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Hybrid support vector regression in electric load during national holiday season

Hybrid support vector regression in electric load during nat...

2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017

作者： Caraka, Rezzy Eko Bakar, Sakhinah Abu Pardamean, Bens Budiarto, Arif School of Mathematical Sciences Faculty of Science and Technology National University of Malaysia Malaysia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538640456

This paper studies non-parametric time-series approach to electric load in national holiday seasons based on historical hourly data in state electric company of Indonesia consisting of historical data of the Northern Sumatera also South and Central Sumatra electricity load. Given a baseline for forecasting performance, we apply our hybrid models and computation platform with combining parameter of the kernel. To facilitate comparison to results of our analysis, we highlighted the results around MAPE-based and R2-based techniques. In order to get more accurate results, we need to improve, investigate, also develop the appropriate statistical tools. Electric load forecasting is a fundamental aspect of infrastructure development decisions and can reduce the energy usage of the nation. © 2017 IEEE.

关键词： Time series

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Learning to predict expression efficacy of vectors in recombinant protein production

Learning to predict expression efficacy of vectors in recomb...

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8th Asia-Pacific bioinformatics Conference, APBC 2010

作者： Chan, Wen-Ching Liang, Po-Huang Shih, Yan-Ping Yang, Ueng-Cheng Lin, Wen-Chang Hsu, Chun-Nan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biological Chemistry Academia Sinica Taipei Taiwan Institute of Biomedical Sciences Academia Sinica Taipei Taiwan

Background: Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in Escherichia coli (E. coli). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression. Results: In this study, we applied machine learning to train prediction models to predict whether a pairing of vector-protein will express or not express in E. coli. For expressed cases, the models further predict whether the expressed proteins would be soluble. We collected a set of real cases from the clients of our recombinant protein production core facility, where six different vectors were designed and studied. This set of cases is used in both training and evaluation of our models. We evaluate three different models based on the support vector machines (SVM) and their ensembles. Unlike many previous works, these models consider the sequence of the target protein as well as the sequence of the whole fusion vector as the features. We show that a model that classifies a case into one of the three classes (no expression, inclusion body and soluble) outperforms a model that considers the nested structure of the three classes, while a model that can take advantage of the hierarchical structure of the three classes performs slight worse but comparably to the best model. Meanwhile, compared to previous works, we show that the prediction accuracy of our best method still performs the best. Lastly, we briefly present two methods to use the trained model in the design of the recombinant protein production

关键词： Vectors

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Exploring the mutation tendency of a specific region in the virus taxonomic groups using data mining techniques

Exploring the mutation tendency of a specific region in the ...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Je, Mikyung Cho, Myeongji Kim, Yoon Jung Son, Hyeon S. Seoul National University Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul Korea Republic of Honam National Institute of Biological Resources Division of Bio It Mokpo Korea Republic of Seoul National University Lab. of Computational Virology and Viroinformatics Graduate School of Public Health Seoul Korea Republic of Seoul National University Institute of Health and Environment Seoul Korea Republic of

ISBN: (纸本)9781665480451

The GDNQ motif is mainly found in nonsegmented (-)ssRNA viruses and plays a role in the catalytic and polymerase activity pathways. However, in some recently reported dsRNA mycoviruses, the GDNQ motif of the (-)ssRNA virus group is found instead of the GDD motif commonly found in the dsRNA virus group. Therefore, in this study, data mining techniques were used to explore the mutation tendency of a specific region in the virus taxonomic groups. Viral protein sequences were obtained from the NCBI Virus data repository, and the performance of several known string-searching algorithms (BF, KMP, and BM) was evaluated to establish an optimal strategy for extracting useful information from sequence big data. Then, the pattern-matching performance of the algorithm was tested to detect whether the conserved region sequence was a common motif type or a mutated motif type. As a result of the analysis, it was confirmed that the BF method produced the fastest results. However, the BM method recorded higher accuracy. Through the virus mutation tendency derived from the analysis results, it was confirmed that 7.1% of dsRNA viruses had a mutated motif type based on the total data analyzed and that it was relatively recent that ssRNA virus motif variants began to be discovered in dsRNA viruses. It is expected that additional in-depth analysis of the host range and infection route of viruses with the mutation will help understand the evolution of dsRNA and ssRNA mycoviruses. © 2022 IEEE.

关键词： Big data

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Parsimonious reconstruction of network evolution

Parsimonious reconstruction of network evolution

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Lecture Notes in Computer Science

作者： Patro, Rob Sefer, Emre Malin, Justin Marçais, Guillaume Navlakha, Saket Kingsford, Carl Center for Bioinformatics and Computational Biology University of Maryland College Park MD 20742 United States Department of Computer Science University of Maryland College Park MD 20742 United States Computational Biology Bioinformatics and Genomics Concentration Biological Sciences Graduate Program University of Maryland College Park MD 20742 United States Program in Applied Mathematics Statistics and Scientific Computation University of Maryland College Park MD 20742 United States School of Computer Science Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9783642230370

We consider the problem of reconstructing a maximally parsimonious history of network evolution under models that support gene duplication and loss and independent interaction gain and loss. We introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories suggest that common ancestral networks can be accurately reconstructed using this parsimony approach. © 2011 Springer-Verlag.

