{"title":"Publisher's Information","authors":"","doi":"10.1109/bibe.2018.00082","DOIUrl":"https://doi.org/10.1109/bibe.2018.00082","url":null,"abstract":"","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125330308","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}
Precision medicine information retrieval (PM IR) is about matching the most relevant scientific articles to an individual patient for reliable disease treatment. To achieve effectiveness and efficiency, the task usually consists of two stages: conventional information retrieval and reranking. Many approaches have been proposed for reranking. However, the performance is still far from satisfactory. In this work, we propose a regression-based reranking scheme for PM IR which uses labelled data regardless of empirical knowledge from similar but not identical documents set. Experiments validate that the performance of our approach is significantly better than that of the state-of-the-art approaches.
{"title":"Regression-Based Documents Reranking for Precision Medicine","authors":"Juncheng Ding, Wei Jin, Haihua Chen","doi":"10.1109/BIBE.2018.00062","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00062","url":null,"abstract":"Precision medicine information retrieval (PM IR) is about matching the most relevant scientific articles to an individual patient for reliable disease treatment. To achieve effectiveness and efficiency, the task usually consists of two stages: conventional information retrieval and reranking. Many approaches have been proposed for reranking. However, the performance is still far from satisfactory. In this work, we propose a regression-based reranking scheme for PM IR which uses labelled data regardless of empirical knowledge from similar but not identical documents set. Experiments validate that the performance of our approach is significantly better than that of the state-of-the-art approaches.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125616808","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}
The model of gene-gene interaction contributing to the biological insight of disease pathology have received significant attention from both medical and computing communities. Through the modeled interactome map, the biological significant of the mutated genes can be revealed and treatments targeting these genes can be taken to prevent further proliferation of the mutated genes. In this paper we propose a novel computational way to interrogate interaction between genes. We utilize centroid computation in the hybrid genetic algorithm and neural network to model interaction between leukemia-related genes. Results indicated the effectiveness of centroid value in detecting significant interactions of gene. Hub genes were also identified.
{"title":"Pathway Analysis of Marker Genes for Leukemia Cancer using Enhanced Genetic Algorithm-Neural Network (enGANN)","authors":"Hau Cherng Wong, C. Lee, Dong-Ling Tong","doi":"10.1109/BIBE.2018.00029","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00029","url":null,"abstract":"The model of gene-gene interaction contributing to the biological insight of disease pathology have received significant attention from both medical and computing communities. Through the modeled interactome map, the biological significant of the mutated genes can be revealed and treatments targeting these genes can be taken to prevent further proliferation of the mutated genes. In this paper we propose a novel computational way to interrogate interaction between genes. We utilize centroid computation in the hybrid genetic algorithm and neural network to model interaction between leukemia-related genes. Results indicated the effectiveness of centroid value in detecting significant interactions of gene. Hub genes were also identified.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128731504","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":"[Title page iii]","authors":"","doi":"10.1109/bibe.2018.00002","DOIUrl":"https://doi.org/10.1109/bibe.2018.00002","url":null,"abstract":"","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131918733","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}
Teppei Matsubara, T. Ochiai, M. Hayashida, T. Akutsu, J. Nacher
Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks to 'omics' data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a convolutional neural network (CNN) approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. Data and CNN code can be downloaded from the link: https://sites.google.com/site/nacherlab/analysis
{"title":"Convolutional Neural Network Approach to Lung Cancer Classification Integrating Protein Interaction Network and Gene Expression Profiles","authors":"Teppei Matsubara, T. Ochiai, M. Hayashida, T. Akutsu, J. Nacher","doi":"10.1109/BIBE.2018.00036","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00036","url":null,"abstract":"Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks to 'omics' data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a convolutional neural network (CNN) approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. Data and CNN code can be downloaded from the link: https://sites.google.com/site/nacherlab/analysis","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122648414","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}
DNA barcoding is widely used in fields, such as taxonomy and species identification. Conventional DNA barcoding sequences employ uninformative or repeat nucleotides in known groups of taxa within a monophylum. Herein, we propose a decision theory-based DNA barcode that tests for the ribulose bisphosphate carboxylase gene (rbcL). The proposed method can generate shorter DNA barcodes called single nucleotide polymorphism (SNP) tags, which shorten rbcL sequences from their full length (400–654 bp) to 25-bp DNA tags. These DNA tags are then represented by quick response (QR) codes containing the species names, accession numbers, and DNA tag sequences. Our proposed method can efficiently reduce data storage and provide DNA barcoding for various plant species.
