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Special Issue on Multi-Modal Biomedical Computing-Deep Transfer Learning

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第4期20卷 2363-2366页

作者： Gao, Honghao Zhang, Zijian (Alex) Barroso, Ramon J. Duran Shanghai Univ Shanghai 200444 Peoples R China Beijing Inst Technol Beijing 102401 Peoples R China Univ Auckland Auckland New Zealand Univ Valladolid Valladolid 47002 Spain

In Recent years, the development of biomedical imaging techniques, integrative sensors, and artificial intelligence has brought many benefits to the protection of health. We can collect, measure, and analyze vast volumes of health-related data using the technologies of computing and networking, leading to tremendous opportunities for the health and biomedical community. Biomedical intelligence, especially precision medicine, is considered one of the most promising directions for healthcare development. This special issue aims to prompt Deep Transfer Learning techniques in Multi-modal Biomedical Computing. After a rigorous review according to relevance, originality, technical novelties, and presentation quality, we selected 21 high-quality manuscripts. A summary is outlined below.

关键词： ieee Standards Biomedical imaging Transfer learning Deep learning Artificial intelligence

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Graph mining of networks from genome biology

Graph mining of networks from genome biology

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7th ieee International conference on bioinformatics and Bioengineering, BIBE

作者： Chin Jr., George Nakamura, Grant C. Chavarria, Daniel G. Sofia, Heidi J. Computational Sciences and Mathematics Division Pacific Northwest National Laboratory Richland WA 99352 United States

ISBN: (纸本)1424415098

We are developing an advanced toolkit for biological networks, using problems from genome biology to drive this work. We now share our experiences in graph analysis and visualization of microbial genome networks using a collection of new and existing graph mining tools and techniques. We address three key problems in genome biology: the organization of complete genome protein networks, feature extraction across chromosomes in microbial strains, and hierarchical structure of protein families and superfamilies. ©2007 ieee.

关键词： biology

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A Particle Swarm Optimization Solution for Challenging Planted(l, d)-Motif Problem

A Particle Swarm Optimization Solution for Challenging Plant...

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10th Annual ieee Symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Reddy, U. Srinivasulu Arock, Michael Reddy, A. V. Natl Inst Technol Dept Comp Applicat Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9781467358750

In bioinformatics, Planted (l, d)-Motif finding is an important and challenging problem, which has many applications. Generally, it is to locate recurring patterns in the promoter regions of co-expressed or co-regulated genes. As we can't expect the pattern to be exact matching copies owing to biological mutations, the motif finding turns to be an NP-complete problem. By approximating the same in different aspects, scientists have provided many solutions in the literature. These solutions are either "exact" or "approximate". All the proposed exact solutions take exponential-time;they need more time to search for larger parameters l and d. The problems of bioinformatics seldom need the exact optimum solution;rather what they need is robust, fast and near optimal solutions. Therefore, it is impractical to use an exact algorithm to search for large parameters of motifs in real biological dataset. In this paper, we have adopted the features of the Particle Swarm Optimization (PSO) with k-nearest neighbor algorithm to solve the Planted (l, d)-Motif Finding Problem. PSO is a global approximation optimization technique and has wide applications. It finds the global best solution by simply adjusting the trajectory of each individual towards its own best location and towards the best particle of the swarm at each generation. We have performed some experiments on synthetic data by increasing number of sequences and the length of the sequences for different (l, d)-Motifs for the following data sets: general instances (10, 2), (11, 2), (12, 3), (15, 4), (16, 5), (18, 6), (20, 7) (30, 11) and (40,15). Challenging instances: (9, 2), (11, 3), (13, 4), (15, 5), (20, 7), (30, 11), (40, 15) and finally, we have applied our proposed method for real biological sequences. From the experimental results we observe that the proposed algorithm is more efficient and accurate compared to existing approximation algorithms and even it works better for larger motif instances.

关键词： Motif Finding Particle Swarm Optimization (PSO) Swarm intelligence (SI) Transcriptional Factor Binding Sites (TFBS) Gene regulation Planted (l, d)-Motifs K-Nearest-Neighbor Algorithm (KNN)

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Remote Pulse Rate Variability Measurement with Adaptive Slope Sum Function 11

Remote Pulse Rate Variability Measurement with Adaptive Slop...

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11th International conference on bioinformatics and computational biology (ICBCB)

作者： Kehang Yang Peixi Li Zhejiang Gongshang Univ Sussex Artificial Intelligence Inst Hangzhou Peoples R China

ISBN: (纸本)9798350397871

Compared to Electrocardiography (ECG) and contact Photoplethysmography (PPG), the signal obtained from remote Photoplethysmography (rPPG) is much more noisy due to the body movement, light variation, sensor noise, low scanning frequency of the cameras, etc. As a result, the measurement of Pulse Rate Variability (PRV) based on rPPG is a more challenging task than measuring PRV and Heart Rate Variability (HRV) from contact PPG and ECG, because it requires precise peak detection on the noisy rPPG signal. To address this issue, some researchers used slope sum function (SSF) to enhance the rising trend of the signal and reduce the downward trend of it, so that the signal became more clear and easier for peak detection. This method is relatively effective, but it has some disadvantages. Firstly, the window size of the SSF is set manually. Secondly, there is a shift between the peaks on the new signal generated by the SSF and the original signal. Thirdly, the parameter is fixed for the entire dataset, which is not ideal for real-life scenario where there are sudden changes in signal. In this paper, we proposed a novel algorithm which adaptively and automatically set the window size of SSF using continuous wavelet transform. We experimentally showed that the proposed method improved the remote measurement of PRV compared to the existing methods in the rPPG framework. We believe that our contribution is conducive to practicalization and commercialization of the related research.

