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

作者： Mohsen Abbaspour Onari Isel Grau Marco S. Nobile Yingqian Zhang Information Systems Group Eindhoven Artificial Intelligence Systems Institute Eindhoven University of Technology Eindhoven The Netherlands Information Systems Group Eindhoven University of Technology Eindhoven The Netherlands Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Venice Italy

Nowadays, a large amount of structured and unstructured data is being produced in various fields, creating tremendous opportunities to implement Machine Learning (ML) algorithms for decision-making. Although ML algorithms can outperform human performance in some fields, the black-box inherent characteristics of advanced models can hinder experts from exploiting them in sensitive domains such as medicine. The black-box nature of advanced ML models shadows the transparency of these algorithms, which could hamper their fair and robust performance due to the complexity of the algorithms. Consequently, individuals, organizations, and societies will not be able to achieve the full potential of ML without establishing trust in its development, deployment, and use. The field of eXplainable Artificial intelligence (XAI) endeavors to solve this problem by providing human-understandable explanations for black-box models as a potential solution to acquire trustworthy AI. However, explainability is one of many requirements to fulfill trustworthy AI, and other prerequisites must also be met. Hence, this survey analyzes the fulfillment of five algorithmic requirements of accuracy, transparency, trust, robustness, and fairness through the lens of the literature in the medical domain. Regarding that medical experts are reluctant to put their judgment aside in favor of a machine, trustworthy AI algorithmic fulfillment could be a way to convince them to use ML. The results show there is still a long way to implement the algorithmic requirements in practice, and scholars need to consider them in future studies.

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Consensus Clustering Strategy for Cell Type Assignments of scRNA-seq Data

Consensus Clustering Strategy for Cell Type Assignments of s...

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

作者： Simone G. Riva Brynelle Myers Paolo Cazzaniga Francesca M. Buffa Andrea Tangherloni Weatherall Institute of Molecular Medicine Oxford UK Radcliffe Department of Medicine University of Oxford Oxford UK Wellcome Centre for Human Genetics University of Oxford Oxford UK Department of Human and Social Sciences University of Bergamo Bergamo Italy Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4 Milano Italy Department of Oncology University of Oxford Oxford UK Department of Computing Sciences Bocconi University Bocconi Institute for Data Science and Analytics Milan Italy Department of Computing Sciences Bocconi University Bocconi Institute for Data Science and Analytics Milan Italy

Cell type annotation is a crucial step for analyzing single-cell RNA sequencing data. Among others, single-cell Automatic Labeling of cell POpulations (scALPO) is a computational pipeline developed to automatically assign the cell types to the identified clusters in scRNA-seq data. Different from most of the approaches, scALPO relies only on the information on marker genes from published literature. Specifically, after the definition of the dataset obtained from gene information retrieved from online databases, the Leiden clustering algorithm is executed to partition cells that are finally annotated. Since the Leiden algorithm might struggle to obtain a reliable outcome under certain circumstances, in this work, we include several clustering algorithms in scALPO, and we propose a pseudo-voting consensus approach that combines the outcome of a set of clustering algorithms. The results obtained on three different datasets show that the consensus approach can improve the cell type annotation without selecting a specific clustering algorithm that best suits the data under investigation.

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

2nd International Symposium on Biomedical and Computational ...

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

ISBN: (纸本)9783031251900

The proceedings contain 58 papers. The special focus in this conference is on Biomedical and computational biology. The topics include: Microanatomy of Left Bundle Branch in Chinese Adult Hearts: Aiming to the Research on Morphological Information;Screening of Key Genes in Retinoblastoma and Construction of ceRNA Regulatory Network;protective Effects of Resveratrol on Brain Edema and Microstructural Changes in Human Brain After Acute Alcohol Intake: Assessment by Diffusion Weighted Kurtosis Imaging;effects of Music Style on Mental Fatigue Induced by Continuous Cognitive Tasks;construction of Constitutive Saccharomyces Cerevisiae Engineered Strain of β-glucosidase;high-Yielding Laccase Strain Breeding and Optimization of Fermentation Conditions;Chinese Medical Text Classification with RoBERTa;operation Status and Optimization Strategy of Hierarchical Medical System in Wuhan;analysis and Forecast of Meteorological Factors on Henoch-SchÖnlein Purpura in Jining;A Self-supervised 3D/2D Registration Method for Incomplete DSA Vessels;human Machine Interaction Using Zero Force Sensing Switches Incorporating Self-adaptation;driving, Personality and Decision-Making: A Study for Understanding Human Behavior;the Brain Activity of the Bilingual Code-Switching Communication;A Symbolic Regression Approach to Hepatocellular Carcinoma Diagnosis Using Hypermethylated CpG Islands in Circulating Cell-Free DNA;multi-agent Neural-Like Models for the Integration of Multimodal Medical Examination Data;the Importance of Prognostic Variables to Monitoring Heart Failure Using Health Management Systems;real-Time Contactless Breathing Monitoring System Using Radar with Web Server;Neuroscience, Neuroaesthetics, Semiotics and Effective Communication of COVID-19 Warning Information;the Effect of CoViD-19 Pandemic on the Hospitalization of a Department of Oncology of an Italian Hospital;Modeling LOS After Percutaneous Valvuloplasty: A Bicentric Study.