{"title":"[Regular Paper] Decision Theory-Based DNA Barcoding Through Quick Response Code Representation","authors":"Cheng-Hong Yang, Kuo-Chuan Wu, Hsueh-Wei Chang, Li-Yeh Chuang","doi":"10.1109/BIBE.2018.00051","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00051","url":null,"abstract":"DNA barcoding is widely used in fields, such as taxonomy and species identification. Conventional DNA barcoding sequences employ uninformative or repeat nucleotides in known groups of taxa within a monophylum. Herein, we propose a decision theory-based DNA barcode that tests for the ribulose bisphosphate carboxylase gene (rbcL). The proposed method can generate shorter DNA barcodes called single nucleotide polymorphism (SNP) tags, which shorten rbcL sequences from their full length (400–654 bp) to 25-bp DNA tags. These DNA tags are then represented by quick response (QR) codes containing the species names, accession numbers, and DNA tag sequences. Our proposed method can efficiently reduce data storage and provide DNA barcoding for various plant species.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122448624","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}
A nonlinear single-slope ADC with SOC integrated local contrast stretch using a configurable multi-frequency counter for bio-microfluidic imaging is presented in this paper. Compared with the conventional off-chip global contrast stretching algorithm, this method does not degrade image quality at the interested light intensity range (cell) at the cost of unconsidered range (sheath fluid) and can be integrated into CMOS image sensor directly. Meanwhile, this method provides higher precision of cell image for the later super-resolution reconstruction. The simulation results indicate that more details of cell image can be obtained in this method.
{"title":"Nonlinear CMOS Image Sensor with SOC Integrated Local Contrast Stretch for Bio-Microfluidic Imaging","authors":"Nan Lyu, LiKang Xu, N. Yu, Hejiu Zhang","doi":"10.1109/BIBE.2018.00050","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00050","url":null,"abstract":"A nonlinear single-slope ADC with SOC integrated local contrast stretch using a configurable multi-frequency counter for bio-microfluidic imaging is presented in this paper. Compared with the conventional off-chip global contrast stretching algorithm, this method does not degrade image quality at the interested light intensity range (cell) at the cost of unconsidered range (sheath fluid) and can be integrated into CMOS image sensor directly. Meanwhile, this method provides higher precision of cell image for the later super-resolution reconstruction. The simulation results indicate that more details of cell image can be obtained in this method.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"30 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113942678","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}
We performed subjective physiological assessment of brain activity using the visually performed n-back task and the n-back task performed by the auditory sense. The visually performed n-back task was done with two tasks that were performed while memorizing presented numbers and the result of computational problems. We characterized and compared the oxygenated hemoglobin concentration change in the brain during the working memory task using near-infrared spectroscopy measurement. Changes in activation of brain activity were observed due to differences in tasks. The difference in the presentation method resulted in a difference in activation of brain activity. Furthermore, the computational n-back task with execution function in working memory induced more brain activity than the usual n-back task. Thus, the computed n-back task is a suitable task to train workers.
