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26th International conference on Research in computational Molecular biology, RECOMB 2022

作者： Hua, Dunming Gu, Ming Du, Yanyi Qi, Li Du, Xiangjun Bai, Zhidong Zhu, Xiaopeng Tian, Dechao Sun Yat-sen University Guangdong Shenzhen510275 China Chongqing Municipal Center for Disease Control and Prevention Chongqing400042 China KLASMOE and School of Mathematics and Statistics Northeast Normal University Jilin Changchun130024 China Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9783031047480

The recent development of mapping technologies, such as Hi-C, that probes the 3D genome organization reveals that a chromosome is divided into topologically associating domains (TADs). TADs are genomic regions where chromatin loci are more frequently interacting with chromatin loci from the same TADs rather than from other TADs. TADs are functional units for transcriptional regulation, such as constraining interactions between enhancers and promoters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Topology

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Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach

Predicting COVID-19 Severity Using a Cut-and-Solve Feature S...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Smith, Kenneth Chan, Michael Brandenburg, John Jones, Katarina A Campagna, Shawn R. Garvin, Michael Templeton, Alan R. Jacobson, Daniel Cruchaga, Carlos Climer, Sharlee University of Missouri - St. Louis Department of Computer Science Saint Louis United States Optimization GotSport Jacksonville Beach United States Stord United States University of Tennessee Knoxville Department of Chemistry Knoxville United States The University of Tennessee Department of Chemistry Knoxville United States Oak Ridge National Laboratory Computational Predictive Biology Oak Ridge United States Washington University Department of Biology Saint Louis United States Oak Ridge National Laboratory Biosciences Division Oak Ridge United States Washington University School of Medicine Department of Psychiatry Saint Louis United States

ISBN: (纸本)9798350337488

Individuals with coronavirus disease 2019 (COVID-19) infection present in a variety of ways, ranging from asymptomatic or mild cough, to organ failure or death. One of the major challenges for the medical community is the quick and accurate determination of how COVID-19 will progress in an individual. Herein, we introduce a new Cut-and-Solve based feature selection program for identifying predictive feature sets in heterogeneous data. We analyze proteomics data from Washington University to identify models ranging in size from a single feature up to five. Validation of logistic regression models using area under the curve (AUC) were applied for both a holdout data set and an independent data set from Massachusetts General Hospital. A variety of known and novel biomarkers for COVID-19 severity were identified. The best model for predicting severe (ventilation or death) vs. non-severe infection is achieved for CALCOCO2 and STC1, with an average AUC=0.81. Based on the known severity markers, several different proteomic pathways are identified. Enrichment analysis indicates activity associated with inflammatory response, as well as myelination and cardiac function. © 2023 ieee.

关键词： COVID-19 Feature Selection Mixed Integer Programming

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A Novel Graph Topology-Based GO-Similarity Measure for Signature Detection From Multi-Omics Data and its Application to Other Problems

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第2期19卷 773-785页

作者： Mallick, Koushik Mallik, Saurav Bandyopadhyay, Sanghamitra Chakraborty, Sikim Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Univ Texas Hlth Sci Ctr Houston Dept Sch Biomed Informat Houston TX 77030 USA Dev Econ Vert Brookings Inst India Ctr New Delhi 110021 India

Large scale multi-omics data analysis and signature prediction have been a topic of interest in the last two decades. While various traditional clustering/correlation-based methods have been proposed, but the overall prediction is not always satisfactory. To solve these challenges, in this article, we propose a new approach by leveraging the Gene Ontology (GO) similarity combined with multiomics data. In this article, a new GO similarity measure, ModSchlicker, is proposed and the effectiveness of the proposed measure along with other standardized measures are reviewed while using various graph topology-based Information Content (IC) values of GO-term. The proposed measure is deployed to PPI prediction. Furthermore, by involving GO similarity, we propose a new framework for stronger disease-based gene signature detection from the multi-omics data. For the first objective, we predict interaction from various benchmark PPI datasets of Yeast and Human species. For the latter, the gene expression and methylation profiles are used to identify Differentially Expressed and Methylated (DEM) genes. Thereafter, the GO similarity score along with a statistical method are used to determine the potential gene signature. Interestingly, the proposed method produces a better performance ( > 0.9 avg. accuracy and > 0.95 AUC) as compared to the other existing related methods during the classification of the participating features (genes) of the signature. Moreover, the proposed method is highly useful in other prediction/classification problems for any kind of large scale omics data.

