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

作者： An, Ulzee Cai, Na Dahl, Andy Sankararaman, Sriram Department of Computer Science UCLA Los AngelesCA United States Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany Section of Genetic Medicine University of Chicago ChicagoIL United States Department of Human Genetics David Geffen School of Medicine UCLA Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine UCLA Los AngelesCA United States

ISBN: (纸本)9783031047480

Biomedical datasets that aim to collect diverse phenotypic and genomic data across large numbers of individuals are plagued by the large fraction of missing data The ability to accurately impute or "fill-in" missing entries in these datasets is critical to a number of downstream applications. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Evaluation of Frameworks for Epidemic Variants and Infectivity using an Evolutionary Algorithm

Evaluation of Frameworks for Epidemic Variants and Infectivi...

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ieee Symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Michael Dubé Sheridan Houghten Dept. of Mathematics and Statistics University of Guelph Guelph ON Canada Dept. of Computer Science Brock University St. Catharines ON Canada

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484633

An evolutionary algorithm is used to evolve personal contact networks representing the individuals in a population and the interactions between them. Such networks can be used to track the progress of an epidemic, as it passes from infected individuals to others. Two fitness functions are used: epidemic duration and epidemic spread. Each of these is evaluated in the context of new variants being introduced during the course of the epidemic. Individuals infected with one variant obtain immunity to that variant and possible partial immunity to future variants. Two frameworks for epidemic variants are presented. In the first, infectivity is coupled directly to how well an individual's immunity covers the variant. In the second, infectivity is decoupled, causing a much higher number of infections but with many of lessened severity due to immunity.

关键词： Epidemics Sociology Evolutionary computation Statistics bioinformatics Immune system computational biology

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Computing Shortest Hyperpaths for Pathway Inference in Cellular Reaction Networks 27th

Computing Shortest Hyperpaths for Pathway Inference in Cel...

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27th International conference on Research in computational Molecular biology, RECOMB 2023

作者： Krieger, Spencer Kececioglu, John Computational Biology Department Carnegie Mellon University PittsburghPA15213 United States Department of Computer Science The University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9783031291180

Signaling and metabolic pathways, which consist of a series of reactions producing target molecules from source compounds, are cornerstones of cellular biology. The cellular reaction networks containing such pathways can be precisely modeled by directed hypergraphs, where each reaction corresponds to a hyperedge, directed from its set of reactants to its set of products. Given such a network represented by a directed hypergraph, inferring the most likely set of reactions that produce a given target from a given set of sources corresponds to finding a shortest hyperpath, which is NP-complete. The best methods currently available for shortest hyperpaths either offer no guarantee of optimality, or exclude hyperpaths containing cycles even though cycles are abundant in real biological pathways. We derive a novel graph-theoretic characterization of hyperpaths, leveraged in a new formulation of the general shortest hyperpath problem as an integer linear program that for the first time handles hyperpaths containing cycles, and present a novel cutting-plane algorithm that can solve this integer program to optimality in practice. This represents a major advance over the best prior exact algorithm, which was limited to acyclic hyperpaths (and hence fails to find a solution for the many biological instances where all hyperpaths are in fact cyclic). In comprehensive experiments over thousands of instances from the standard NCI-PID and Reactome databases, we demonstrate that our cutting-plane algorithm quickly finds an optimal hyperpath, with a median running-time of under ten seconds and a maximum time of around thirty minutes, even on large instances with many thousands of reactions. Source code implementing our cutting-plane algorithm for shortest hyperpaths in a new tool called Mmunin is available free for research use at http://***. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Molecules

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Neuro-Degenerative Disorder Prediction Using Curated Short Mutated Genome Sequences Through Hybrid Deep Neural Networks

Neuro-Degenerative Disorder Prediction Using Curated Short M...

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ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Naman Bhatia L V S K B Kasyap Varanasi Parth Raghuwanshi Avinash Saroj Chandra Mohan Dasari Computer Science and Engineering Indian Institute Of Information Technology Sri City India Computer Science and Engineering Group Indian Institute of Information Technology Sri City India

