A new approach to clustering biological data using message passing

2004 ◽  
Author(s):  
Huimin Geng ◽  
D. Bastola ◽  
H. Ali
Keyword(s):  
Message Passing ◽  
Biological Data ◽  
New Approach

scholarly journals Physical activity in Sahara Moroccan hemodialysis patients

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
A. Bahadi ◽  
H. Lagtarna ◽  
S. Benbria ◽  
Y. Zajjari ◽  
D. Elkabbaj ◽  
...  
Keyword(s):  
Physical Activity ◽  
Hemodialysis Patients ◽  
Education Level ◽  
Chronic Hemodialysis ◽  
Biological Data ◽  
New Approach ◽  
Male Predominance ◽  
Low Education ◽  
Reduced Physical Activity ◽  
A Prospective Study

Abstract Objective The evaluation of physical activity for chronic hemodialysis patients is a new approach for patient global care. The objective of this work is to evaluate the physical activity in chronic hemodialysis patients and identify the risk factors associated with reduced physical activity. This is a prospective study for 6 months including 150 chronic hemodialysis patients in the Guelmim-Oued Noun Regionin Moroccan Sahara. We use Baecke's survey, translated and validated in Arabic local language. The socio-demographic, clinical, and biological data were completed during the interrogation and from the medical records of the patients. Results The mean age of our patients was 54.6 ± 16.4 years, with male predominance (59%). Most patients have a low education level and 60% were illiterate. Hypertension was found in 54% of our patients, diabetes in 39%, and cardiovascular disease in 10% of patients. Low Physical activity was associated with gender (OR = 4.05), age (OR = 1.03) and high education level (OR = 0.2). Our work has met the various pre-established objectives, however other more specific studies must be conducted to better characterize the profile of physical activity in chronic hemodialysis patients.

scholarly journals Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

2021 ◽  
Author(s):  
Roozbeh Manshaei
Keyword(s):  
Unique Solution ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Biological Systems ◽  
Network Reconstruction ◽  
Biological Data ◽  
Tangible Interaction ◽  
Survival Prediction ◽  
New Approach ◽  
Gene Regulatory

Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can inform and reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.

scholarly journals Physical activity in Sahara Moroccan hemodialysis patients

2020 ◽  
Author(s):  
abdelaali bahadi ◽  
Hamza lagtarna ◽  
sanae benbria ◽  
yassir zajjari ◽  
Driss El kabbaj ◽  
...  
Keyword(s):  
Physical Activity ◽  
Hemodialysis Patients ◽  
Education Level ◽  
Chronic Hemodialysis ◽  
Biological Data ◽  
New Approach ◽  
Male Predominance ◽  
Low Education ◽  
Reduced Physical Activity ◽  
A Prospective Study

Abstract Background: The evaluation of physical activity for chronic hemodialysis patients is a new approach for the patient global care. The objective of this work is to evaluate the physical activity in chronic hemodialysis patients and identify the risk factors associated with reduced physical activity. Methods: This is a prospective study during 6 months including 150 chronic hemodialysis patients in the Guelmim-Oued Noun Regionin moroccan sahara. We use Baecke's survey, translated and validated in Arabic local language. The socio-demographic, Clinical and biological data were completed during the interrogation and from the medical records of the patients. Results: The mean age of our patients was 54.6 +/- 16.4 years, with male predominance (59%). Most patients have a low education level and 60% were illiterate. Hypertension was found in 54% of our patients, diabetes in 39% and cardiovascular disease in 10% of patients. Low Physical activity was associated with gender (OR=4.05), age (OR=1.03) and education level (OR=0.2). Conclusions: Our work has met the various pre-established objectives, however other more specific studies must be conducted to better characterize the profile of physical activity in chronic hemodialysis patients.

scholarly journals An Executive Approach to Achieve Mutual Exclusion in Distributed Data using Topology and Association Rule.

2014 ◽  
Vol 13 (6) ◽  
pp. 4537-4542
Author(s):  
Mr. Anurag Singh ◽  
Dr. Amod Tiwari
Keyword(s):  
Distributed System ◽  
Association Rule ◽  
Message Passing ◽  
Computer Network ◽  
Mutual Exclusion ◽  
Distributed Data ◽  
Worst Case ◽  
New Approach ◽  
And Topology ◽  
Minimum Number

In this paper, a new approach is being proposed to achieve mutual exclusion in distributed system using computer network and topology of nth nodes. In this executive approach nodes communicate among themselves using message passing technique. In this executive approach, distributed system with n nodes is logically partitioned into number of sub distributed system having only m½ nodes, where m is obtained by adding a minimum number in n to make it next perfect square number only if n is not a perfect square. Proposed algorithm is a Token based approach and achieves token optimally in 2 messages only for the best case and in worst case a node achieves token in n messages only.

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

2021 ◽  
Author(s):  
Roozbeh Manshaei
Keyword(s):  
Unique Solution ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Biological Systems ◽  
Network Reconstruction ◽  
Biological Data ◽  
Tangible Interaction ◽  
Survival Prediction ◽  
New Approach ◽  
Gene Regulatory

Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can informand reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.

scholarly journals Is it possible to perform a comprehensive analysis of the diagnostic data of mental diseases in the early stages?

2019 ◽  
pp. 77-81
Author(s):  
N. M. Zalutskaya ◽  
A. Eran ◽  
Sh. Freilikhman ◽  
R. Balicer ◽  
N. A. Gomzyakova ◽  
...  
Keyword(s):  
High Performance ◽  
Heterogeneous Data ◽  
Autism Spectrum ◽  
Biological Data ◽  
Multidimensional Data ◽  
New Approach ◽  
Spectrum Disorders ◽  
Effective Use ◽  
Goals And Objectives ◽  
Performance Computing

The work annotates the goals and objectives of the planned joint Russian-Israeli research project aimed at a comprehensive assessment of the data obtained during the examination of patients with mild cognitive decline and autism spectrum disorders. The process of their analysis will be based on complex methods, the effective use of which requires readily available means of operating with clinical and biological data, which, in turn, can be implemented through modern cloud and high-performance computing technologies. It is planned to use the new approach associated with the use of newSQL database as an API, and then use the distributed computing tools for working with heterogeneous data, which will lead to features in the analysis of correlations in multidimensional data arrays. For this purpose it is planned to use the methods of multidimensional statistical analysis and modern methods of machine learning.

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