关键词： Graph topology-based semantic similarity gene signature multi-omics data Information Content disease classification

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Heterogeneity in Blood Biomarker Trajectories After Mild TBI Revealed by Unsupervised Learning

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第3期19卷 1365-1378页

作者： Bui, Lien A. Yeboah, Dacosta Steinmeister, Louis Azizi, Sima Hier, Daniel B. Wunsch, Donald C. Olbricht, Gayla R. Obafemi-Ajayi, Tayo Missouri State Univ Dept Comp Sci Springfield MO 65897 USA Missouri Univ Sci & Technol Dept Math & Stat Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Elect & Comp Engn Appl Computat Intelligence Lab Rolla MO 65409 USA Missouri State Univ Engn Program Springfield MO 65897 USA

Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed annually in the United States. Concussion is a heterogeneous disorder in causation, symptoms, and outcome making precision medicine approaches to this disorder important. Persistent disabling symptoms sometimes delay recovery in a difficult to predict subset of mTBI patients. Despite abundant data, clinicians need better tools to assess and predict recovery. Data-driven decision support holds promise for accurate clinical prediction tools for mTBI due to its ability to identify hidden correlations in complex datasets. We apply a Locality-Sensitive Hashing model enhanced by varied statistical methods to cluster blood biomarker level trajectories acquired over multiple time points. Additional features derived from demographics, injury context, neurocognitive assessment, and postural stability assessment are extracted using an autoencoder to augment the model. The data, obtained from FITBIR, consisted of 301 concussed subjects (athletes and cadets). Clustering identified 11 different biomarker trajectories. Two of the trajectories (rising GFAP and rising NF-L) were associated with a greater risk of loss of consciousness or post-traumatic amnesia at onset. The ability to cluster blood biomarker trajectories enhances the possibilities for precision medicine approaches to mTBI.

关键词： Trajectory Sports Blood Injuries Proteins Precision medicine Biological system modeling Unsupervised learning statistical analysis concussions predictive modeling GFAP NF-L tau UCH-L1

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Explainability techniques for Artificial intelligence models in medical diagnostic

Explainability techniques for Artificial Intelligence models...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Fedra Rosita Falvo Mario Cannataro Department of Medical and Surgical Sciences Magna Graecia University Catanzaro Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The integration of artificial intelligence (AI) techniques into clinical settings presents critical challenges due to the opacity of machine learning models, often referred to as "black boxes": this study explores the application of explainability techniques, specifically Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), in the context of medical diagnostics. Using the "Diabetes Health Indicators Dataset", we applied Logistic Regression as a predictive model to identify key risk factors for diabetes and evaluate the ability of explainability techniques to improve transparency and interpretability. The results demonstrate that SHAP provides a detailed global and local understanding of feature importance, offering clinicians insights into key predictors such as HighBP, CholCheck, and GenHlth; LIME complements this by delivering intuitive explanations for individual predictions, enabling rapid and accessible interpretation. The combination of these techniques enhances trust in AI systems by providing both comprehensive insights and actionable explanations. Challenges related to computational complexity, scalability, and the integration of these methods into clinical workflows are also discussed, along with recommendations for future research aimed at developing scalable, interpretable AI models for ethical and responsible medical use.

关键词： Logistic regression computational modeling Biological system modeling Scalability Machine learning Predictive models Diabetes Medical diagnosis Medical diagnostic imaging Context modeling

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Predicting miRNA-Disease Association Based on Improved Graph Regression

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第6期19卷 3604-3613页

作者： Li, Lei Gao, Zhen Zheng, Chun-Hou Qi, Rong Wang, Yu-Tian Ni, Jian-Cheng Qufu Normal Univ Sch Cyber Sci & Engn Qufu 273165 Shandong Peoples R China Anhui Univ Sch Artifial Intelligence Hefei 230601 Anhui Peoples R China

