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arXiv 2024年

作者： Mallona, Izaskun Lütge, Almut Carrillo, Ben Incicau, Daniel Gerber, Reto Sonrel, Anthony Soneson, Charlotte Robinson, Mark D. Department of Molecular Life Sciences University of Zurich Zurich8057 Switzerland SIB Swiss Institute of Bioinformatics University of Zurich Zurich8057 Switzerland Swiss Data Science Centre Zurich8092 Switzerland Friedrich Miescher Institute for Biomedical Research Basel4056 Switzerland

Benchmarking in bioinformatics is a process of designing, running and disseminating rigorous performance evaluations of methods (software). Benchmarking systems facilitate the benchmarking process by providing an entrypoint to store, coordinate and execute concrete benchmarks. We describe an alpha version of a new benchmarking system, Omnibenchmark, to facilitate benchmark formalization and execution in solo and community efforts. Omnibenchmark provides a benchmark definition syntax (in a configuration YAML file), a dynamic workflow generation based on Snakemake, S3-compatible storage handling, and reproducible software environments using EasyBuild, lmod, Apptainer or conda. Tutorials and installation instructions are available from https://***. © 2024, CC BY-SA.

关键词： bioinformatics

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InDRI: Intelligent Diagnosis Radiology Images 6

InDRI: Intelligent Diagnosis Radiology Images

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Nugroho, Kuncahyo Setyo Ritonga, Imelda Liana Gultom, Roby Pahala Januario Lubis, Elida Afni Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Universitas Imelda Medan Indonesia Medan20238 Indonesia Universitas Imelda Medan Indonesia Medan20238 Indonesia Clinical Pathology Unit Rumah Sakit Imelda Pekerja Indonesia Medan20239 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

The need for an early screening and Computer-Aided Diagnosis (CAD) system based on Artificial Intelligence (AI) for the field of radiology is essential to realize considering the large impact of lung diseases globally. However, developing AI requires a lot of data, which is a challenge because data in the health sector tends to be limited in quantity and has disparities between diagnostic categories (data imbalance). To overcome this issue, DINO ViT models was trained with the publicly available COVID-19 Radiography database in our previous study. The model was subsequently implemented into a web application, with this study focusing on the development of a prototype for the application. The study found this application can be useful for medical personnel and doctors in carrying out early screening and CAD. Meanwhile, it also highlighted the general unfamiliarity with AI applications among medical staff, emphasizing the need for increased education and training on AI's role in healthcare. © 2024 IEEE.

关键词： Lung cancer

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Brain Tumor Classification Using UNet Deep Neural Networks from 3D MRI Images 1

Brain Tumor Classification Using UNet Deep Neural Networks f...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Shaik, Mohammad Khaja Kalpana, D. Sesadri, U. Mukherjee, Saptarshi Dastagiraiah, C. Reddy, Pundru Chandra Shaker St. Ann's College of Engineering and Technology Electronics and Communication Engineering Andhra Pradesh Chirala India Departmet of CSE-Data Science India Vardhaman College of Engineering Computer Science and Engineering Telanagana Hyderabad India Maulana Abul Kalam Azad University of Technology M.Tech-Bioinformatics West Bengal India Anurag University Computer Science and Engineering Telangana Hyderabad India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798350371802

A patient's therapy options and prognosis are both improved by detecting and diagnosing a brain tumor early. Brain tumors can be located and diagnosed with the help of magnetic resonance imaging (MRI). In real practice, however, medical personnel rely entirely on their own time and expertise when manually identifying brain tumors from a vast number of MRI scans. These days, medical diagnosis and treatment recommendation systems that use computer-aided expertise are all the rage. Brain tumor detection makes use of a plethora of frameworks based on machine-learning(ML) &deep-learning(DL). The purpose of this research is to present a DL-based approach for effective brain tumor segmentation and classification. The research uses convolutional-neural-networks(CNNs) for tumor classification after volumetric segmentation of MRI-images using the 3D-UNet model. To demonstrate the models' validity, loss and precision plots are provided. We compare the outcomes of several methods described in the literature with the measured performance of the models we offer. When compared to the most recent cutting-edge methods, the proposed work comes out on top. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Meta-Posterior Consistency for the Bayesian Inference of Metastable Systems

arXiv

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arXiv 2024年

作者： Adams, Zachary P. Mukherjee, Sayan Freie Universität Berlin Department of Mathematics and Computer Science Center for Scalable Data Analytics and Artificial Intelligence Universität Liepzig Germany Center for Scalable Data Analytics and Artificial Intelligence Department of Computer Science Universität Liepzig Germany Max Planck Institute for Mathematics in the Natural Sciences Duke University Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics United States

