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ieee conference on Decision and Control

作者： Zahra Vahdat Abhyudai Singh Department of Electrical and Computer Engineering University of Delaware Newark DE USA Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Across diverse cell types, genes are often expressed at low levels resulting in significant stochastic fluctuations (noise) in intracellular copy number of mRNAs and proteins over time. Motivated by single-cell experiments, we capture this stochasticity by considering a given gene that toggles between transcriptionally active and inactive states with the time spent in each state being an arbitrary random variable. mRNAs are synthesized from the active state as per a Poisson process and each molecule degrades after a random lifespan (i.e., random time from birth to death). Modeling this process using the Stochastic Hybrid System (SHS) formalism we derive exact analytical results for the statistical moments of molecular counts. Our results show that for fixed mean mRNA numbers, fluctuations in mRNA levels are amplified with decreasing noise in mRNA lifespan. Relaxing the Poisson process assumption, we next consider a scenario where transcription events occur such that the time between successive events is an arbitrarily distributed random variable. In this case, we show that decreasing noise in mRNA lifespan can both increase/decrease mRNA count fluctuations depending on the underlying transcriptional process. Finally, we extend these results to the protein level, where increasing noise in mRNA counts is sometimes associated with decreasing noise in protein copy numbers.

关键词： Proteins Analytical models Fluctuations Exponential distribution Noise Stochastic processes Information processing Random variables Timing Kinetic theory

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A novel framework based on network embedding for the simulation and analysis of disease progression.

A novel framework based on network embedding for the simulat...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Francesco Chiodo Mario Torchia Enza Messina Elisabetta Fersini Tommaso Mazza Pietro Hiram Guzzi Dept. Surgical and Medical Sciences University of Catanzaro Catanzaro Italy University of Milano-Bicocca IRCSS Casa Sollievo della Sofferenza

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Modelling infectious disease spreading is crucial for planning effective containment measures, as shown in the COVID-19 pandemic. The effectiveness of planned measures can also be measured regarding saved lives and economic resources. Therefore, introducing methods able to model the evolution and the impact of measures, as well as planning tailored and updated measures, is a crucial step. Existing models for spreading modelling belong to two main classes: (i) compartmental models based on ordinary differential equations and (ii) contact-based models based on a contact structure using an underlining layer to simulate diffusion. Nevertheless, none of these methods can leverage the high computational power of artificial intelligence and deep learning. We propose a novel framework for simulating and analysing disease progression for these methods. The framework is based on the multiscale simulation of the spreading based on using a multiscale contact model built on top of a diffusion model customised by the user. The evolution of the spreading, modelled as a graph with attributed nodes, is then mapped into a latent space through graph embedding. Finally, deep learning models are used in the latent space to analyse and forecast methods without running expensive computational simulations of the contact-based model.

关键词： Deep learning Analytical models Infectious diseases Pandemics computational modeling Biological system modeling Predictive models

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Getting 'phi psi chi al' with proteins: minimum message length inference of joint distributions of backbone and sidechain dihedral angles

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bioinformatics 2023年第Supplement_1期39卷 I357-I367页

作者： Amarasinghe, Piyumi R. Allison, Lloyd Stuckey, Peter J. de la Banda, Maria Garcia Lesk, Arthur M. Konagurthu, Arun S. Monash Univ Fac Informat Technol Dept Data Sci & Artificial Intelligence Clayton Vic 3800 Australia OPTIMA ARC Ind Training & Transformat Ctr Carlton Vic 3053 Australia Penn State Univ Dept Biochem & Mol Biol University Pk PA 16802 USA

