{"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":null,"pages":null},"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}
Lymphomas and neuroblastoma are the two commonly diagnosed cancers in childhood. Despite extensive studies in cytogenetic and mutations in childhood cancers, new diagnoses were still reported. This is due to multifactorial characteristic of the diseases and a lack of systematic-level information on genetic interaction and pathways between markers to understand the development and progression of the diseases. This study aims to sought a system biology approach to infer gene regulatory networks for lymphomas and neuroblastoma. Results demonstrated that our approach simplifies complex genetic interactions without losing important biological information of the genes. Candidate markers that infer lymphoma and neuroblastoma and their relevant pathways were identified.
{"title":"A Systems Biology Approach to Model Gene-Gene Interaction for Childhood Sarcomas","authors":"Dong-Ling Tong, C. Lee","doi":"10.1109/BIBE.2018.00074","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00074","url":null,"abstract":"Lymphomas and neuroblastoma are the two commonly diagnosed cancers in childhood. Despite extensive studies in cytogenetic and mutations in childhood cancers, new diagnoses were still reported. This is due to multifactorial characteristic of the diseases and a lack of systematic-level information on genetic interaction and pathways between markers to understand the development and progression of the diseases. This study aims to sought a system biology approach to infer gene regulatory networks for lymphomas and neuroblastoma. Results demonstrated that our approach simplifies complex genetic interactions without losing important biological information of the genes. Candidate markers that infer lymphoma and neuroblastoma and their relevant pathways were identified.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114929086","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":null,"pages":null},"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}
{"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":null,"pages":null},"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}
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":null,"pages":null},"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}
For positron emission tomography (PET), successive approximation register (SAR) analog-to-digital converter (ADC) and switched-capacitor (SC) low-pass filter implemented in tsmc 0.18-um CMOS process is presented. To reduce DAC switching energy and layout size, a hybrid resistor-capacitor DAC is applied. To save energy, asynchronous control logic to drive the ADC is used. A pre-amplifier based comparator circuit is built to reduce the kickback noise from the dynamic latch. The proposed filter uses cascades of first-order and second-order biquad seting blocks. In order to reach the largest possible input dynamic range, the method of dynamic range scaling and minimum capacitor scaling is used.
针对正电子发射断层扫描(PET)，提出了在台积电0.18 um CMOS工艺中实现的逐次逼近寄存器(SAR)模数转换器(ADC)和开关电容(SC)低通滤波器。为了降低DAC的开关能量和减小电路布局尺寸，采用了电阻-电容混合DAC。为了节省能量，采用异步控制逻辑驱动ADC。为了减小动态锁存器的反扰噪声，设计了基于前置放大器的比较器电路。所提出的滤波器使用一阶和二阶二元设置块的级联。为了达到最大的输入动态范围，采用了动态范围缩放和最小电容缩放的方法。
{"title":"SAR ADC with DAC and SC Low-Pass Filter for Positron Emission Tomography Application","authors":"W. Lai","doi":"10.1109/BIBE.2018.00024","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00024","url":null,"abstract":"For positron emission tomography (PET), successive approximation register (SAR) analog-to-digital converter (ADC) and switched-capacitor (SC) low-pass filter implemented in tsmc 0.18-um CMOS process is presented. To reduce DAC switching energy and layout size, a hybrid resistor-capacitor DAC is applied. To save energy, asynchronous control logic to drive the ADC is used. A pre-amplifier based comparator circuit is built to reduce the kickback noise from the dynamic latch. The proposed filter uses cascades of first-order and second-order biquad seting blocks. In order to reach the largest possible input dynamic range, the method of dynamic range scaling and minimum capacitor scaling is used.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130183693","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":null,"pages":null},"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}
Lysine malonylation is a newly discovered post-translational modification of proteins, which plays an important role in regulating many cellular functions. Several approaches are available to identify malonylation proteins and its malonylation sites, however; experimental identification of malonylation sites is often laborious and costly. Therefore, computational schemes are needed to identify potential malonylation sites prior to in vitro experimentation. In this paper, a novel computational scheme iLMS (Identification of Lysine-Malonylation Sites) has been developed by combining primary sequences and evolutionary features via a support vector machine classifier. The final iLMS scheme achieved a robust performance in cross-validation test in both human and mouse datasets. For the mouse data, the iLMS predictor outperformed other existing implementations. The iLMS is a promising computational scheme for the prediction of malonylation sites.
{"title":"iLMS, Computational Identification of Lysine-Malonylation Sites by Combining Multiple Sequence Features","authors":"M. Hasan, H. Kurata","doi":"10.1109/BIBE.2018.00077","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00077","url":null,"abstract":"Lysine malonylation is a newly discovered post-translational modification of proteins, which plays an important role in regulating many cellular functions. Several approaches are available to identify malonylation proteins and its malonylation sites, however; experimental identification of malonylation sites is often laborious and costly. Therefore, computational schemes are needed to identify potential malonylation sites prior to in vitro experimentation. In this paper, a novel computational scheme iLMS (Identification of Lysine-Malonylation Sites) has been developed by combining primary sequences and evolutionary features via a support vector machine classifier. The final iLMS scheme achieved a robust performance in cross-validation test in both human and mouse datasets. For the mouse data, the iLMS predictor outperformed other existing implementations. The iLMS is a promising computational scheme for the prediction of malonylation sites.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129749336","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":null,"pages":null},"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}
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":null,"pages":null},"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}
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