The vast majority of the literature on learning dynamical systems or stochastic processes from time series has focused on stable or ergodic systems, for both Bayesian and frequentist inference procedures. However, most real-world systems are only metastable, that is, the dynamics appear to be stable on some time scale, but are in fact unstable over longer time scales. Consistency of inference for metastable systems may not be possible, but one can ask about metaconsistency: Do inference procedures converge when observations are taken over a large but finite time interval, but diverge on longer time scales? In this paper we introduce, discuss, and quantify metaconsistency in a Bayesian framework. We discuss how metaconsistency can be exploited to efficiently infer a model for a sub-system of a larger system, where inference on the global behavior may require much more data. We also discuss the relation between metaconsistency and the spectral properties of the model dynamical system in the case of uniformly ergodic *** Codes 62F15, 60J70 © 2024, CC BY.

关键词： Stochastic systems

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An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2

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Procedia Computer Science 2024年 245卷 470-479页

作者： Alam Ahmad Hidayat Rudi Nirwantono Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia 11480

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings.

关键词： bioinformatics computational biology dengue protein docking structural biology

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quantms: A cloud-based pipeline for proteomics reanalysis enables the quantification of 17521 proteins in 9,502 human samples

Research Square

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Research Square 2023年

作者： Dai, Chengxin Pfeuffer, Julianus Wang, Hong Sachsenberg, Timo Demichev, Vadim Kohlbacher, Oliver Perez-Riverol, Yasset Chongqing Key Laboratory of Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Algorithmic Bioinformatics Freie Universität Berlin Berlin Germany Department of Computer Science Applied Bioinformatics University of Tübingen Tübingen72076 Germany Charité - Universitätsmedizin Berlin Berlin10117 Germany European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom

Public proteomics data is rapidly increasing, creating a computational challenge for large-scale reanalysis. Here, we introduce quantms, an open-source cloud-based pipeline for massively parallel proteomics data analysis. We used quantms to reanalyze 56 of the largest datasets, comprising 26801 instrument files from 9502 human samples, to quantify 17521 based on 1.02 million unique peptides. Based on standard file formats improves the reproducibility and deposition of the data to ProteomeXchange. © 2023, CC BY.

关键词： Mass spectrometry

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Improving Cancer Gene Identification by Enhancing Common Information Between the PPI Network and Gene Functional Association 39

Improving Cancer Gene Identification by Enhancing Common Inf...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Deng, Chao Li, Hongdong Wang, Jianxin School of Computer Science and Engineering Central South University Changsha410083 China Hunan Provincial Key Lab on Bioinformatics Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi830091 China

ISBN: (纸本)157735897X

Identifying cancer genes is crucial for treatment and understanding pathogenesis. Recent methods typically leverage protein-protein interaction (PPI) networks or gene functional association data from annotated gene sets. There may be some shared neighborhood structure information between these two types of gene association data. While this common information may contain more accurate gene association information, existing methods often overlook this potential. To address this gap, we introduce DISFusion, which integrates multi-omics cancer data, PPI networks, and gene functional associations to identify cancer genes. A key innovation of DISFusion is the cross-view decorrelation loss, which enhances the common information between PPI networks and gene functional associations, thereby improving prediction accuracy. Extensive experiments indicate that DISFusion outperforms state-of-the-art methods and exhibits greater generalization ability. Moreover, analysis of CPTAC pan-cancer proteomic data highlights significant associations between the 30 novel cancer genes predicted by DISFusion and multiple cancer types, underscoring its practical utility. These findings validate the effectiveness of enhancing common information and provide new insights into cancer gene identification. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Autonomous self-evolving research on biomedical data: the DREAM paradigm

arXiv

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arXiv 2024年

作者： Deng, Luojia Wu, Yijie Ren, Yongyong Lu, Hui Department of Bioinformatics and Biostatistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China SJTU-Yale Joint Center for Biostatistics and Data Science Technical Center for Digital Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China

