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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app;there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80. © 2023 The Authors. Published by Elsevier B.V.

关键词： Google Play Store Sentiment Analysis SVM TikTok VADER Sentiment

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eXpanda: An integrated platform for network analysis and visualization

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In Silico Biology 2007年第2期7卷 141-144页

作者： Negishi, Yoshiteru Nakamura, Hiroyuki Yachie, Nozomu Saito, Rintaro Tomita, Masaru Institute for Advanced Biosciences Keio University Fujisawa 252-8520 Japan Bioinformatics Program Graduate School of Media and Governance Keio University Fujisawa 252-8520 Japan Department of Environmental Information Keio University Fujisawa 252-8520 Japan

Analysis and visualization of biological networks, such as protein-protein and protein-DNA interactions, are crucially important toward obtaining a thorough understanding of living systems. Here, we present an integrative software platform, eXpanda, which enables an analysis of a very broad range of biological networks, with a special focus on the extraction of characteristic topologies which potentially function as units in the networks. eXpanda is provided as a Perl library which gives full-automatic connections to various biological databases via a Perl programmable interface and can perform topological analysis based on graph theory. The results of these analyses are visualizable by vector graphics. eXpanda is under GNU General Public License. Software package, detailed documentations, source codes, and some sample scripts are downloadable at http://***/expanda/. © 2007 IOS Press. All rights reserved.

关键词： Interactome Network analysis Network visualization Perl library Scalable Vector Graphics (SVG)

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Systematic Literature Review: An Intelligent Pulmonary TB Detection from Chest X-Rays 1

Systematic Literature Review: An Intelligent Pulmonary TB De...

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1st International Conference on Computer Science and Artificial Intelligence, ICCSAI 2021

作者： Jimmy Cenggoro, Tjeng Wawan Pardamean, Bens Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Tuberculosis (TB) is one of the top ten reasons for death from an infectious agent. Although TB is curable and preventable, delay in diagnosis and treatment can lead the patient to death. Advancements in computer-Aided diagnosis (CAD), particularly in medical images classification, significantly contribute to early TB detection. The current state-of-Art CAD for medical images classification applications using a method base on deep learning techniques. The problem faced in this deep learning technique is that, in general, it only uses a single modal for the model. In contrast, in medical practice, the data used for TB analysis not only focuses on images but also includes clinical data such as demographics, patient assessments, and lab test results. This systematic literature review describes different deep learning methods using single modal or multimodal techniques that combined images with other clinical data. We conducted a systematic search on Springer, PubMed, ResearchGate, and google scholar for original research leveraging deep learning for Pulmonary TB detection. © 2021 IEEE.

关键词： Computer aided diagnosis

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Doubly-stochastic normalization of the Gaussian kernel is robust to heteroskedastic noise

arXiv

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arXiv 2020年

作者： Landa, Boris Coifman, Ronald R. Kluger, Yuval Program in Applied Mathematics Yale University Interdepartmental Program in Computational Biology and Bioinformatics Yale University Department of Pathology Yale University School of Medicine

A fundamental step in many data-analysis techniques is the construction of an affinity matrix describing similarities between data points. When the data points reside in Euclidean space, a widespread approach is to from an affinity matrix by the Gaussian kernel with pairwise distances, and to follow with a certain normalization (e.g. the row-stochastic normalization or its symmetric variant). We demonstrate that the doubly-stochastic normalization of the Gaussian kernel with zero main diagonal (i.e. no self loops) is robust to heteroskedastic noise. That is, the doubly-stochastic normalization is advantageous in that it automatically accounts for observations with different noise variances. Specifically, we prove that in a suitable high-dimensional setting where heteroskedastic noise does not concentrate too much in any particular direction in space, the resulting (doubly-stochastic) noisy affinity matrix converges to its clean counterpart with rate m−1/2, where m is the ambient dimension. We demonstrate this result numerically, and show that in contrast, the popular row-stochastic and symmetric normalizations behave unfavourably under heteroskedastic noise. Furthermore, we provide a prototypical example of simulated single-cell RNA sequence data with strong intrinsic heteroskedasticity, where the advantage of the doubly-stochastic normalization for exploratory analysis is evident. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Meeting report on RECOMB 2013 （the 17th Annual International Conference on Research in Computational Molecular Biology）

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Frontiers of Electrical and Electronic Engineering in China 2013年第2期8卷 175-181页

作者： Xuegong Zhang Fengzhu Sun MOE Key Laboratory of Bioinformatics and Bioinformatics Division/Center for Synthetic and Systems Biology TNLIST and Department of Automation Tsinghua University Beijing 100084 China Molecular and Computational Biology Program University of Southern California Los Angeles CA 90089 USA

RECOMB 2013 was successfully held in Tsinghua University, Beijing, China on April 7-10, 2013, hosted by the bioinformatics Division and Center for Synthetic and Systems Biology, Tsinghua National Laboratory for Information Science and Technology （TNLIST）. A total of about 500 professionals from both academia and industry from 29 countries and regions attended the conference and its RECOMB-Seq satellite workshop after the main conference.

