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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Smilja Stokanović Vukašin Spasojević Ilija Tanasković Jaka Sodnik Nadica Miljković School of Electrical Engineering University of Belgrade Belgrade Serbia School of Electrical Engineering University of Belgrade The Institute for Artificial Intelligence of Serbia R&D Novi Sad Serbia Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

This study presents a computational method for generating eye movement data (specifically horizontal saccade velocity profiles) with the aim of providing benchmark input for the evaluation of data processing workflows and for the employment of machine and deep learning algorithms. The usability of generated saccades is further explored by comparison of two selected algorithms for saccade detection. Precisely, we assess and compare the modified and adaptive I-VT (Identification by Velocity Threshold) algorithm and the MAD (Median Absolute Deviation) algorithm. Both algorithms are tested on the publicly available dataset recorded in one subject while watching a movie trailer and on synthesized saccade velocity profiles. Obtained results suggest that both algorithms have exactly the same performance when generated saccade velocity is not scarcely contaminated with noise (F1Score of 94.42%), while for generated velocity with noise contamination, detection performance of both algorithms significantly degrades (F1 - Score of 36.57% for I-VT and slightly better performance of 40% for MAD algorithm). The proposed method for generating saccadic eye movements profiles could be further utilized to simulate other types of eye movements profiles, such as glissades. Also, the presented algorithm could be explored for the evaluation and design of novel algorithms for eye tracker data analysis, hopefully offering more reliability and higher precision in the eye tracker-based studies.

关键词： Deep learning Data analysis Tracking Noise Employment Reliability engineering Motion pictures Usability Contamination Noise level

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KenDTI: An Ensemble Model for Predicting Drug-Target Interaction by Integrating Multi-Source Information

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第4期18卷 1305-1314页

作者： Yu, Zhimiao Lu, Jiarui Jin, Yuan Yang, Yang Shanghai Jiao Tong Univ Sch Elect Informat & Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Chem & Chem Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Brain Comp & Machine Intelligence Dept Comp Sci & Engn Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr Shanghai 200240 Peoples R China

The identification of drug-target interactions (DTIs) is an essential step in the process of drug discovery. As experimental validation suffers from high cost and low success rate, various computational models have been exploited to infer potential DTIs. The performance of DTI prediction depends heavily on the features extracted from drugs and target proteins. The existing predictors vary in input information and each has its own advantages. Therefore, combining the advantages of individual models and generating high-quality representations for drug-target pairs are effective ways to improve the performance of DTI prediction. In this study, we exploit both biochemical characteristics of drugs via network integration and molecular sequences via word embeddings, then we develop an ensemble model, KenDTI, based on two types of methods, i.e., network-based and classification-based. We assess the performance of KenDTI on two large-scale datasets, The experimental results show that KenDTI outperforms the state-of-the-art DTI predictors by a large margin. Moreover, KenDTI is robust against missing data in input networks and lack of prior knowledge. It is able to predict for drug-candidate chemical compounds with scarce information.

关键词： Drugs Proteins Feature extraction Training Chemicals Predictive models Prediction algorithms Drug-target interaction convolutional neural networks word embeddings network integration ensemble classifier

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Identify ncRNA Subcellular Localization via Graph Regularized k-Local Hyperplane Distance Nearest Neighbor Model on Multi-Kernel Learning

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第6期19卷 3517-3529页

作者： Zhou, Haohao Wang, Hao Tang, Jijun Ding, Yijie Guo, Fei Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin 300072 Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA Univ Elect Sci & Technol China Yangtze Delta Region Inst Quzhou Quzhou 324000 Zhejiang Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China