In contemporary biomedical research, the efficiency of data-driven approaches is hindered by large data volumes, tool selection complexity, and human resource limitations, necessitating the development of fully autonomous research systems to meet complex analytical needs. Such a system should include the ability to autonomously generate research questions, write analytical code, configure the computational environment, judge and interpret the results, and iteratively generate more in-depth questions or solutions, all without human intervention. In this study, we developed DREAM, the first biomedical data-dRiven self-Evolving Autonomous systeM, which can independently conduct scientific research without any human involvement. Utilizing a clinical dataset and two omics datasets, DREAM demonstrated its ability to raise and deepen scientific questions, with difficulty scores for clinical data questions surpassing top published articles by 5.7% and outperforming GPT-4 and bioinformatics graduate students by 58.6% and 56.0%, respectively. Overall, DREAM has a success rate of 80% in autonomous clinical data mining. Certainly, human can participate in different steps of DREAM to achieve more personalized goals. After evolution, 10% of the questions exceeded the average scores of top published article questions on originality and complexity. In the autonomous environment configuration of the eight bioinformatics workflows, DREAM exhibited an 88% success rate, whereas GPT-4 failed to configure any workflows. Using the clinical dataset as an example, DREAM was over 10,000 times more efficient than the average scientist with a single computer core, and capable of revealing new discoveries. As a self-evolving autonomous research system, DREAM provides an efficient and reliable solution for future biomedical research. In the future, this research paradigm may also have a revolutionary impact on other data-driven scientific research fields. Copyright © 2024, The Authors. All right

关键词： Clinical research

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The effect of visual advanced driver assistance systems on a following human driver in a mixed-traffic condition

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Procedia Computer Science 2023年 216卷 221-229页

作者： Dedy Ariansyah Bens Pardamean Giandomenico Caruso Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers’ ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-traffic condition.

关键词： sustainable transport management platooning car-following visual advanced driver assistance system virtual realtiy

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Development of artificial intelligence for variable rate application based oil palm fertilization recommendation system 6

Development of artificial intelligence for variable rate app...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Firmansyah, Erick Pardamean, Bens Ginting, Candra Mawandha, Hangger Gahara Pratama Putra, Dian Suparyanto, Teddy Institute of Agriculture STIPER Faculty of Agriculture Yogyakarta55282 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Institute of Agriculture STIPER Faculty of Agriculture Yogyakarta55282 Indonesia

ISBN: (纸本)9781665449373

The need for fertilization knowledge that meets good cultivation principles is the background for the need for a platform that can help provide advice on fertilization implementation. The conventional method through leaf and soil analysis to determine the dose of fertilization has many obstacles in its implementation. The availability of experts to provide advice is also not available at all times. One way to transfer knowledge to non-experts is to use Artificial Intelligence (AI) in the form of a dynamic expert system. The purpose of this research is to create a dynamic expert system that can provide advice and apply fertilization quickly, cheaply, accurately, and available at all times. In building this AI system, a knowledge base, working memory, inference engine, and interface are needed. The implementation of dynamic expert system development consists of four stages, namely (1) literature study, (2) laboratory analysis, (3) construction of an inference engine, and (4) interface creation. Based on the literature study that has been carried out, it is known that there are three domains of knowledge of oil palm fertilization, namely the domain of soil, plants and climate. Each of these knowledge domains consists of attributes, sub-attributes, and facts of knowledge which are then arranged in the form of mathematical rules. The relationship between the three knowledge domains is used as the basis for making fertilization rules. The result of this research is an application with Artificial Intelligence to provide information on nutritional needs according to plant age, population, production, land area and location. The application provides recommendations for the type, frequency, amount of fertilizer, and time of application. © 2021 IEEE.

关键词： Inference engines

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