关键词： Remote Photoplethysmography Peak Detection Pulse Rate Variability Slope Sum Function Continuous Wavelet Transform

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Modeling and Simulation Studies of Complex Biological Systems for Precision Medicine and Healthcare

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2019年第1期16卷 91-92页

作者： Shen, Bairong Jiang, Xiaoqian Zhao, Xing-Ming Soochow Univ Ctr Syst Biol Suzhou 215006 Peoples R China UTHealth Sch Biomed Informat Houston TX 77030 USA Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai 200433 Peoples R China

Here present three articles on the private preservation of genome or electronic health record (EHR) data. Three articles focus on the algorithm developing for the analysis of GWAS and EHR-based phenotyping as well as electrocardiogram (ECG)-based disease recognition and classification. Here also include an article developing an algorithm for repositioning of old drugs for their new applications. The articles in this special section proposed several computational model and simulation methods to address diverse medical and healthcare issues which will be helpful to the promotion of the cross-disciplinary researches on the translational medicine and healthcare.

关键词： Diseases Drugs Electrocardiography Electronic Health Records Genomics Health Care Medical Signal Processing Signal Classification Drugs EHR Based Phenotyping Data Translational Medicine computational Model Electrocardiogram Based Disease Classification Electrocardiogram Based Disease Recognition GWAS Electronic Health Record Data Genome Healthcare Complex Biological Systems Special Issues And Sections Biological System Modeling Diseases Data Models Data Privacy Big Data Precision Engineering Medical Services Genomics

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Integrating Multiple Data Sources for Combinatorial Marker Discovery: A Study in Tumorigenesis

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第2期15卷 673-687页

作者： Bandyopadhyay, Sanghamitra Mallik, Saurav Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

Identification of combinatorial markers from multiple data sources is a challenging task in bioinformatics. Here, we propose a novel computational framework for identifying significant combinatorial markers (SCMs) using both gene expression and methylation data. The gene expression and methylation data are integrated into a single continuous data as well as a (post-discretized) boolean data based on their intrinsic (i.e., inverse) relationship. A novel combined score of methylation and expression data (viz., CoMEx) is introduced which is computed on the integrated continuous data for identifying initial non-redundant set of genes. Thereafter, (maximal) frequent closed homogeneous genesets are identified using a well-known biclustering algorithm applied on the integrated boolean data of the determined non-redundant set of genes. A novel sample-based weighted support (WS) is then proposed that is consecutively calculated on the integrated boolean data of the determined non-redundant set of genes in order to identify the non-redundant significant genesets. The top few resulting genesets are identified as potential SCITs. Since our proposed method generates a smaller number of significant non-redundant genesets than those by other popular methods, the method is much faster than the others. Application of the proposed technique on an expression and a methylation data for Uterine tumor or Prostate Carcinoma produces a set of significant combination of markers. We expect that such a combination of markers will produce lower false positives than individual markers.

关键词： SCM CoMEx (maximal biclustered) frequent closed homogeneous genesets sample-based weighted support measure gene expression and methylation data

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Analysis of Interacting Factors Contributing to the Onset of Diabetes Type 2 in the Female Pima Population: A Step towards Understanding the Links

Analysis of Interacting Factors Contributing to the Onset of...

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ieee conference on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Dobromyslin, Vitaly, I Megherbi, Dalila B. Univ Massachusetts Ctr Comp Machine Human Intelligence Networking & Lowell MA 01854 USA Instrumentat Lab Acute Care Analyt Dept Bedford MA 01730 USA