Non-coding RNAs (ncRNAs) are a type of RNAs which are not used to encode protein sequences. Emerging evidence shows that lots of ncRNAs may participate in many biological processes and must be widely involved in many types of cancers. Therefore, understanding their functionality is of great importance. Similar to proteins, various functions of ncRNAs relies on their subcellular localizations. Traditional high-throughput methods in wet-lab to identify subcellular localization is time-consuming and costly. In this paper, we propose a novel computational method based on multi-kernel learning to identify multi-label ncRNA subcellular localizations, via graph regularized k-local hyperplane distance nearest neighbor algorithm. First, we construct six types of sequencebased feature descriptors and select important feature vectors. Then, we build a multi-kernel learning model with Hilbert-Schmidt independence criterion (HSIC) to obtain optimal weights for vairous features. Furthermore, we propose the graph regularized k-local hyperplane distance nearest neighbor algorithm (GHKNN) as a binary classification model for detecting one kind of non-coding RNA subcellular localization. Finally, we apply One-vs-Rest strategy to decompose multi-label problem of non-coding RNA subcellular localizations. Our method achieves excellent performance on three ncRNA datasets and three human ncRNA datasets, and outperforms other outstanding machine learning methods. Comparing to existing method, our model also performs well especially on small datasets. We expect that this model will be useful for the prediction of subcellular localization and the study of important functional mechanisms of ncRNAs. Furthermore, we establish user-friendly web server (http://***/) with the implementation of our method, which can be easily used by most experimental scientists.

关键词： RNA Location awareness Feature extraction Proteins Kernel Databases Biological information theory Non-coding RNA subcellular localization multi-label classification multi-kernel learning k-local hyperplane distance nearest neighbor

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Ensemble Learning Based Gene Regulatory Network Inference

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INTERNATIONAL JOURNAL ON ARTIFICIAL intelligence TOOLS 2023年第5期32卷 2360005-2360005页

作者： Peignier, Sergio Sorin, Baptiste Calevro, Federica Univ Lyon INSA Lyon INRAE BF2IUMR 203 Villeurbanne France

In the machine learning field, the technique known as ensemble learning aims at combining different base learners in order to increase the quality and the robustness of the predictions. Indeed, this approach has widely been applied to tackle, with success, real world problems from different domains, including computational biology. Nevertheless, despite their potential, ensembles combining results from different base learners have been understudied in the context of gene regulatory network inference. In this paper we applied genetic algorithms and frequent itemset mining, to design small but effective ensembles of gene regulatory network inference methods. These ensembles were evaluated and compared to well-established single and ensemble methods, on both real and synthetic datasets. Results showed that small ensembles, consisting of few but diverse base learners, enhance the exploration of the solution space, and compensate base learners biases, outperforming state-of-the-art methods. Results advocate for the use of such methods as gene regulatory network inference tools.

关键词： bioinformatics gene regulatory network inference ensemble learning

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ResaPred: A Deep Residual Network With Self-Attention to Predict Protein Flexibility

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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ieee TRANSACTIONS ON computational biology AND bioinformatics 2025年第1期22卷 216-227页

作者： Wang, Wei Wan, Shitong Jin, Hu Liu, Dong Zhang, Hongjun Zhou, Yun Wang, Xianfang Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Peoples R China Key Lab Artificial Intelligence & Personalized Lea Xinxiang 453007 Peoples R China Hanyang Univ Sch Elect Engn Ansan 15588 South Korea Anyang Univ Sch Comp Sci & Technol Anyang 455000 Peoples R China Henan Inst Technol Coll Comp Sci & Technol Engn Xinxiang 453000 Peoples R China

Grasping the intrinsic properties of protein structure is crucial for comprehending relevant biological mechanisms, with protein flexibility standing out as a critical aspect. Therefore, the prediction of protein flexibility is of great importance in understanding molecular mechanisms. We propose a deep learning method named ResaPred, which extracts diverse features from protein sequences, such as secondary structure, torsion angle, solvent accessibility, etc. ResaPred is a novel deep network based on a modified 1D residual module and a self-attention mechanism, which effectively extracts deep key features related to flexibility. The modified 1D residual module consists of three convolution layers, with batchnorm and relu layers added after each layer to prevent gradient explosion or vanishing. Incorporating self-attention mechanisms into neural network architectures introduces a significant advantage in capturing long-range dependencies within sequential data. We conduct experiments on the non-strict and strict cases, and achieve state-of-the-art results in predicting flexibility compared to existing methods. Furthermore, we extended our analysis to explore the correlation between protein secondary structure and solvent accessibility with flexibility. Finally, we used two important viral proteins as case studies, confirming the effectiveness of our method in recognizing the flexibility of protein structures.