The tendency of an amino acid to adopt certain configurations in folded proteins is treated here as a statistical estimation problem. We model the joint distribution of the observed mainchain and sidechain dihedral angles () of any amino acid by a mixture of a product of von Mises probability distributions. This mixture model maps any vector of dihedral angles to a point on a multi-dimensional torus. The continuous space it uses to specify the dihedral angles provides an alternative to the commonly used rotamer libraries. These rotamer libraries discretize the space of dihedral angles into coarse angular bins, and cluster combinations of sidechain dihedral angles () as a function of backbone conformations. A `good' model is one that is both concise and explains (compresses) observed data. Competing models can be compared directly and in particular our model is shown to outperform the Dunbrack rotamer library in terms of model complexity (by three orders of magnitude) and its fidelity (on average 20% more compression) when losslessly explaining the observed dihedral angle data across experimental resolutions of structures. Our method is unsupervised (with parameters estimated automatically) and uses information theory to determine the optimal complexity of the statistical model, thus avoiding under/over-fitting, a common pitfall in model selection problems. Our models are computationally inexpensive to sample from and are geared to support a number of downstream studies, ranging from experimental structure refinement, de novo protein design, and protein structure prediction. We call our collection of mixture models as PhiSiCal (phi psi chi al).

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HS-DTI: Drug-target Interaction Prediction Based on Hierarchical Networks and Multi-order Sequence Effect

HS-DTI: Drug-target Interaction Prediction Based on Hierarch...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Xu Gong Maotao Liu Haichao Sun Min Li Qun Liu Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Computer-aided drug design utilizes powerful computers to simulate the tasks in drug design, which could significantly accelerate drug development and reduce cost consumption. Drug-target interaction (DTI) prediction is the critical stage in computer-aided drug design and has drawn increased interest from both academia and pharmacogenomics. A variety of computational models for DTI have been available that rely only on the linear representation of compounds and amino acid sequences. However, the significant drawbacks are that these models do not consider the structures and functions of specific functional groups in drugs as well as the multi-order sequence effect of proteins. In this study, we present a novel computational model named HS-DTI that attempts to extract plentiful features of drugs and proteins by capturing functional group structures and incorporating multi-order sequence information, respectively. Specifically, the probability assignment matrix is learned through the hierarchical network module to identify the functional group structures in reconstructed drug graphs on the drug encoder. Next, modelling 1st-order and 2nd-order sequence information based on the hydrophilicity, hydrophobicity, and side-chain mass of the most contiguous and the second-most contiguous amino acid residues. Finally, we comprehensively evaluated the performance of HS-DTI with several state-of-the-art methods on real-world datasets. Experimental results clearly show the effectiveness of the proposed model. Additionally, ablation experiments are also being devised to validate this performance improvement.

关键词： Drugs Proteins computational modeling Biological system modeling Predictive models Feature extraction Amino acids

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An Analytical Paradigm for Exploration of Diabetes Using Machine Learning

An Analytical Paradigm for Exploration of Diabetes Using Mac...

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International conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ritu Chauhan Aarushi Mishra Ruchi Jakhmola Mani Eiad Yafi Megat F. Zuhairi Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Proteomics and Translational Research Lab Center for Medical Biotechnology Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology University of Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Machine Learning is an approach in AI which has swiftly emerged as a transformative and powerful tool in the healthcare industry, changing the methods of diagnosing and predicting diseases. In the present situation, the application of different machine learning methods, including deep learning, ensemble techniques, and natural language processing, across various healthcare activities, such as disease prediction, medical imaging analysis, drug discovery, and tailored treatment suggestions, has offered encouraging prospects for improved diagnostics, treatment, and patient results. By utilizing a diverse range of data sources, including electronic health records, medical imaging, and wearable devices, machine learning algorithms have facilitated early detection, precise prognostication, and customized interventions. The application of machine learning algorithms into health databases is the ongoing area of research concerning future modelling predictions. The current study revolves around the utilization of descriptive analytic methods and machine learning algorithms to predict if an individual is diabetic. The effectiveness of four distinct models of machine learning, specifically Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM) and K Nearest Neighbor (KNN), is assessed and contrasted. The respective accuracies of these classifiers are noted as 89.83%, 77.16%, 84.84%, 81.16%.