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Psychological disorders are a range of conditions characterized by significant disruptions in a person's thoughts, emotions, and behavior. The WHO reports that over a billion people worldwide have neurological illnesses, and ineffective diagnostics hinder treatment. Mental health disorders continue to be a pervasive global health concern, necessitating innovative early detection and intervention approaches. The classical methods often rely on expensive clinical diagnoses and computational techniques in genome-wide association studies (GWAS) that can be time-consuming. We curate the short genome sequence data for five disorders of fixed and variable lengths through two novel approaches to address these limitations. To our knowledge, this is the first work that suggests extracting short genetic sequences for psychiatric disorders. We investigate different deeplearning (DL) techniques to predict mental health disorders based on the prepared datasets. Our study aims to enhance the accuracy and timeliness of mental health assessments, potentially revolutionizing mental health care delivery. The classification accuracies for the proposed graph neural network with HMM representation on fixed length genome sequence were as follows: SCZ=80%; BP=71% which is the highest when compared to the other DL models. The outcomes of our research reveal promising results, with the curated short genome sequences significantly contributing to identifying mental health disorders. The proposed data curation approaches lay a solid basis for the adoption of DL based mental health prediction tools in healthcare systems. The model encourages early intervention and ultimately improves patient outcomes.

关键词： Accuracy computational modeling Genomics Hidden Markov models Mental health Medical services Graph neural networks Encoding bioinformatics computational biology

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Guest Editorial for Special Section on the 14th International conference on Intelligent Computing (ICIC)

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2020年第5期17卷 1474-1475页

作者： Huang, De-Shuang Bevilacqua, Vitoantonio Gromiha, Michael Tongji Univ Instute Machine Learning & Syst Biol Shanghai 201804 Peoples R China Polytech Bari Elect & Elect Dept Via Orabona I-70126 Bari BA Italy Indian Inst Technol IIT Dept Biotechnol Madras Tamil Nadu India

The papers in this special section were presented at the Fourteenth International conference on Intelligent Computing (ICIC) held in Wuhan, China, on August 15-18, 2018.

关键词： Special Issues And Sections computational intelligence Machine Learning bioinformatics computational biology

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CytoFlow: A Novel computational Method to Construct Signal Transduction Networks at Single-Cell Resolution Based on Flow Networks

CytoFlow: A Novel Computational Method to Construct Signal T...

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Medical Artificial intelligence (MedAI), ieee International conference on

作者： Yi Dai Ziheng Duan Siwei Xu Jing Zhang Department of Computer Science University of California Irvine Irvine USA

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

The signal transduction network is essential for eukaryotic cellular communication and response to environmental signals, with disruptions leading to various diseases. As our understanding of intracellular signaling expands, the demand for computational tools that can efficiently synthesize this information into comprehensive networks grows. To meet this need, we developed CytoFlow, a novel computational model designed to construct cell-type-specific signal transduction net-works by leveraging protein-protein interaction and single-cell transcriptomics data. Specifically, it models the network from receptors to transcription factors as a penalized flow network, optimized using linear programming techniques. We validated CytoFlow's superior precision against existing methods in reconstructing the yeast mitogen-activated protein kinase pathway, and demonstrated its ability to identify cell-type-specific signaling patterns in the human prefrontal cortex and peripheral blood mononuclear cells. In summary, CytoFlow offers a precise and cost-effective solution for constructing detailed signal transduction networks, advancing our capacity to understand and analyze cellular signaling processes.

关键词： Proteins computational modeling RNA Transcriptomics Cells (biology) Linear programming Data models computational efficiency Signal resolution Diseases

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IPRIns: A tool with the improved precision and recall for insertion detection in the human genome 9

IPRIns: A tool with the improved precision and recall for in...

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9th ieee International conference on bioinformatics and computational biology, ICBCB 2021

作者： Piwluang, Sakkayaphab Wichadakul, Duangdao Software Engineering Program Faculty of Engineering Chulalongkorn University Department of Computer Engineering Bangkok Thailand Faculty of Engineering Chulalongkorn University Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9780738132020

An insertion is a specific type of the structural variations. The identification of insertions in a human genome is essential for the study of diseases or their functional effects. There are many tools available for identifying the insertion type with different methods and strategies. However, most of them could not deliver both good recall and precision, especially for the real datasets sequenced with the paired-end short reads. In this paper, we propose iPRIns, a new computational method for detecting insertions aiming to improve both precision and recall. The proposed method with discovering and filtering processes outperformed all other three tools for 5 out of 10 real datasets, the variations of NA12878, for both precision and recall. iPRIns is released under the open-source GPLv3 license. The source code and documentation are available at https://***/cucpbioinfo/iPRIns. © 2021 ieee.