关键词： Genes

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A Drug-Induced Liver Injury Prediction Model using Transcriptional Response Data with Graph Neural Network

A Drug-Induced Liver Injury Prediction Model using Transcrip...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Doyeong Hwang Minji Jeon Jaewoo Kang Department of Computer Science and Engineering Korea University Seoul Republic of Korea Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Republic of Korea

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Drug-Induced Liver Injury (DILI) is a major cause of failed drug candidates in clinical trials and withdrawal of approved drugs from the market. Therefore, machine learning-based DILI prediction can be key in increasing the success rate of drug discovery because drug candidates that are predicted to potentially induce liver injury can be rejected before clinical trials. However, existing DILI prediction models mainly focus on the chemical structures of drugs. Since we cannot determine whether a drug will cause liver injury based solely on its structure, DILI prediction based on the transcriptional effect of a drug on a cell is necessary. In this paper, we propose GLIT which is a model that uses transcriptional response data and chemical structures and can be used for drug-induced liver injury prediction. GLIT learns the embedding vectors of drug structures and drug-induced gene expression profiles using graph attention networks in a biological knowledge graph for predicting DILI. GLIT outperformed a baseline model that uses only drug structure information by 7% and 19.2% in terms of correct classification rate (CCR) and Matthews correlation coefficient (MCC), respectively. In addition, we conducted a literature survey to confirm whether the class labels of drugs, in the unknown DILI class, predicted by GLIT are correct.

关键词： Drugs Gene expression Neural networks Injuries Liver Predictive models Biological system modeling

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Cover Feature: Diversity Oriented Strategy (DOS) for the Efficient Synthesis of Benzofuro[2,3-b]pyridine Derivatives with Anticancer Activity (ChemMedChem 3/2025)

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ChemMedChem 2025年第3期20卷

作者： Reymark Ereje Jantana Yahuafai Theeranuch Jaroenchuensiri Patcharaporn Supakijjanusorn Sukanya Unson Borwornlak Toopradab Thanyada Rungrotmongkol Somsak Pianwanit Chanat Aonbangkhen Tanatorn Khotavivattana Center of Excellence in Natural Products Department of Chemistry Faculty of Science Chulalongkorn University Pathumwan Bangkok 10330 Thailand Department of Physical Sciences College of Science University of the Philippines Baguio Baguio City 2600 Philippines Clinical Research Section Division of Research and Academic Support National Cancer Institute Bangkok 10400 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Pathumwan Bangkok 10330 Thailand Center of Excellence in Computational Chemistry Department of Chemistry Faculty of Science Chulalongkorn University Pathumwan Bangkok 10330 Thailand

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A scalable method for detecting multiple loci associated with traits using TF-IDF weighting and association rule mining

A scalable method for detecting multiple loci associated wit...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Sunwon Lee Jaewoo Kang Junho Oh Department of Graduate Program in Bioinformatics Korea University Seoul South Korea Department of Computer Science and Engineering Korea University Seoul South Korea

The recent advance in SNP genotyping has made a significant contribution to reduction of the costs for large-scale genotyping. The development also has dramatically increased the size of the SNP genotype data. The increase of the volume of the data, however, has posed a huge obstacle to the conventional analysis techniques that are typically vulnerable to the high-dimensionality problem. To address the issue, we propose a method that exploits two well-tested models: the document-term model and the transaction analysis model. The proposed method consists of two phases. In the first phase, we reduce the dimensions of the SNP genotype data by extracting significant SNPs through transformation of the data in lieu of the document-term model. In the second phase, we discover the association rules that signify the relations between the SNPs and the traits, through the application of the transactional analysis in the reduced-dimension genotype data. We validated the discovered rules through the literature survey. Experiments were also carried out using the HGDP panel data provided by the Foundation Jean Dausset-CEPH, which prove the validity of our new method for identifying appropriate dimensional reduction and associations of multiple SNPs and traits.