关键词： Support vector machines Machine learning algorithms Machine learning Medical services Diabetes Drug discovery Decision trees Medical diagnostic imaging Random forests Diseases

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Decoding the Functional Interactome of Non-model Organisms with PHILHARMONIC 29th

Decoding the Functional Interactome of Non-model Organisms...

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29th International conference on Research in computational Molecular biology, RECOMB 2025

作者： Sledzieski, Sam Versavel, Charlotte Singh, Rohit Ocitti, Faith Devkota, Kapil Kumar, Lokender Shpilker, Polina Roger, Liza Yang, Jinkyu Lewinski, Nastassja Putnam, Hollie Berger, Bonnie Klein-Seetharaman, Judith Cowen, Lenore Center for Computational Biology Flatiron Institute New YorkNY United States Department of Computer Science Tufts University MedfordMA United States Departments of Biostatistics and Bioinformatics Duke University DurhamNC United States School of Biotechnology Faculty of Applied Sciences and Biotechnology Shoolini University Himachal Pradesh Solan173229 India School of Molecular Sciences and School of Ocean Futures Arizona State University TempeAZ United States Department of Mechanical Engineering Seoul National University Seoul Korea Republic of Department of Chemical and Life Science Engineering Virginia Commonwealth University RichmondVA United States Department of Biological Sciences University of Rhode Island KingstonRI United States Computer Science and Artificial Intelligence Laboratory and Department of Mathematics MIT CambridgeMA United States School of Molecular Sciences and College of Health Solutions Arizona State University PhoenixAZ United States

ISBN: (纸本)9783031902512

We introduce PHILHARMONIC, a computational framework that couples deep learning de novo network inference with robust unsupervised spectral clustering algorithms to uncover functional relationships and high-level organization in non-model organisms. Our novel clustering approach produces highly informative functional modules by de-noising the predicted network. We also develop a novel algorithm called ReCIPE, which aims to reconnect disconnected clusters, increasing functional enrichment and biological interpretability. We initially perform remote homology-based functional annotation by leveraging hmmscan and GODomainMiner to assign initial functions to proteins at large evolutionary distances;our clusters then enable us to newly assign functions to uncharacterized proteins through "function by association." We validate the ability of PHILHARMONIC to recover gold-standard functional enrichments in the well-annotated fruit fly D. melanogaster, and apply it to investigate stress response in the reef-building coral P. damicornis and its algal symbiont C. goreaui. Easy to run end-to-end and requiring only a sequenced proteome, PHILHARMONIC is an engine for biological hypothesis generation and discovery in non-model organisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Towards building a Self-Sovereign Identity Framework for Healthcare

Towards building a Self-Sovereign Identity Framework for Hea...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Emmanouil G. Spanakis Ilias Politis Evangelos Markakis Dimitra Papatsaroucha Athanasios Vasileios Grammatopoulos Vaios Bolgouras Anna Angelogianni Christos Xenakis Vangelis Sakkalis Computational Biomedicine Laboratory Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion Crete Greece ISI Athena Research Center Greece Patras Greece InQbit Innovations SRL Romania Electrical & Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Systems Security Lab University of Piraeus Greece

Drifted by the hype of new and efficient machine learning and artificial intelligence models aiming to unlock the information wealth hidden inside heterogeneous datasets across different markets and disciplines, healthcare data are in the center of novel technological advancements in predictive health diagnostics, remote healthcare, assistive leaving and wellbeing. Nevertheless, this emerging market has underlined the necessity of developing new methods and updating existing ones for preserving the privacy of the data and their owners, as well as, ensuring confidentiality and trust throughout the health care data processing pipelines. This paper presents one of the key innovations of a Horizon Europe funded project named "TRUSTEE", which focuses on building a trust and privacy framework for cross-European data exchange by employing a secure and private federated framework to empower companies, organizations, and individuals to securely access data across different disciplines, use and re-use data and metadata to extract knowledge with trust. In particular we present our work on implementing strong authentication and continuous authorization schemes based on the duality of eIDAS trust framework and Self Sovereign Identity (SSI) management to ensure security and trust over authentication, authorization and accounting processes for healthcare.