{"title":"Using NIRS to Detect Brain oxyHb Changes During Short-Term Memory Tasks","authors":"Takuya Sasabe, H. Hagiwara","doi":"10.1109/BIBE.2018.00067","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00067","url":null,"abstract":"We performed subjective physiological assessment of brain activity using the visually performed n-back task and the n-back task performed by the auditory sense. The visually performed n-back task was done with two tasks that were performed while memorizing presented numbers and the result of computational problems. We characterized and compared the oxygenated hemoglobin concentration change in the brain during the working memory task using near-infrared spectroscopy measurement. Changes in activation of brain activity were observed due to differences in tasks. The difference in the presentation method resulted in a difference in activation of brain activity. Furthermore, the computational n-back task with execution function in working memory induced more brain activity than the usual n-back task. Thus, the computed n-back task is a suitable task to train workers.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117239126","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}
Ho Seon Choi, Myounghoon Shim, Chang Hee Lee, Y. Baek
CoP(Center of pressure) and GRF(ground reaction force) of insole are very important values in biomechanics area. They are using for calculating kinematics, dynamics of human or controlling of robot like exoskeletons. As an alternative to high-cost insole pressure sensors that can measure the insole pressure distribution and calculate the center of pressure, a FSR (Force Sensing Resistor) foot sensor with FSR sensors on the bottom of the insole was developed. However, the value of the CoP calculated using fixed coordinates and the values of FSR sensors were not sufficiently accurate and FSR sensors cannot cover the whole area of the insole so it can not calculate the magnitude of GRF. Hence, in this paper, a model capable of estimating of GRF and calibrating CoP measured by FSR foot sensors using neural network fitting is introduced. These processes rely on the fact that foot has protruding areas that are initially in contact with the ground while walking, with the size and magnitude of the pressure exerted by other non-protruding areas estimated using the the constant patterns of the pressure values of the protruding areas. This paper presents the division of the insole based on anatomical shape of foot, estimations of appropriate numvers and locations of the FSR sensors, creation of virtual forces and their floating coordinates, development of algorithms with neural network fitting for estimating the values, and calculation of the estimated GRF and calibrated CoP. Validation is conducted by comparing the Values with those of F-Scan System(Tekscan, Inc.)
{"title":"Estimating GRF(Ground Reaction Force) and Calibrating CoP(Center of Pressure) of an Insole Measured by an Low-Cost Sensor with Neural Network","authors":"Ho Seon Choi, Myounghoon Shim, Chang Hee Lee, Y. Baek","doi":"10.1109/BIBE.2018.00043","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00043","url":null,"abstract":"CoP(Center of pressure) and GRF(ground reaction force) of insole are very important values in biomechanics area. They are using for calculating kinematics, dynamics of human or controlling of robot like exoskeletons. As an alternative to high-cost insole pressure sensors that can measure the insole pressure distribution and calculate the center of pressure, a FSR (Force Sensing Resistor) foot sensor with FSR sensors on the bottom of the insole was developed. However, the value of the CoP calculated using fixed coordinates and the values of FSR sensors were not sufficiently accurate and FSR sensors cannot cover the whole area of the insole so it can not calculate the magnitude of GRF. Hence, in this paper, a model capable of estimating of GRF and calibrating CoP measured by FSR foot sensors using neural network fitting is introduced. These processes rely on the fact that foot has protruding areas that are initially in contact with the ground while walking, with the size and magnitude of the pressure exerted by other non-protruding areas estimated using the the constant patterns of the pressure values of the protruding areas. This paper presents the division of the insole based on anatomical shape of foot, estimations of appropriate numvers and locations of the FSR sensors, creation of virtual forces and their floating coordinates, development of algorithms with neural network fitting for estimating the values, and calculation of the estimated GRF and calibrated CoP. Validation is conducted by comparing the Values with those of F-Scan System(Tekscan, Inc.)","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133124490","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}
A. Nunes, A. Ambrósio, M. Castelo‐Branco, Rui Bernardes
In this paper, we imaged the retina of wild-type and the triple-transgenic mouse model of Alzheimer’s disease (3xTg- AD) with optical coherence tomography to assess changes in the retinal tissue associated with the Alzheimer’s disease. Texture analysis allowed to identify differences between groups at the age of four months, and to find biomarkers of disease progression. Furthermore, our findings suggest that specific layers of the retina may play a fundamental role in the assessment of early changes associated with the Alzheimer’s disease.
{"title":"[Regular Paper] Texture Biomarkers of Alzheimer's Disease and Disease Progression in the Mouse Retina","authors":"A. Nunes, A. Ambrósio, M. Castelo‐Branco, Rui Bernardes","doi":"10.1109/BIBE.2018.00016","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00016","url":null,"abstract":"In this paper, we imaged the retina of wild-type and the triple-transgenic mouse model of Alzheimer’s disease (3xTg- AD) with optical coherence tomography to assess changes in the retinal tissue associated with the Alzheimer’s disease. Texture analysis allowed to identify differences between groups at the age of four months, and to find biomarkers of disease progression. Furthermore, our findings suggest that specific layers of the retina may play a fundamental role in the assessment of early changes associated with the Alzheimer’s disease.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130775851","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}
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