in scSeq data along with known GBM microenvironment cell signatures revealed glioma tumor population along with surrounding microglia/macrophages, astrocytes, pericytes, oligodendrocytes, T cells and endothelial cells. The analysis of snSeq was able to capture the majority of cell types from patient tissues (tumor and microenvironment cells), but interestingly presented different cell type composition in microenvironment cell types such as microglia/macrophages. Integrating single-cell and single-nucleus transcriptomic data using canonical correlation analysis facilitated a comparison of snSeq and scSeq, contrasting depiction for certain cell types (e.g. NKX6-2 gene in Oligodendrocytes). Differential analysis of pathways between tumor and microenvironment cells unveiled potentially rewired pathways such as double strand break repair pathway. Our results demonstrat

关键词： glioblastoma microenvironment single cell sequencing single nucleus sequencing Microenvironments cell type Lipoblastoma SINGLE CELL Cell Microenvironment Tumours Analyses, Sequence

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Compressive genomics for protein databases

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bioinformatics 2013年第13期29卷 283-290页

作者： Daniels, Noah M. Gallant, Andrew Peng, Jian Cowen, Lenore J. Baym, Michael Berger, Bonnie Tufts Univ Dept Comp Sci Medford MA 02451 USA MIT Dept Math Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Harvard Univ Sch Med Dept Syst Biol Boston MA 02115 USA

Motivation: The exponential growth of protein sequence databases has increasingly made the fundamental question of searching for homologs a computational bottleneck. The amount of unique data, however, is not growing nearly as fast;we can exploit this fact to greatly accelerate homology search. Acceleration of programs in the popular PSI/DELTA-BLAST family of tools will not only speed-up homology search directly but also the huge collection of other current programs that primarily interact with large protein databases via precisely these tools. Results: We introduce a suite of homology search tools, powered by compressively accelerated protein BLAST (CaBLASTP), which are significantly faster than and comparably accurate with all known state-of-the-art tools, including HHblits, DELTA-BLAST and PSI-BLAST. Further, our tools are implemented in a manner that allows direct substitution into existing analysis pipelines. The key idea is that we introduce a local similarity-based compression scheme that allows us to operate directly on the compressed data. Importantly, CaBLASTP's runtime scales almost linearly in the amount of unique data, as opposed to current BLASTP variants, which scale linearly in the size of the full protein database being searched. Our compressive algorithms will speed-up many tasks, such as protein structure prediction and orthology mapping, which rely heavily on homology search.

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Vessel segmentation in low contrast X-ray angiogram images

Vessel segmentation in low contrast X-ray angiogram images

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ieee International conference on Image Processing

作者： B. Felfelian H. R. Fazlali N. Karimi S. M. R. Soroushmehr S. Samavi B. Nallamothu K. Najarían Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Emergency Medicine University of Michigan Ann Arbor U.S.A. Departruent of Internal Medicine University of Michigan Ann Arbor U.S.A. Deptartment of Computational Medicine and Bioinformatics University of Michigan Ann Arbor U.S.A.

Coronary artery disease is one of the major causes of death throughout the world. An effective method for diagnosing this disease is X-ray angiography. The images are usually of poor quality and low contrast. This is due to non-uniform illumination, appearance of other body organs and artifacts, low SNR values, etc. Accurate segmentation of arteries is a challenging and important task. In this paper we first extract coronary arteries region of interest (ROI) using Hessian filter. Then, we combine these results with the flux flow measurements for accurate identification of vessel pixels. Post processing is performed to eliminate falsely identified vessel pixels. Finally, we segment the coronary arteries by selecting the largest connected component. Qualitative and quantitative evaluations of our method show high effectiveness of the proposed method. In terms of capturing major vessels our method is successful in 96% of cases.

关键词： Decision support systems Biomedical imaging Indexes Q measurement Computers biology Computer vision

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Roe: A computational-Efficient Anti-hallucination Fine-Tuning Technology for Large Language Model Inspired by Human Learning Process 16th

Roe: A Computational-Efficient Anti-hallucination Fine-Tunin...

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16th International conference on Brain Informatics, BI 2023

作者： Liu, Xueqing Sajda, Paul Columbia University New YorkNY10027 United States

ISBN: (纸本)9783031430749

Large language models (LLMs) have shown significant potential in natural language processing (NLP) tasks, but they are plagued by issues of hallucination, where generated text contains false or fabricated information. This paper introduces a novel method, called "Roe," to combat hallucination in LLMs by incorporating a fine-tuning technique that promotes honesty in the model’s responses. Inspired by psychological studies on promoting honesty in children, the method rewards both correct answers and the behavior of admitting uncertainty by answering "I don’t know" when the model is unsure. The objective function is designed to encourage the model to switch between these two types of responses based on its confidence level. Experiments demonstrate that the Roe method achieves significantly better performance in addressing hallucination compared to the base Llama model and the original Alpaca model. The method also achieves comparable results to the state-of-the-art GPT4All model while using a smaller training dataset. The results of the HALTT4LLM benchmark further confirm the effectiveness of the Roe method in combating hallucination, particularly in handling fake questions. Sample outputs of the Roe model compared to the Alpaca model highlight the model’s adherence to factual information and its avoidance of direct references to fictional items in fabricated questions. Overall, the Roe method presents a promising approach for mitigating hallucination in large language models, enhancing their reliability and trustworthiness in generating text. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Optimization of Microgrid System for Community Electrification by using HOMER Pro

Optimization of Microgrid System for Community Electrificati...