Recently, as a growing number of associations between microRNAs (miRNAs) and diseases are discovered, researchers gradually realize that miRNAs are closely related to several complicated biological processes and human diseases. Hence, it is especially important to construct availably models to infer associations between miRNAs and diseases. In this study, we presented Improved Graph Regression for miRNA-Disease Association Prediction (IGRMDA) to observe potential relationship between miRNAs and diseases. In order to reduce the inherent noise existing in the acquired biological datasets, we utilized matrix decomposition algorithm to process miRNA functional similarity and disease semantic similarity and then combining them with existing similarity information to obtain final miRNA similarity data and disease similarity data. Then, we applied miRNA-disease association data, miRNA similarity data and disease similarity data to form corresponding latent spaces. Furthermore, we performed improved graph regression algorithm in latent spaces, which included miRNA-disease association space, miRNA similarity space and disease similarity space. Non-negative matrix factorization and partial least squares were used in the graph regression process to obtain important related attributes. The cross validation experiments and case studies were also implemented to prove the effectiveness of IGRMDA, which showed that IGRMDA could predict potential associations between miRNAs and diseases.

关键词： Diseases Semantics Predictive models Prediction algorithms Matrix decomposition Biological system modeling Proteins MiRNA similarity disease similarity miRNA-disease association improved graph regression

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Document-Level Chemical-Induced Disease Relation Extraction via Hierarchical Representation Learning

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第5期19卷 2782-2793页

作者： Zhao, Weizhong Zhang, Jinyong Yang, Jincai Jiang, Xingpeng He, Tingting Cent China Normal Univ Sch Comp Hubei Prov Key Lab Artificial Intelligence & Smar Wuhan 430079 Hubei Peoples R China Cent China Normal Univ Natl Language Resources Monitoring & Res Ctr Netw Wuhan 430079 Hubei Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China

Over the past decades, Chemical-induced Disease (CID) relations have attracted extensive attention in biomedical community, reflecting wide applications in biomedical research and healthcare field. However, prior efforts fail to make full use of the interaction between local and global contexts in biomedical document, and the derived performance needs to be improved accordingly. In this paper, we propose a novel framework for document-level CID relation extraction. More specifically, a stacked Hypergraph Aggregation Neural Network (HANN) layers are introduced to model the complicated interaction between local and global contexts, based on which better contextualized representations are obtained for CID relation extraction. In addition, the CID Relation Heterogeneous Graph is constructed to capture the information with different granularities and improve further the performance of CID relation classification. Experiments on a real-world dataset demonstrate the effectiveness of the proposed framework.

关键词： Diseases Chemicals Context modeling Data mining Biomedical optical imaging Biological system modeling Visualization Chemical-induced disease relation extraction hypergraph aggregation heterogeneous graph document-level

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Branching Out to Speciation in a Model of Fractionation: The Malvaceae

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1875-1884页

作者： Zhang, Yue Zheng, Chunfang Islam, Sindeed Kim, Yong-Min Sankoff, David Univ Ottawa Dept Math & Stat Ottawa ON K1S 2C3 Canada Korea Res Inst Biosci & Biotechnol Korean Bioinformat Ctr Daejeon 34141 South Korea

Fractionation is the genome-wide process of losing one gene per duplicate pair following whole genome doubling (WGD). An important type of evidence for duplicate gene loss is the frequency distribution of similarities between paralogous gene pairs in a genome or orthologous gene pairs in two species. We extend a previous branching process model for fractionation, originally accounting for paralog similarities, to encompass the distribution of ortholog similarities, after multiple rounds of whole genome doubling and fractionation, with the speciation event occurring at any point. We estimate the fractionation rates during all the inter-event periods in each lineage of the plant family Malvaceae. We suggest a major correction of the phylogenetic position of the durian sub-family, and discover a new triplication event in this lineage.

关键词： bioinformatics Genomics Fractionation Phylogeny Trajectory Biological system modeling computational modeling Whole genome duplication fractionation branching process gene pair similarity distribution Malvaceae durian

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A Quantitative Approach for Evaluating Disease Focus and Interpretability of Deep Learning Models for Alzheimer's Disease Classification

A Quantitative Approach for Evaluating Disease Focus and Int...