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DeepFusionDTA: Drug-Target Binding Affinity Prediction With Information Fusion and Hybrid Deep-Learning Ensemble Model

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第5期19卷 2760-2769页

作者： Pu, Yuqian Li, Jiawei Tang, Jijun Guo, Fei Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin 300350 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 100049 Peoples R China Tianjin Univ Minist Educ Key Lab Syst Bioengn Tianjin 300072 Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

Identification of drug-target interaction (DTI) is the most important issue in the broad field of drug discovery. Using purely biological experiments to verify drug-target binding profiles takes lots of time and effort, so computational technologies for this task obviously have great benefits in reducing the drug search space. Most of computational methods to predict DTI are proposed to solve a binary classification problem, which ignore the influence of binding strength. Therefore, drug-target binding affinity prediction is still a challenging issue. Currently, lots of studies only extract sequence information that lacks feature-rich representation, but we consider more spatial features in order to merge various data in drug and target spaces. In this study, we propose a two-stage deep neural network ensemble model for detecting drug-target binding affinity, called DeepFusionDTA, via various information analysis modules. First stage is to utilize sequence and structure information to generate fusion feature map of candidate protein and drug pair through various analysis modules based deep learning. Second stage is to apply bagging-based ensemble learning strategy for regression prediction, and we obtain outstanding results by combining the advantages of various algorithms in efficient feature abstraction and regression calculation. Importantly, we evaluate our novel method, DeepFusionDTA, which delivers 1.5 percent CI increase on KIBA dataset and 1.0 percent increase on Davis dataset, by comparing with existing prediction tools, DeepDTA. Furthermore, the ideas we have offered can be applied to in-silico screening of the interaction space, to provide novel DTIs which can be experimentally pursued. The codes and data are available from https://***/guofei-tju/DeepFusionDTA.

关键词： Drugs Proteins computational modeling Compounds Feature extraction Deep learning Convolution Drug-target binding affinity multi-information fusion multi-channel hybrid neural network bagging-based ensemble learning

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Artificial intelligence-Driven Protein Folding System Employing Alpha Fold

Artificial Intelligence-Driven Protein Folding System Employ...

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Sowmiya, R. Kumar, M. Pravin Kumar, T.M. Sathish Rasika, P. Devi, M. Poornima Dharshini, S. Priya Sns College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Department of Medical Electronics Tamil Nadu Erode India K.S.R. College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Sns College of Pharmacy and Health Sciences Department of Pharmaceutics Tamil Nadu Coimbatore India Sns College of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India Periyar College of Pharmaceutical Sciences Department of Pharmaceutics Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798331509828

Artificial intelligence (AI) is a main role to solve the lot of real-live issues. One of the significant challenges in the bioinformatics that researchers have been identified recently is understand the protein folding. First of all, people want to give a big hand off to the advancements because, day to day activities is performed in 3-dimensional basis that is formed based on a sequence of amino acids using protein folds. The folding of proteins is mostly related to three major issues. The first issue is the lack of a specific folding code. There is a folding mechanism in the second one. The ability to ascertain the 3-dimensional structure of proteins presents the last challenge. The structural components and microscopic machinery that make up human cells are called proteins. They provide vital tasks include storing oxygen, breaking down food, and creating scaffolding to support cells in maintaining their structure. Here, Amino acids is formed based on a single chain mechanism because amino acids folds safely and it performs a unique structure that is incorporated here. All living things, including viruses, are composed of proteins, including every element of human body and every other organism. The computational approach can be used to predict the structure of minuscule proteins. In this research work explained about how artificial intelligence is incorporated in predictions of protein structure and artificial intelligence give the solution for the problem of problem folding. © 2025 ieee.

