ACM SIGBioinformatics Record最新文献

{"title":"Using Hybrid Bioimaging Analysis for Rare Neurological Diseases","authors":"M. Caligiuri, G. Tradigo, P. Vizza","doi":"10.1145/3615362.3615365","DOIUrl":"https://doi.org/10.1145/3615362.3615365","url":null,"abstract":"Hybrid PET/MRI is a promising new approach for simultaneously investigating the relationships between intrinsic metabolic and functional brain changes. In this abstract, we present an analysis workflow for longitudinal PET/MRI studies in rare neurological diseases. We envision that software tools based on the presented workflow can be used to evaluate progressive disease-related volumetric, functional, and metabolic alterations. Furthermore, it could be useful for characterize degenerative pathologies with fast dynamics. Our main contribution is the use of multimodal hybrid PET/MRI data fusion to identify potential markers of disease progression, which could support physicians in future trials to test disease-modifying drugs.","PeriodicalId":198698,"journal":{"name":"ACM SIGBioinformatics Record","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125810642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges for AI Regulation in Health and for Healthcare Organizations: Notes from the University of Florida's NSF-Sponsored Workshop on AI Governance","authors":"M. Prosperi, Jiang Bian, C. Harle, Mo Wang, Gwen Lee, Yixuan Li, G. Štiglic, Jens Kleesiek, Praditporn Pongtriang, P. Veltri","doi":"10.1145/3615362.3615363","DOIUrl":"https://doi.org/10.1145/3615362.3615363","url":null,"abstract":"Artificial intelligence (AI) has impacted human life at many levels, entailing economic and societal changes. AI algorithms are increasingly used by organizations to generate predictions that feed into decisions (e.g., who is eligible for insurance coverage, approved for bank loans, selected for job interviews). Since the data used for developing the algorithms can contain bias such as gender or racial prejudice, AI predictions can become discriminatory. For-profit and not-for-profit organizations face the hurdles of developing, applying, and maintaining governance of AI, making sure that goal optimization responds to ethical and fairness values.","PeriodicalId":198698,"journal":{"name":"ACM SIGBioinformatics Record","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125100345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Stochastic Optimization Algorithms with Specific Bioinformatics Applications","authors":"Aysegül Bumin","doi":"10.1145/3615362.3615364","DOIUrl":"https://doi.org/10.1145/3615362.3615364","url":null,"abstract":"Large scale stochastic optimization is at the core of machine learning and plays an important role in solving optimization problems in bioinformatics. Most of the existing algorithms are based on stochastic gradient descent (SGD), a conceptually simple algorithm that works reasonably well in practice. However, it also suffers from a slow convergence rate and requires manual tuning for best performance. This research focuses on developing stochastic proximal algorithms and improving them further for specific applications in bioinformatics, e.g. drug repurposing. The goal is to 1) make the per-iteration complexity efficient and 2) benefit from the structure of the missing data and external information available. Upon successfully being conducted, it greatly benefits the field of optimization, machine learning and bioinformatics.","PeriodicalId":198698,"journal":{"name":"ACM SIGBioinformatics Record","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123009639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social health recommender system","authors":"Marida De Maria, P. Vizza, P. Lambardi, G. Tradigo","doi":"10.1145/3411750.3411752","DOIUrl":"https://doi.org/10.1145/3411750.3411752","url":null,"abstract":"The amount of information and services available on the web represents an important opportunity for people to enrich and share knowledges. The easy access and the continuous growth of data in the web are responsible of an overload of information that leads the user to navigate in a saturated and often uninteresting and non-comprehensive environment. In the medical-clinical context, a lot of information on the web is often incomplete, inaccurate or completely wrong due to an incorrect sharing and a lack of control of the sources. In this context, recommendation systems become essential to filter truthful information and to target users with respect to their needs. The status of recent covid-19 pandemic highlighted the necessity of having health reliable sources. Health recommender systems support user in medical environment to find right information. In this contribution, we report about a project of health recommender system aiming to: (i) aggregate similar users and (ii) guarantee the truthful and quality of the extracted information through a check of the sources and a validation by the medical scientific community.","PeriodicalId":198698,"journal":{"name":"ACM SIGBioinformatics Record","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115871981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an app for fluid dynamics simulations in small and large vessels","authors":"G. Fragomeni, L. T. Gaudio","doi":"10.1145/3383672.3383673","DOIUrl":"https://doi.org/10.1145/3383672.3383673","url":null,"abstract":"The rheological properties of the blood depend on numerous factors among which the geometry and the size of the conduit in which the blood circulates. The human body is made up by vessels having a different structure and characteristics, which depend on the function they perform, above all on the pressure they have to bear generated at the cardiac level. So, a great criticality when approaching a study on blood flow behavior in the circulatory system is to use an appropriate mathematical model for the circulating fluid. Though the CFD analysis (computational fluid dynamics), going to investigate what are the differences produced by the use of models in the study of vessels of different caliber can be a valid support in the study of vessel fluid dynamics, in particular if they are affected by pathologies. In this regard, the aim of this work, is through the implementation of an application, to carry out a statistical analysis of the effects of different blood flow models in arteries to identifying a prediction about the percentage variation of the shear stress indices involved in the impact of atherosclerotic plaque.","PeriodicalId":198698,"journal":{"name":"ACM SIGBioinformatics Record","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115567680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inference and analysis of gene regulatory networks using data mining techniques","authors":"Syed Sazzad Ahmed, Swarup Roy","doi":"10.1145/3383672.3383675","DOIUrl":"https://doi.org/10.1145/3383672.3383675","url":null,"abstract":"Causality draws the relationship between the cause and the effect among a pair of items. Causality inference in Gene Regulatory Networks (GRNs) is a challenging