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ieee Region 10 Humanitarian Technology conference (R10-HTC)

作者： Md.Kamrul Islam Jannatul Mawa Akanto Mohammad Zeyad S.M. Masum Ahmed Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh Chattogram Bangladesh Erasmus+ Joint MSC in Smart Cities and Communities University of the Basque Country (UPV/EHU) Bilbao Spain Erasmus+ Joint MSC in Smart Cities and Communities Faculty of Engineering University of Mons (UMONS) Mons Belgium

A microgrid included multiple energy sources such as a diesel generator, photovoltaic system (PV), wind turbines (WTs), and so on. The key reason for the growth of hybrid energy technology or microgrid is the reliability, abundance of renewable energy (RE) and it can use the resources in an efficient manner. This research aims to incorporate a stand-alone community microgrid in Cumilla, Bangladesh to deliver electricity to a residential community and determine the optimum solution to satisfy total demand. The load profile is considered in this study for a residential community situated in Cumilla. Moreover, three case studies are taken into account to analyze the optimum result. The simulation analysis is executed in the optimization software that is the Hybrid Optimization Model for Multiple Energy Resources (HOMER pro). Several types of analysis are conducted such as NPV, COE, emission, energy production, and operating cost. The simulation results reveal that case 3 (solar, wind and diesel generator) is the optimum solution as it has the lowest NPV, COE, and operating cost.

关键词： Analytical models Costs Simulation Wind speed Microgrids Generators Software

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PREMIER Turbo: Probabilistic error-correction using Markov inference in errored reads using the turbo principle

PREMIER Turbo: Probabilistic error-correction using Markov i...

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ieee Global conference on Signal and Information Processing (GlobalSIP)

作者： Xin Yin Zhao Song Karin Dorman Aditya Ramamoorthy Dept. of Statistics Iowa State University Ames IA Iowa State University Ames IA US Dept. of Genetics Development & Cell Biology Iowa State University Ames IA Dept. of Electrical & Computer Eng. Iowa State University Ames IA

We present a probabilistic algorithm for error correction for high throughput DNA sequencing data. Our approach leverages our prior algorithm PREMIER where sequencer outputs are modeled as independent realizations of a Hidden Markov Model (HMM) and the problem of error correction is posed as one of maximum likelihood sequence detection over this HMM. In this work we propose an algorithm called PREMIER Turbo which can be viewed as an iterative application of the PREMIER approach. Specifically, we apply error correction in both the forward and the backward directions in a given read. We also present a heuristic inspired by turbo-equalization that incorporates the prior belief on a nucleotide position returned by the Baum-Welch algorithm into the error correction steps. Our approach significantly improves the correction of nucleotides in the beginning of the read. Our test results on the real C. elegans and E. coli datasets show that PREMIER Turbo achieves a significantly better error correction performance than the other competing methods.

关键词： Hidden Markov models Viterbi algorithm Error correction Genomics bioinformatics computational modeling Benchmark testing

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Prediction of Protein-Protein Interactions Using Subcellular and Functional Localizations

Prediction of Protein-Protein Interactions Using Subcellular...

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2010 International conference on Life System Modeling and Simulation & 2010 International conference on Intelligent Computing for Sustainable Energy and Environment

作者： Yanliang Cai, Jiangsheng Yu , and Hanpin Wang Keylaboratory of High Confidence Software Technologies, Ministry of Education School of Electronic Engineering and Computer Science, Peking University, China

Protein-protein interaction (PPI) plays an important role in the living organisms, and a major goal of proteomics is to determine the PPI networks for the whole organisms. So both experimental and computational approaches to predict PPIs are urgently needed in the field of proteomics. In this paper, four distinct protein encoding methods are proposed, based on the biological significance extracted from the categories of protein subcellular and functional localizations. And then, some classifiers are tested to prediction PPIs. To show the robustness of classification and ensure the reliability of results, each classifier is examined by many independent random experiments of 10-fold cross validations. The model of random forest achieves some promising performance of PPIs.

关键词： Prediction of Protein-Protein Interactions Using Subcellular and Functional Localizations

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PepPFN: protein-peptide binding residues prediction via pre-trained module-based Fourier Network

PepPFN: protein-peptide binding residues prediction via pre-...

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Artificial intelligence (CAI), ieee conference on

作者： Xue Li Ben Cao Hongzhen Ding Na Kang Tao Song College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Centre for Computational Biology Duke-NUS Medical School Singapore School of Computer Science and Technology Dalian University of Technology Dalian China CFAR A*STAR Singapore The Ninth Department of Health Care Administration The Second Medical Center Chinese PLA General Hospital Beijing China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Molecular activities in vivo, such as protein-peptide binding residues, contribute to understanding disease mechanisms and discovering targeted drugs, but are often limited by the cost of wet experiments. Therefore, this paper proposes a Pre-trained module-based Fourier Network inspired by Fourier Neural Operators for predicting protein-peptide binding residues (PepPFN). First, the model captures highlatent protein representation and implicit information through Bert and ESM, respectively. Subsequently, the contrastive learning module is used to optimize the representation of binding residues to mitigate the effects of data diversity. Furthermore, the order and frequency information of the residual is obtained by strengthening the model with Fourier network. Comprehensive analysis of multiple tasks and protein visualization results demonstrates that PepPFN outperforms state-of-the-art methods on multiple performance metrics. Datasets and the source code is available at https://***/xueleecs/***.

关键词： Proteins Visualization Biological system modeling Source coding Contrastive learning Feature extraction Data models

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