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Evolving hypernetwork classifiers for microRNA expression profile analysis

Evolving hypernetwork classifiers for microRNA expression pr...

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Congress on Evolutionary Computation

作者： Sun Kim Soo-Jin Kim Byoung-Tak Zhang The Center for Bioinformatics Technology Graduate Program in Bioinformatics Seoul National University Seoul South Korea The School of Computer Science and Engineering Seoul National University Seoul South Korea

High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms. While computational analysis for the microarrays show good performance, it is still difficult to infer modules of multiple co-regulated genes. Here, we present a novel classification method to identify the gene modules associated with cancers from microarray data. The proposed approach is based on 'hypernetworks', a hypergraph model consisting of vertices and weighted hyperedges. The hypernetwork model is inspired by biological networks and its learning process is suitable for identifying interacting gene modules. Applied to the analysis of microRNA (miRNA) expression profiles on multiple human cancers, the hypernetwork classifiers identified cancer-related miRNA modules. The results show that our method performs better than decision trees and naive Bayes. The biological meaning of the discovered miRNA modules has been examined by literature search.

关键词： Cancer Gene expression Biological system modeling Data analysis Support vector machines Support vector machine classification Evolutionary computation Humans Decision trees Diseases

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Characterizing the tumor microenvironmental changes in a network perspective

Characterizing the tumor microenvironmental changes in a net...

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第四届全国生物信息学与系统生物学学术大会

作者：李文婷王瑞孙之荣 Institute of Bioinformatics School of Life SciencesTsinghua University Computational Biology and Bioinformatics Program Institute for Genome Science and PolicyDuke University Medical CenterDurhamNC27705

Alternations in tumor microenvironment are critical in driving tumor development. However,it proves difficult to characterize their molecular components and further relate to important pathological *** using a network approach,we characterized systematic expression changes at multi-gene module levels between colorectal tumors with and without recurrence after surgery and confirmed their association with two critical

关键词： Characterizing the tumor microenvironmental changes in a network perspective

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Learning modular structures from network data and node variables 31

Learning modular structures from network data and node varia...

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31st International Conference on Machine Learning, ICML 2014

作者： Azizi, Elham Airoldi, Edoardo M. Galagan, James E. Bioinformatics Program Boston University BostonMA02215 United States Department of Statistics Harvard University CamrbdigeMA02138 United States Departments of Biomedical Engineering and Microbiology Boston University BostonMA02215 United States

ISBN: (纸本)9781634393973

A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individ-uals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, groups are termed modules, and the focus is on estimating the network structure among modules. However, estimation solely from node-specific variables can lead to spurious dependencies, and un- verifiable structural assumptions are often used for regularization. Here, we propose an extended model that leverages direct observations about the network in addition to node-specific variables. By integrating complementary data types, we avoid the need for structural assumptions. We illustrate theoretical and practical significance of the model and develop a reversible- jump MCMC learning procedure for learning modules and model parameters. We demonstrate the method accuracy in predicting modular structures from synthetic data and capability to learn regulatory modules in the Mycobacterium tuberculosis gene regulatory network. Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved.

关键词： Probability distributions

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A randomized algorithm for aligning DNA sequences to reference genomes

A randomized algorithm for aligning DNA sequences to referen...

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IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)

作者： Nam S. Vo Quang Tran Nobal Niraula Vinhthuy Phan Department of Computer Science University of Memphis Memphis TN Bioinformatics Program University of Memphis Memphis TN

ISBN: (纸本)9781479907151

The alignment of reads generated by next-generation sequencers is an important problem in many biomedical applications. Although many methods have been proposed, we introduce a new randomized algorithm with the distinction of having high performance across a wide range of read lengths and base error rates. We utilize two FM indices to facilitate efficient bidirectional searching. Randomization allows us to estimate effectively key parameters, which ultimately account for the consistency in performance of the method. Our method by and large outperformed some of the recent and popular methods over a wide range of read lengths and base error rates.

关键词： Genomics Frequency modulation Indexes Microorganisms bioinformatics Transforms DNA

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Condition specific subnetwork identification using an optimization model

Condition specific subnetwork identification using an optimi...

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第二届最优化与系统生物学国际研讨会

作者： Yong Wang Yu Xia Bioinformatics Program Department of Chemistry Boston University Academy of Mathematics and Systems Science Chinese Academy of Sciences

Subnetworks can reveal the complex patterns of the whole-genome network by extracting the interactions that depend on temporal, spatial, or condition specific context. In this paper we present an optimization framework to identify condition specific subnetworks. This framework allows us to identify the most coherent subnetwork by integrating the information from both nodes and edges in the graph. Importantly we design an algorithm to solve the optimization problem efficiently. It is very fast and can extract subnetworks from large-scale network with about 10000 nodes. As a pilot study we apply our method to identify type 2 diabetes related subnetworks in the human protein-protein interaction network.

关键词： Condition specific subnetwork Optimization model and algorithm Diabetes

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