task due to the limits of appropriate data and large dataset size of real networks. Such an inference is very important because it might give the blueprint of how a complex organism operates which might further reveal the probable cause of some of the terminal diseases. A number of techniques have been proposed and applied to infer causal relationships in various domains. However, they are not specific to regulatory network inference. In our work, we made an empirical study on a few selective methods in inferring causal GRNs. To assess the candidate methods in terms of their quality of inference we used time-series expression data from the DREAM challenges. Our findings revealed that among the selected causality finding techniques, Causation Entropy gives the best result, however, it is computationally expensive. We used the second-best approach, Transfer Entropy, to infer casual GRN of breast cancer datasets. The topological analysis of the network revealed that top out-degree genes such as SLC39A5 which are considered central genes, plays an important role in cancer progression. The main intention behind this research work was to develop an effective inference method for causal Gene Regulatory Network which is computationally less expensive and also considers the effects of time-delay during regulation of one gene by the other. We proposed a novel time delayed inferring method for GRNs using association rule mining. We tried to infer the regulatory sign of the edges i.e. whether influencing positively or negatively. Comparative results highlight that our proposed technique produced better inference results in comparison to several of the existing techniques. We applied the proposed method to perform post inference analysis on malignant cancer network and benign network. Various networks parameters like the degree distribution, average path length, clustering coefficient, network re-wiring structure analysis, network topological variation analysis, and connectivity variation analysis are performed to identify the differentially expressed genes between the two networks. To avoid inference biasness and to obtain a consensus based conclusion we made a comparative analysis between our proposed method and transfer entropy based method and also computed the average score. Such an analysis of the network has revealed four genes, namely, STON2, UMODL1, STK11, and COBL which are already under investigation for its role in various types of cancer as seen from various researches.","PeriodicalId":198698,"journal":{"name":"ACM SIGBioinformatics Record","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128548563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early radiomics experiences in predicting CyberKnife response in acoustic neuroma","authors":"N. D'Amico, R. Sicilia, E. Cordelli, G. Valbusa, E. Grossi, I. B. Zanetti, Deborah Fazzini, G. Scotti, G. Beltramo, G. Iannello, P. Soda","doi":"10.1145/3307616.3307620","DOIUrl":"https://doi.org/10.1145/3307616.3307620","url":null,"abstract":"Vestibular schwannomas, also known as acoustic neuromas, are benign primary intracranial tumor of the myelin-forming cells of the 8th cranial nerve. Stereotactic radiosurgery is one of the available therapies that can effectively control tumor growth, and it can be performed using the CyberKnife robotic device. However, this therapy may have side effects and its efficacy should be assessed up to two years. In this respect, being able to forecast the treatment response using the data collected during the initial and routinely MR images could be a valuable support when planning a personalised therapy. This manuscript therefore introduces a machine learning-based radiomics approach that first computes quantitative biomarkers from MR images and then predicts the treatment response, taking also into consideration the dataset class skewness.","PeriodicalId":198698,"journal":{"name":"ACM SIGBioinformatics Record","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127127937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the analysis of biomedical signals for disease classification","authors":"P. Vizza, G. Tradigo","doi":"10.1145/3307616.3307619","DOIUrl":"https://doi.org/10.1145/3307616.3307619","url":null,"abstract":"The analysis of biomedical signals and images is relevant for early diagnosis, detection and treatment of diseases. It represents the first step in the proper management of pathological conditions. Therefore, it is essential to support clinical practice during the diagnosis process by extracting relevant information and by classifying different diseases. This contribution outlines the methodologies of the most frequently used analysis techniques in biomedicine and their applications. The aim is to report about typical biosignals and bioimages and their analysis to enhance the importance of signal processing in the study and classification of specific diseases.","PeriodicalId":198698,"journal":{"name":"ACM SIGBioinformatics Record","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126655302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of biomedical signal for patterns identification in complex diseases","authors":"D. Mirarchi","doi":"10.1145/3307616.3307618","DOIUrl":"https://doi.org/10.1145/3307616.3307618","url":null,"abstract":"The human body is made up of many different biological systems that give information about their health condition. These information are contained in signals generated by the biological systems. The analysis of these signals is important to extract the information supporting physicians in their daily activity and improving their knowledge on health status. We investigate on biomedical signals for data analysis, as well as for relevant information extraction. Specifically, we have investigated on biomedical signals such as vestibu-lar signals, voice signals, biological data and EEG signals related respectively to vestibular apparatus, speech impairments and neurological diseases, human papilloma virus and geolocalization, and neurological activity. Data have been stored and organized in specific database. Finally, relevant information have been extracted by these data by using data mining techniques.","PeriodicalId":198698,"journal":{"name":"ACM SIGBioinformatics Record","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122603772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACM BCB 2018: brief event summary","authors":"Acm Staff","doi":"10.1145/3287083.3287084","DOIUrl":"https://doi.org/10.1145/3287083.3287084","url":null,"abstract":"ACM BCB served as the international forum concerned with Bioinformatics, Computational Biology and Health Informatics event. It involves biologist, computer scientists, clinicians and computer engineer researchers to discuss about problems in bioinformatics, healthcare, biological and health related algorithms.","PeriodicalId":198698,"journal":{"name":"ACM SIGBioinformatics Record","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130854603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}