关键词： Scaffolds (biology)

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iPhosS(Deep)-PseAAC: Identification of Phosphoserine Sites in Proteins Using Deep Learning on General Pseudo Amino Acid Compositions

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第3期19卷 1703-1714页

作者： Naseer, Sheraz Hussain, Waqar Khan, Yaser Daanial Rasool, Nouman Univ Management & Technol Dept Comp Sci Lahore 54770 Pakistan Univ Punjab Natl Ctr Artificial Intelligence Lahore 54590 Pakistan Univ Karachi Int Ctr Chem & Biol Sci Karachi 75270 Pakistan

Among all the PTMs, the protein phosphorylation is pivotal for various pathological and physiological processes. About 30 percent of eukaryotic proteins undergo the phosphorylation modification, leading to various changes in conformation, function, stability, localization, and so forth. In eukaryotic proteins, phosphorylation occurs on serine (S), Threonine (T) and Tyrosine (Y) residues. Among these all, serine phosphorylation has its own importance as it is associated with various importance biological processes, including energy metabolism, signal transduction pathways, cell cycling, and apoptosis. Thus, its identification is important, however, the in vitro, ex vivo and in vivo identification can be laborious, time-taking and costly. There is a dire need of an efficient and accurate computational model to help researchers and biologists identifying these sites, in an easy manner. Herein, we propose a novel predictor for identification of Phosphoserine sites (PhosS) in proteins, by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) with deep features. We used well-known DNNs for both the tasks of learning a feature representation of peptide sequences and performing classifications. Among different DNNs, the best score is shown by Covolutional Neural Network based model which renders CNN based prediction model the best for Phosphoserine prediction. Based on these results, it is concluded that the proposed model can help to identify PhosS sites in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.

关键词： Proteins Predictive models Feature extraction Deep learning Amino acids Training Task analysis Deep features deep neural networks phosphorylation phosphoserine PseAAC

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Reconstruction of Gene Regulatory Networks Using Multiple Datasets

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第3期19卷 1827-1839页

作者： Saremi, Mehrzad Amirmazlaghani, Maryam Amirkabir Univ Technol Dept Artificial Intelligence Tehran *** Iran

Motivation: Laboratory gene regulatory data for a species are sporadic. Despite the abundance of gene regulatory network algorithms that employ single data sets, few algorithms can combine the vast but disperse sources of data and extract the potential information. With a motivation to compensate for this shortage, we developed an algorithm called GENEREF that can accumulate information from multiple types of data sets in an iterative manner, with each iteration boosting the performance of the prediction results. Results: The algorithm is examined extensively on data extracted from the quintuple DREAM4 networks and DREAM5's Escherichia coli and Saccharomyces cerevisiae networks and sub-networks. Many single-dataset and multi-dataset algorithms were compared to test the performance of the algorithm. Results show that GENEREF surpasses non-ensemble state-of-the-art multi-perturbation algorithms on the selected networks and is competitive to present multiple-dataset algorithms. Specifically, it outperforms dynGENIE3 and is on par with iRafNet. Also, we argued that a scoring method solely based on the AUPR criterion would be more trustworthy than the traditional score. Availability: The Python implementation along with the data sets and results can be downloaded from ***/msaremi/GENEREF.

关键词： Vegetation Prediction algorithms Steady-state Forestry Regression tree analysis Perturbation methods Inference algorithms Gene regulatory network random forest boosting

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MuST: Maximizing the Latent Capacity of Spatial Transcriptomics Data with Multi-modality Structure Transformation

MuST: Maximizing the Latent Capacity of Spatial Transcriptom...