关键词： bioinformatics

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conference Committee

Conference Committee

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ieee International conference on bioinformatics and computational biology (ICBCB)

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AI trustworthiness in prostate cancer imaging: a look at algorithmic and system transparency

AI trustworthiness in prostate cancer imaging: a look at alg...

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2023 ieee EMBS Special Topic conference on Data Science and Engineering in Healthcare, Medicine and biology, ieeeCONF 2023

作者： Colantonio, Sara Berti, Andrea Buongiorno, Rossana Del Corso, Giulio Pachetti, Eva Pascali, Maria Antonietta Kalantzopoulos, Charalampos Kalokyri, Varvara Kondylakis, Haridimos Tachos, Nikolaos Fotiadis, Dimitris Giannini, Valentina Mazzetti, Simone Regge, Daniele Papanikolaou, Nickolas Marias, Konstantinos Tsiknakis, Manolis The Institute of Information Science and Technologies The National Research Council of Italy Via G. Moruzzi 1 Pisa56124 Italy The Foundation for Research and Technology Hellas Institute of Computer Science Heraklion Greece The Foundation for Research and Technology Hellas Ioannina Greece University of Ioannina Greece The Champalimaud Foundation Computational Clinical Imaging Group Lisboa Portugal The Department of Surgical Sciences University of Turin Turin10126 Italy The Department of Radiology Candiolo Cancer Institute FPO-IRCCS Candiolo10060 Italy

ISBN: (纸本)9798350383386

A responsible approach to artificial intelligence and machine learning technologies, grounded in sound scientific foundations, technical robustness, rigorous testing and validation, risk-based continuous monitoring and alignment with human values is imperative to guarantee their favorable impact and prevent any adverse effects they may have on individuals and communities. An essential aspect of responsible development is transparency, which constitutes a fundamental principle of the European approach towards artificial intelligence. Transparency can be achieved at different levels, such as data origin and use, system development, operation and usage. In this paper, we present the techniques implemented and delivered in the EU H2020 ProCAncer-I project to meet the transparency requirements at the different levels *** Relevance: This paper examines the primary transparency hurdles in artificial intelligence for medical imaging diagnostics, and presents the approaches that the EU H2020 project ProCAncer-I is taking to address them. © 2023 ieee.

关键词： Transparency

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Discovery of Bioactive Constituents for Colitis From Traditional Chinese Medicine Prescription via Deep Neural Network

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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ieee TRANSACTIONS ON computational biology AND bioinformatics 2025年第2期22卷 946-955页

作者： Ren, Zhixiang Ren, Yiming Liu, Pengfei Shu, Qi Ma, Huijuan Xu, Huan Peng Cheng Lab Shenzhen 518055 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 528406 Peoples R China East China Univ Sci & Technol Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met Shanghai 200237 Peoples R China East China Univ Sci & Technol Sch Pharm Shanghai Key Lab New Drug Design Shanghai 200237 Peoples R China Anhui Univ Sci & Technol Joint Res Ctr Occupat Med & Hlth IHM Hefei 231131 Peoples R China Anhui Univ Sci & Technol Sch Publ Hlth Hefei 231131 Peoples R China

Colitis is a commonly encountered inflammatory disease in colon tissue, which can be triggered by various causes. Although a few ingredients in traditional Chinese medicine (TCM) have been identified as effective for the treatment of colitis, it remains a great challenge to discover the potential therapeutic bioactive constituents and their modes of action among thousands of ingredients in TCM prescriptions. To address this issue, we propose a pipeline that combines deep neural network (DNN) with network pharmacology to discover bioactive constituents. By integrating the herbal information network of 9,845 nodes and 161,950 edges, which includes detailed information on bioactive molecules and protein targets, with a prescription list expanded through a novel data augmentation strategy, the DNN can recommend diverse herbal combinations. Network pharmacology study revealed that the 10 most frequent constituents in recommended prescriptions were associated with multiple inflammatory signaling pathways. To verify the bioactive constituents in the recommended prescriptions, 5 selected constituents were administrated to BALB/c mice with colitis. Suppressive effects of disease progression and pro-inflammatory factors comparable to sulfasalazin were observed with these compounds, revealing the effectiveness of our artificial intelligence strategy in discovering bioactive constituents from TCM prescriptions.

关键词： Training Pharmacology Compounds Diseases Medical diagnostic imaging Data mining computational biology bioinformatics Artificial neural networks Proteins Deep neural network bioactive constituents colitis anti-inflammation network pharmacology traditional Chinese medicine prescriptions

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