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Automatic Breathing Pattern Analysis from Reading-Speech Signals

Automatic Breathing Pattern Analysis from Reading-Speech Sig...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Gauri Deshpande Björn W. Schuller Pallavi Deshpande Anuradha Rajiv Joshi Senior Scientist at TCS Research Pune India Pursuing PhD From University of Augsburg Germany Professor and the Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Full Professor at the Bharti Vidyapeeth (DTU) College of Engineering Pune India Department of Physiology Pune Full Professor at the Bharti Vidyapeeth (DTU) Medical College India

As the speech production mechanism is related to the breathing process, speech signals and breathing patterns impact each other. Breathing patterns are the physiological signals which help in understanding the psychological, physiological and cognitive states of an individual. Capturing such patterns relies on the availability of equipment such as respiratory belts, which are costly and uncomfortable to wear for long duration. In this paper, we attempt to extract the breathing patterns from speech signals, which are easily available and can be recorded using a smartphone’s microphone. In the presented work, simultaneous speech and breath signals are captured from 100 Indians of the age group 20 to 25 years while they read a phonetically balanced passage in English language. We have identified five distinct breathing templates; following two broad speech-breath categories, exhibited by the speakers while they read the same passage. For one of the two categories, the time domain features with regression network can extract the breathing patterns from speech with a Pearson correlation coefficient of 0.70. By computational modelling, we distinguish these two breathing categories from speech with a classification accuracy of 79%.

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Estimation of continuous environments by robot swarms: Correlated networks and decision-making

Estimation of continuous environments by robot swarms: Corre...

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ieee International conference on Robotics and Automation (ICRA)

作者： Mohsen Raoufi Pawel Romanczuk Heiko Hamann Science of Intelligence Research Cluster of Excellence Berlin Germany Department of Biology Institute for Theoretical Biology Humboldt Universität zu Berlin Berlin Germany Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Bernstein Center for Computational Neuro-science Berlin Germany Department of Computer and Information Science University of Konstanz Konstanz Germany

Collective decision-making is an essential capability of large-scale multi-robot systems to establish autonomy on the swarm level. A large portion of literature on collective decision-making in swarm robotics focuses on discrete decisions selecting from a limited number of options. Here we assign a decentralized robot system with the task of exploring an unbounded environment, finding consensus on the mean of a measurable environmental feature, and aggregating at areas where that value is measured (e.g., a contour line). A unique quality of this task is a causal loop between the robots' dynamic network topology and their decision-making. For example, the network's mean node degree influences time to convergence while the currently agreed-on mean value influences the swarm's aggregation location, hence, also the network structure as well as the precision error. We propose a control algorithm and study it in real-world robot swarm experiments in different environments. We show that our approach is effective and achieves higher precision than a control experiment. We anticipate applications, for example, in containing pollution with surface vehicles.

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A Novel Concept of Analysing Performance of Deaf Students using Neural Networks

A Novel Concept of Analysing Performance of Deaf Students us...

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2021 International conference on Communication, Control and Information Sciences, ICCISc 2021

作者： Raji, N.R. Siva Balan, R.V. Biji, C.L. Noorul Islam Centre for Higher Education Dept. of Computer Applications Nagercoil India University of Kerala Dept. of Computational Biology and Bioinformatics Thiruvananthapuram India

ISBN: (纸本)9781665412797

Deaf students need modified curriculum reflecting their special needs as exceptional learners. The barriers related to their disability should be identified and recommendations to overcome these barriers should be made. This paper introduces a novel concept of feature engineering in analyzing the performance assessment of deaf students. In this paper an attempt is made to identify the features specific to deaf students and develop a dataset. The classification models are used to classify the students according to their performance. On comparison, the classification accuracy of ANN is found to be better and the performance of this model is evaluated using various parameters-Accuracy, Precision, Recall, F1-Score and AUROC. © 2021 ieee.

关键词： Machine learning

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