关键词： Proteins Feature extraction Amino acids Protein sequence Neural networks Accuracy Training Solvents Shape computational biology B-factor protein flexibility residual network

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Combination Supplements for Endometrial Cancer

Combination Supplements for Endometrial Cancer

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Madhurima Mondal Aditya Lahiri Haswanth Vundavilli Giuseppe Del Priore Peter Reeves Aniruddha Datta Department of Electrical and Computer Engineering Texas A&M University College Station TX USA Department of Biomedical and Health Informatics The Children's Hospital of Philadelphia Philadelphia PA USA Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA Department of Obstetrics and Gynecology Morehouse School of Medicine Atlanta GA USA Sumaq Life LLC Michigan USA

This paper suggests an optimal supplement combination (EGCG, Curcumin, Melatonin, Aspirin, and Baicalein) for treating endometrial cancer using a Boolean model. Endometrial cancer affects the uterine lining, with a high incidence in 2023. Surgery, chemotherapy, or radiation may not be options for some patients, leading to increased interest in dietary supplements. However, using ineffective supplements can cause health problems. Our study focuses on safer, less toxic supplements, offering the most efficient combination.

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Mitigating Effects of Packet Loss in Wireless Neural Data Transfer: Exploring the Influence of Low-Cost Recovery Methods on Spikes and HFOs in iEEG

Mitigating Effects of Packet Loss in Wireless Neural Data Tr...

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Midwest symposium on Circuits and Systems (MWSCAS)

作者： Amir Hossein Ayyoubi Behrang Fazli Besheli Chandra Prakash Swamy Jhan L. Okkabaz Michael M. Quach Daniel J. Curry Nuri F. Ince Department of Neurologic Surgery Mayo Clinic Rochester MN USA Department of Bioinformatics and Computational Biology University of Minnesota Minneapolis MN USA Department of Neurology Texas Children's Hospital Houston TX USA Department of Neurosurgery Texas Children's Hospital Houston TX USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

The wireless transmission of neural data may pose the risk of packet loss (PL), potentially compromising signal quality or, in extreme cases, causing complete data loss. Addressing lost packets is essential to ensure data integrity and preserve vital neural patterns. This study investigates the effect of PL interference on epilepsy neuro biomarkers, focusing specifically on interictal epileptiform spikes and high frequency oscillations (HFOs), and the performance of the low computational cost interpolation methods. We observed that 95% of spikes and 81 % of HFOs could be recovered with linear interpolation at 5% PL, while 97% and 86%, respectively, with spline interpolation. Linear interpolation has the potential to recover neural events and reduce the noise floor with modest packet loss levels of up to 5% at a lower computational cost. However, for a higher level of PL, utilizing more intricate methodologies such as spline interpolation becomes imperative.

关键词： Wireless communication Interpolation Noise Packet loss Interference Hafnium oxide Propagation losses

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GMX: Instruction Set Extensions for Fast, Scalable, and Efficient Genome Sequence Alignment 23

GMX: Instruction Set Extensions for Fast, Scalable, and Effi...

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56th ieee/ACM International symposium on Microarchitecture (MICRO)

作者： Doblas, Max Lostes-Cazorla, Oscar Aguado-Puig, Quim Cebry, Nicholas Fontova-Muste, Pau Batten, Christopher Marco-Sola, Santiago Moreto, Miquel Barcelona Supercomputing Ctr Barcelona Spain Univ Autonoma Barcelona Bellaterra Spain Cornell Univ Ithaca NY USA Univ Politecn Cataluna Barcelona Spain

ISBN: (纸本)9798400703294

Sequence alignment remains a fundamental problem in computer science with practical applications ranging from pattern matching to computational biology. The ever-increasing volumes of genomic data produced by modern DNA sequencers motivate improved software and hardware sequence alignment accelerators that scale with longer sequence lengths and high error rates without losing accuracy. Furthermore, the wide variety of use cases requiring sequence alignment demands flexible and efficient solutions that can match or even outperform expensive application-specific accelerators. To address these challenges, we propose GMX, a set of ISA extensions that enable efficient sequence alignment computations based on dynamic programming (DP). GMX extensions provide the basic building-block operations to perform fast tile-wise computations of the DP matrix, reducing the memory footprint and allowing easy integration into widely-used algorithms and tools. Furthermore, we provide an efficient hardware implementation that integrates GMX extensions in a RISC-V-based edge system-onchip (SoC). Compared to widely-used software implementations, our hardware-software co-design leveraging GMX extensions obtains speed-ups from 25-265x, scaling to megabyte-long sequences. Compared to domain-specific accelerators (DSA), we demonstrate that GMX-accelerated implementations demand significantly less memory bandwidth, requiring less area per processing element (PE). As a result, a single GMX-enabled core achieves a throughput per area between 0.35-0.52x that of state-of-the-art DSAs while being more flexible and reusing the core's resources. Post-place-and-route results for a GMX-enhanced SoC in 22nm technology shows that GMX extensions only account for 1.7% of the overall area while consuming just 8.47mW. We conclude that GMX extensions represent versatile and scalable ISA additions to improve the performance of genome analysis tools and other use cases that require fast and efficient sequence al