关键词： Association rules Data models Analytical models Diseases Asia Context

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How Retroactivity Affects the Behavior of Incoherent Feed-Forward Loops

arXiv

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arXiv 2020年

作者： Wang, Junmin Belta, Calin Isaacson, Samuel A. Bioinformatics Graduate Program Boston University BostonMA United States Department of Mathematics and Statistics Boston University BostonMA United States

An incoherent feed-forward loop (IFFL) is a network motif known for its ability to accelerate responses and generate pulses. Though functions of IFFLs are well studied, most previous computational analysis of IFFLs used ordinary differential equation (ODE) models where retroactivity, the effect downstream binding sites exert on the dynamics of an upstream transcription factor (TF), was not considered. It remains an open question to understand the behavior of IFFLs in contexts with high levels of retroactivity, e.g., in cells transformed/transfected with high-copy plasmids, or in eukaryotic cells where a TF binds to numerous high-affinity binding sites in addition to one or more functional target sites. Here we study the behavior of IFFLs by simulating and comparing ODE models with different levels of retroactivity. We find that increasing retroactivity in an IFFL can increase, decrease, or keep the network’s response time and pulse amplitude constant. This suggests that increasing retroactivity, traditionally considered as an impediment to designing robust synthetic systems, could be exploited to improve the performance of IFFLs. We compare the behaviors of IFFLs to negative autoregulatory loops, another sign-sensitive response-accelerating network motif, and find that increasing retroactivity in a negative autoregulated circuit can only slow the response. The inability of a negative autoregulatory loop to flexibly handle retroactivity may have contributed to its lower abundance in eukaryotic relative to bacterial regulatory networks, a sharp contrast to the significant abundance of IFFLs in both cell types. Copyright © 2020, The Authors. All rights reserved.

关键词： Transcription

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Unsupervised Neural Tracing In Densely Labeled Multispectral Brainbow Images

Unsupervised Neural Tracing In Densely Labeled Multispectral...

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IEEE International Symposium on Biomedical Imaging

作者： Bin Duan Logan A Walker Douglas H Roossien Fred Y Shen Dawen Cai Yan Yan Illinois Institute of Technology Biophysics University of Michigan Bioinformatics Ball State University Neuroscience Graduate Program Cell and Developmental Biology Michigan Medicine

ISBN: (纸本)9781665412469;9781665429474

Recent advances in imaging technologies for generating large quantities of high-resolution 3D images, especially multispectral labeling technology such as Brainbow, permits unambiguous differentiation of neighboring neurons in a densely labeled brain. This enables, for the first time, the possibility of studying the connectivity between many neurons from a light microscopy image. The lack of reliable automated neuron morphology reconstruction, however, makes data analysis the bottleneck of extracting rich informatics in neuroscience. Supervoxel-based neuron segmentation methods have been proposed to solve this problem, however, previous approaches have been impeded by the large numbers of errors which arise in the final segmentation. In this paper, we present a novel unsupervised approach to trace neurons from multispectral Brainbow images, which prevents segmentation errors and tracing continuity errors using two innovations: First, we formulate a Gaussian mixture model-based clustering strategy to improve the separation of segmented color channels that provides accurate skeletons for the next steps. Then, a skeleton graph approach is proposed to allow the identification and correction of discontinuities in the neuron tree topology. We find that these innovations allow better performance over current state-of-the-art approaches, which results in more accurate neuron tracing results close to human expert annotation.

关键词： Image segmentation Technological innovation Three-dimensional displays Neuroscience Microscopy Neurons Morphology

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Determinants of translation speed are randomly distributed across transcripts resulting in a universal scaling of protein synthesis times

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Physical Review E 2018年第2期97卷 022409-022409页

作者： Ajeet K. Sharma Nabeel Ahmed Edward P. O’Brien Department of Chemistry Pennsylvania State University University Park Pennsylvania 16802 USA Bioinformatics and Genomics Graduate Program The Huck Institutes of the Life Sciences Pennsylvania State University University Park Pennsylvania 16802 USA

Ribosome profiling experiments have found greater than 100-fold variation in ribosome density along mRNA transcripts, indicating that individual codon elongation rates can vary to a similar degree. This wide range of elongation times, coupled with differences in codon usage between transcripts, suggests that the average codon translation-rate per gene can vary widely. Yet, ribosome run-off experiments have found that the average codon translation rate for different groups of transcripts in mouse stem cells is constant at 5.6 AA/s. How these seemingly contradictory results can be reconciled is the focus of this study. Here, we combine knowledge of the molecular factors shown to influence translation speed with genomic information from Escherichia coli, Saccharomyces cerevisiae and Homo sapiens to simulate the synthesis of cytosolic proteins in these organisms. The model recapitulates a near constant average translation rate, which we demonstrate arises because the molecular determinants of translation speed are distributed nearly randomly amongst most of the transcripts. Consequently, codon translation rates are also randomly distributed and fast-translating segments of a transcript are likely to be offset by equally probable slow-translating segments, resulting in similar average elongation rates for most transcripts. We also show that the codon usage bias does not significantly affect the near random distribution of codon translation rates because only about 10% of the total transcripts in an organism have high codon usage bias while the rest have little to no bias. Analysis of Ribo-Seq data and an in vivo fluorescent assay supports these conclusions.

关键词： Gene expression Bacteria Genes Proteins Markovian processes Monte Carlo methods

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