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2024 ieee International conference on bioinformatics and Biomedicine, BIBM 2024

作者： Tam, Thomas Yu Chow Liang, Litian Chen, Ke Wang, Haohan Wu, Wei University of Pittsburgh Dept. of Health Information Management PittsburghPA United States Carnegie Mellon University Computational Biology Department PittsburghPA United States University of Illinois Urbana-Champaign School of Information Sciences Urbana United States University of Illinois at Urbana-Champaign School of Information Sciences ChampaignIL United States

ISBN: (纸本)9798350386226

Deep learning (DL) models have shown significant potential in Alzheimer's Disease (AD) classification. However, understanding and interpreting these models remains challenging, which hinders the adoption of these models in clinical practice. Techniques such as saliency maps have been proven effective in providing visual and empirical clues about how these models work, but there still remains a gap in understanding which specific brain regions DL models focus on and whether these brain regions are pathologically associated with *** bridge such gap, in this study, we developed a quantitative disease-focusing strategy to first enhance the interpretability of DL models using saliency maps and brain segmentations;then we propose a disease-focus (DF) score that quantifies how much a DL model focuses on brain areas relevant to AD pathology based on clinically known MRI-based pathological regions of AD. Using this strategy, we compared several state-of-the-art DL models, including a baseline 3D ResNet model, a pretrained MedicalNet model, and a MedicalNet with data augmentation to classify patients with AD vs. cognitive normal patients using MRI data;then we evaluated these models in terms of their abilities to focus on disease-relevant regions. Our results show interesting disease-focusing patterns with different models, particularly characteristic patterns with the pretrained models and data augmentation, and also provide insight into their classification performance. These results suggest that the approach we developed for quantitatively assessing the abilities of DL models to focus on disease-relevant regions may help improve the interpretability of these models for AD classification and facilitate their adoption for AD diagnosis in clinical practice. The code is publicly available at https://***/Liang-lt/ADNI. © 2024 ieee.

关键词： Neurodegenerative diseases

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A computational Monte Carlo Simulation Strategy to Determine the Temporal Ordering of Abnormal Age Onset Among Biomarkers of Alzheimer's Disease

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第5期19卷 2613-2622页

作者： Guo, Xiaojuan Chen, Kewei Chen, Yinghua Xiong, Chengjie Su, Yi Yao, Li Reiman, Eric M. Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Beijing Normal Univ Beijing Key Lab Brain Imaging & Connect Beijing 100875 Peoples R China Banner Alzheimers Inst Phoenix AZ 85006 USA Arizona State Univ Dept Math & Stat Tempe AZ 85281 USA Univ Arizona Coll Med Dept Neurol Tucson AZ 85721 USA Arizona Alzheimers Consortium Phoenix AZ 85014 USA Knight Alzheimers Dis Res Ctr St Louis MO 63130 USA Washington Univ Div Biostat Sch Med St Louis St Louis MO 63110 USA Translat Genom Res Inst Div Neurogen Phoenix AZ 85004 USA Univ Arizona Dept Psychiat Tucson AZ 85721 USA

To quantitatively determining the temporal ordering of abnormal age onsets (AAO) among various biomarkers for Alzheimer's disease (AD), we introduced a computational Monte-Carlo simulation (CMCS) to statistically examine such ordering of an AAO pair or over all AAOs. The CMCS 1) simulates longitudinal data, estimates AAO for each iteration, and finally assesses the type-I error of an AAO pair or all AAO ordering. Using hippocampus volume (V-HC), cerebral glucose hypometabolic convergence index (HCI), plasma neurofilament light (NfL), mini-mental state exam (MMSE), the auditory verbal learning test-long term memory (AVLT-LTM), short term memory (AVLT-STM) and clinical-dementia rating sum of box scale (CDR-SOB) from 382 mild cognitive impairment converters and non-converters, the CMCS estimated type-I error for the earlier AAO of V-HC, AVLT_STM and AVLT_LTM each than MMSE was significant (p<0.002). The type-I error for the overall AAO temporal ordering of V-HC <= AVLT_STM <= AVLT_LTM < HCI <= MMSE <= CDR-SOB <= NfL was p = 0.012. These findings showed that our CMCS is capable of providing statistical inferences for quantifying AAO ordering which has important implications in advancing our understanding of AD.

关键词： Biomarkers Alzheimer's disease Diseases Trajectory Biological system modeling Human computer interaction Brain modeling Abnormal age onset Alzheimer's disease biomarker linear mixed effects Monte Carlo simulation temporal ordering

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