关键词： ISA extensions hardware acceleration genomics sequence alignment edit-distance bioinformatics microarchitecture

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EnTSSR: A Weighted Ensemble Learning Method to Impute Single-Cell RNA Sequencing Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2781-2787页

作者： Lu, Fan Lin, Yilong Yuan, Chongbin Zhang, Xiao-Fei Le Ou-Yang Shenzhen Univ Coll Elect & Informat Engn Shenzhen Key Lab Media SecurGuangdong Key Lab In Guangdong Lab Artificial Intelligence & Digital E Shenzhen 518060 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518129 Peoples R China Cent China Normal Univ Sch Math & Stat Wuhan 430079 Peoples R China Cent China Normal Univ Hubei Key Lab Math Sci Wuhan 430079 Peoples R China

The advancements of single-cell RNA sequencing (scRNA-seq) technologies have provided us unprecedented opportunities to characterize cellular states and investigate the mechanisms of complex diseases. Due to technical issues such as dropout events, scRNA-seq data contains excess of false zero counts, which has a substantial impact on the downstream analyses. Although several computational approaches have been proposed to impute dropout events in scRNA-seq data, there is no strong consensus on which is the best approach. In this study, we propose a novel weighted ensemble learning method, named EnTSSR, to impute dropout events in scRNA-seq data. By using a multi-view two-side sparse self-representation framework, our model can exploit the consensus similarities between genes and between cells based on the imputed results of various imputation methods. Moreover, we introduce a weighted ensemble strategy to leverage the information captured by various imputation methods effectively. Down-sampling experiments, clustering analysis, differential expression analysis and cell trajectory inference are carried out to evaluate the performance of our proposed model. Experiment results demonstrate that our EnTSSR can effectively recover the true expression pattern of scRNA-seq data.

关键词： Sparse matrices Sequential analysis Data models RNA Mathematical model Linear programming Learning systems Single-cell RNA sequencing dropout events ensemble learning

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Neuroscience, Neuroaesthetics, Semiotics and Effective Communication of COVID-19 Warning Information

Neuroscience, Neuroaesthetics, Semiotics and Effective Commu...

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2nd International symposium on Biomedical and computational biology, BECB 2022

作者： Tello, Alcides Bernardo Chen, Chiao-Yun Muggleton, Neil G. Universidad de Huanuco Huanuco10001 Peru Universidad Nacional Hermilio Valdizan Huanuco10001 Peru Department of Criminology National Chung Cheng University Chiayi Minhsiung600 Taiwan Institute of Cognitive Neuroscience National Central University Taoyuan330 Taiwan Cognitive Intelligence and Precision Healthcare Centre National Central University Taoyuan330 Taiwan

ISBN: (纸本)9783031251900

The need to indicate the significant adverse effects of COVID-19 and the behaviour desired from the population to address this offer an excellent context to consider the varied approaches to providing such information. More specifically, it offers the opportunity to consider the potential utility of neuroscience and what could be usefully added when thinking about the design and presentation of warnings and information. With the understandable wish to neutralise the threat of the COVID-19 pandemic, countries have been displaying miscellaneous messages against the spread of SARS-CoV-2, perhaps some with untested assumptions that those messages would be effective. Despite this, there seems to be highly variable effectiveness in conveying protective messages. Primary causes of poor compliance with various preventive messages might involve a lack of clear vision and direction if the aim is to change citizens’ responsibility for their behaviour, consistency of such changes, and people having confidence in the information they are presented with. It would seem beneficial, in terms of effectiveness, for information presentation to be tailored to target community groups and for this to come from the governments or authorities after determining achievable practical interventions, understanding the citizen’s perceptions of the messages and how science, and particularly neuroscience, shows that the words in language alter behaviour. Last and not least, this allows suitable stylistic consistency to be applied to aid with messaging efficiency and recognisability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Coronavirus

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