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2024 ieee International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Zang, Zelin Li, Liangyu Xu, Yongjie Li, Jiahao Duan, Chenrui Sun, Yi Lei, Zhen Li, Stan Z. Westlake University Ai Lab Research Center for Industries of the Future Hangzhou China China Institute of Automation China School of Artificial Intelligence China

ISBN: (纸本)9798350386226

Spatial transcriptomics (ST) technologies have revolutionized the study of gene expression patterns in tissues by providing multimodality data in transcriptomic, spatial, and morphological, offering opportunities for understanding tissue biology beyond transcriptomics. However, we identify the modality bias phenomenon in ST data species, i.e., the inconsistent contribution of different modalities to the labels leads to a tendency for the analysis methods to retain the information of the dominant modality. How to mitigate the adverse effects of modality bias to satisfy various downstream tasks remains a fundamental challenge. This paper introduces Multiple-modality Structure Transformation, named MuST, a novel methodology to tackle the challenge. MuST integrates the multi-modality information contained in the ST data effectively into a uniform latent space to provide a foundation for all the downstream tasks. It learns intrinsic local structures by topology discovery strategy and topology fusion loss function to solve the inconsistencies among different modalities. Thus, these topology-based and deep learning techniques provide a solid foundation for a variety of analytical tasks while coordinating different modalities. The effectiveness of MuST is assessed by performance metrics and biological significance. The results show that it outperforms existing state-of-the-art methods with clear advantages in the precision of identifying and preserving structures of tissues and biomarkers. MuST offers a versatile toolkit for the intricate analysis of complex biological systems. The code is available at https://***/zangzelin/code_Must. © 2024 ieee.

关键词： Tissue

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Towards Understanding Evolved Memory Retaining Circuitry in the Wireworld Cellular Automaton

Towards Understanding Evolved Memory Retaining Circuitry in ...

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computational intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), ieee symposium on

作者： Clifford Bohm Arend Hintze Department of Integrative Biology Michigan State University East Lansing MI USA Department for MicroData Analytics Dalarna University Falun Sweden

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

Understanding how cognition evolved will require a deep dive into the fundamental workings of evolved cognitive systems. Here, we focus on analyzing digitally evolved “Wire brains”, a model of cognition based on the Wireworld cellular automata. By studying these simple yet functionally rich models of collective intelligence, we aim to shed light on the operational principles of distributed cognitive systems at their most basic level. We present a novel methodology for analyzing evolved Wire brains, employing information theory to map information flows and uncover the operational principles underlying cognitive processes. Our findings reveal key mechanisms of memory in Wire brains, offering broader implications for understanding the evolution of biological cognition and inspiring innovative approaches in artificial intelligence and cognitive science.

关键词： Evolution (biology) Cognitive processes Automata Collective intelligence Brain modeling Cognitive science Wire Cognitive systems Biological information theory computational intelligence

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TP-ML: A Machine-Learning-Based Tool to Identify Threonine Proteases Using Sequence-Derived Optimal Features

IEEE Transactions on Computational Biology and Bioinformatic...

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ieee Transactions on computational biology and bioinformatics 2025年第3期22卷 984-996页

作者： Ahmad Firoz Adeel Malik Nitin Mahajan Le Thi Phan Hani S. H. Mohammed Ali Chang-Bae Kim Balachandran Manavalan Department of Biological Sciences Faculty of Science King Abdulaziz University Jeddah Saudi Arabia Princess Dr. Najla Bint Saud Al-Saud Center for Excellence Research in Biotechnology King Abdulaziz University Jeddah Saudi Arabia Institute of Intelligence Informatics Technology Sangmyung University Seoul South Korea Wugen St. Louis MO USA Computational Biology and Bioinformatics Laboratory College of Biotechnology and Bioengineering Department of Integrative Biotechnology Sungkyunkwan University Suwon South Korea Department of Biotechnology Sangmyung University Seoul South Korea

Threonine proteases (TPs) are enzymes vital for several biological processes and diseases including Alzheimer’s disease and cancer. Their potential to target and degrade proteins intracellularly makes them valuable for various therapeutic and industrial applications. However, traditional experimental methods for identifying and characterizing novel TPs are exhaustive, time-consuming, and expensive. To address this, we developed TP-ML, a support vector machine-based prediction tool that can differentiate TP from non-TP sequences. We generated a benchmark dataset and calculated the physicochemical and compositional features using primary amino acid sequences. Subsequently, a comparison was made between the two feature selection approaches to identify the optimal feature sets from the original encodings. These optimal features were then used to train five different machine-learning classifiers, each assessed independently. TP-ML was selected as the best model showing consistent performance during cross-validation and independent evaluation, and achieved an accuracy of 0.934 and 0.888, respectively. We anticipate TP-ML to be a powerful tool for identifying TPs, aiding in their experimental characterization and industrial application exploration.

关键词： Enzymes Protein sequence Biotechnology bioinformatics computational biology Encoding Training Peptides Electronic mail Biological system modeling

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