ISBN: (纸本)9781728194684

One of the biggest challenges in building predictive models relates to proper feature selection. As it is well known, good feature selection will result in accurate prediction independently of the classifier. We propose a novel processing pipeline consisting of unsupervised clustering to balance the class prevalence in the training set, which enables the development of stable models with less overfitting. In this paper, we use the proposed pipeline to predict the onset of Type 2 Diabetes (T2D) in the female Pima American Indian population. The Pima population, living near Phoenix, Arizona in the United States, has been known to have the highest T2D prevalence. While it generally remains unclear how various physiological variables and interacting risk factors contribute to the onset of T2D, here for the first time, we quantify the contribution of each of the physiological variables and their interactions to the predicted probability of developing T2D. Our findings in the adult Pima female T2D cohort point to the existence of two distinct population sub-groups, separated primarily by age and the number of pregnancies. For the first time in this dataset, we show several factor interactions: a) subjects with lower BMI (26.0 +/- 2.2 vs. 41.5 +/- 4.9) experienced a 57-78% reduction in T2D conversion rate at the high plasma glucose (>133.7 mg/dL) and high serum insulin (>168.0 pmol/L) levels respectively, and b) low serum insulin (70.1 +/- 16.7 vs. 294.7 +/- 136.6 pmol/L) was associated with a 90% lower risk of T2D conversion rate in individuals with high triceps skin thickness (>34 mm), and interestingly c) non-hypotensive (85.6 +/- 7.2 vs. 54.3 +/- 7.7 mm Hg) women with <= 1 pregnancy had 81% lower risk of T2D conversion. With an increasing number of pregnancies, the trend reversed, and non-hypotensive women experienced a 41% higher rate of T2D conversion. Through the evaluation of multiple feature selection methods and classifiers, we show the potential value of the pro

关键词： Type 2 Diabetes unsupervised machine learning clustering factor interaction analysis Pima American Indians diabetes population cardiovascular predictors for post-pregnancy chronic disease development in the Pima population

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Predicting continuous epitopes in proteins

Predicting continuous epitopes in proteins

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2005 ieee computational Systems bioinformatics conference, Workshops and Poster Abstracts

作者： Tandon, Reeti Adak, Sudeshna Sarachan, Brion FitzHugh, William Heil, Jeremy Narayan, Vaibhav A. Computational Biology and Biostatistics Lab. GE Global Research Bangalore India Computational Biology and Biostatistics Lab. GE Global Research Niskayuna NY United States Celera Genomics 45 West Gude Drive Rockwille MD United States

ISBN: (纸本)0769524427

The ability to predict antigenic sites on proteins is crucial for the production of synthetic peptide vaccines and synthetic peptide probes of antibody structure. Large number of amino acid propensity scales based on various properties of the antigenic sites like hydrophilicity, flexibility/mobility, turns and bends have been proposed and tested previously. However these methods are not very accurate in predicting epitopes and non-epitope regions. We propose algorithms that combine 14 best performing individual propensity scales and give better prediction accuracy as compared to individual scales. © 2005 ieee.

关键词： Proteins

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RFL-Score: Random Forest with Lasso Scoring Function for Protein-Ligand Molecular Docking 21

RFL-Score: Random Forest with Lasso Scoring Function for Pro...

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21st ieee conference on computational intelligence in bioinformatics and computational biology, CIBCB 2024

作者： Arrua, Oscar E. Aderhold, Andrej Werhli, Adriano V. Dos Santos MacHado, Karina Center for Computational Sciences Universidade Federal do Rio Grande RS Rio Grande Brazil

ISBN: (纸本)9798350356632

Due to the growth computational power and easy access to vast amounts of biological data, machine learning (ML) models are being increasingly considered within the field of Rational Drug Design (RDD). In RDD, molecular docking methods are employed to predict the optimal binding mode of a small molecule (ligand) in the binding site of a target receptor (typically a protein). Molecular docking software utilizes Scoring Functions (SFs) to estimate the protein-ligand binding affinity. These SFs can be classified according to their development methodology: Physics-based, Empirical, Knowledge-based, and ML-based. ML-based SFs are trained using features extracted from protein-ligand complexes with known experimental affinities, relying heavily on the attributes used during training. Both the input data and the selected features can significantly impact the accuracy of the SFs. Additionally, different ML algorithms can be applied to select the most appropriate features and propose the SF model. In this study we propose RFL-Score, a novel ML-based SF using Random Forest with Lasso regression. RFL-Score is a ready-to-use SF developed with a straightforward methodology and trained exclusively on open-access biomolecular data. Only open-source software was employed for extracting features from the proteins, ligands, and complexes. The proposed SF was evaluated using the well-established CASF-2016 benchmark achieving outstanding performance compared to other SFs. The RFL-Score scoring function and elements used for training are freely available on GitHub at https://***/rflscore. © 2024 ieee.

关键词： Molecular docking

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computational intelligence in surgery and diagnosis

Computational intelligence in surgery and diagnosis

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20th Annual International conference of the ieee-Engineering-in-Medicine-and-biology-Society

作者： Micheli-Tzanakou, E Rutgers State Univ Dept Biomed Engn Piscataway NJ 08854 USA

ISBN: (纸本)0780351649

We have used advanced signal processing and innovative image processing methods that are used outside the operating room. The software is written in C++ in a windows environment and can be used on any PC. We will demonstrate some applications on a laptop. Applications to be long with computational intelligence for diagnostic purposes and as visualization aids inside and demonstrated, include blood cell classification, mammography, evoked potentials, ophthalmology, EEG and field potentials in Parkinson's disease. The latter methods along with 3D reconstruction of MRI images of Parkinsonian patients, are currently used in the operating room for Pallidotomies.

关键词： Computer aided diagnosis

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