Pub Date : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279754
B. Kuhn, Ray X. Lee, G. Stephens
Despite the long history and wide use of standard behavioral tests to measure emotion in laboratory animals, the approach and logic have been heavily criticized. Here we solved the fundamental logical and quantitative problems by a proof-of-principle testing approach with fine-scale behavioral analysis. The reported approach was able to detect informative behavioral details and further prove stress incubation after acute psychological trauma in mice. Standard analyses, in contrast, gave inconclusive results. This approach provides a technical advance allowing exploration of a wide range of potential measurements using standard behavioral tests, and a more solid basis supporting the concluded animal emotionality.
{"title":"Detection and identification of animal emotionality-exposing stress incubation in Mice","authors":"B. Kuhn, Ray X. Lee, G. Stephens","doi":"10.1109/ICIIBMS.2017.8279754","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279754","url":null,"abstract":"Despite the long history and wide use of standard behavioral tests to measure emotion in laboratory animals, the approach and logic have been heavily criticized. Here we solved the fundamental logical and quantitative problems by a proof-of-principle testing approach with fine-scale behavioral analysis. The reported approach was able to detect informative behavioral details and further prove stress incubation after acute psychological trauma in mice. Standard analyses, in contrast, gave inconclusive results. This approach provides a technical advance allowing exploration of a wide range of potential measurements using standard behavioral tests, and a more solid basis supporting the concluded animal emotionality.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128444806","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}
Pub Date : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279711
Hmway Hmway Tar, Zay Ya Zaw
This paper presented a novel approach for document clustering applying cloud technologies for the system's performance issue. It is critical for application service in cloud computing to provide precise information. Ontology servicing is one of the methods to deal with semantic ambiguity and information overload efficiently through appropriate semantic models and semantic technology. This system is the advanced and extended version of the system we have been published before. The experiments reveal that even the testing documents increased; the system may actually be able to produce useful result for text document clustering. In this paper, we propose a cloud service that exploits a novel ontology-based technique for identifying cloud service to improve the accuracy of cloud services searching. Our approach has the capability to perform cloud service concepts from cloud service sources. The main idea behind our method is cloud services using an ontology-based technique.
{"title":"Semantic based clustering system with cloud computing","authors":"Hmway Hmway Tar, Zay Ya Zaw","doi":"10.1109/ICIIBMS.2017.8279711","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279711","url":null,"abstract":"This paper presented a novel approach for document clustering applying cloud technologies for the system's performance issue. It is critical for application service in cloud computing to provide precise information. Ontology servicing is one of the methods to deal with semantic ambiguity and information overload efficiently through appropriate semantic models and semantic technology. This system is the advanced and extended version of the system we have been published before. The experiments reveal that even the testing documents increased; the system may actually be able to produce useful result for text document clustering. In this paper, we propose a cloud service that exploits a novel ontology-based technique for identifying cloud service to improve the accuracy of cloud services searching. Our approach has the capability to perform cloud service concepts from cloud service sources. The main idea behind our method is cloud services using an ontology-based technique.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134403199","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}
Pub Date : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279745
Wei Cai, Nansong Wu, F. Shi, Jialing Tong
Brain Machine Interface (BMI) is a spike sorting provide a connection between the external behavior and neural behavior of animals. Moreover, the spike sorting is significant for stability of the advanced application. To detect neuronal activity, multichannel recording is one of major methods. This paper proposed a software-hardware co-design framework with a 16- channel neural recording. Two-stage spike detection usually included a threshold method and a nonlinear energy operator (NEO). The spike clustering used the feature extraction. This multichannel spike sorting system algorithm were verified by simulations data and experiments results. The results presented a significant improvement on feature space during spike separation, due to the discrete derivative method.
{"title":"Software-hardware cosystem brain interface desig","authors":"Wei Cai, Nansong Wu, F. Shi, Jialing Tong","doi":"10.1109/ICIIBMS.2017.8279745","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279745","url":null,"abstract":"Brain Machine Interface (BMI) is a spike sorting provide a connection between the external behavior and neural behavior of animals. Moreover, the spike sorting is significant for stability of the advanced application. To detect neuronal activity, multichannel recording is one of major methods. This paper proposed a software-hardware co-design framework with a 16- channel neural recording. Two-stage spike detection usually included a threshold method and a nonlinear energy operator (NEO). The spike clustering used the feature extraction. This multichannel spike sorting system algorithm were verified by simulations data and experiments results. The results presented a significant improvement on feature space during spike separation, due to the discrete derivative method.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132704518","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}
Pub Date : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279731
Zeyang Yu, Wael Asaad, A. Nurmikko, I. Ozden
Parkinson's disease is a neurodegenerative disorder with severe motor deficits such as bradykinesia, muscle rigidity, tremor at rest and abnormal posture. From neurophysiological perspective, the most prominent feature of Parkinsonian pathophysiology is enhanced beta-band power (1340 Hz beta oscillations) in the local field potentials (LFPs) in motor cortex and in several basal ganglia nuclei. Currently, the most effective treatment for advanced Parkinson's disease is the electrical deep brain stimulation (eDBS) targeting at the subthalamic nucleus or internal globus pallidus, in which electrical current at about 125 Hz is continuously injected in to the target area. Even though eDBS significantly alleviates motor symptoms of the disease, it does not provide a complete cure. Therefore, there have been ongoing efforts to develop more effective brain stimulation paradigms, e.g. exploration of alternative areas for stimulation, or employment of advanced stimulation paradigms. A major obstacle against these efforts has been the ambiguities associated with electrical stimulation. Due to nonspecific nature of electrical stimulation and its incompatibility with simultaneous electrophysiology, it has been challenging to fine tune stimulation parameters and target specific neuronal groups or circuits with eDBS. In this work, we demonstrate optogenetics-based brain stimulation as a potential alternative to electrical brain stimulation in the treatment of Parkinson's disease. Optogenetics, with its cellular specificity and compatibility with electrophysiology, offers unique opportunities to monitor the neural activity while modulating the activity of targeted neuronal populations. In our study, we address two important premises for assessment of an optogenetics-based therapeutic brain stimulation paradigm: (1) validation of therapeutic value of precisely targeted deep brain optogenetic modulation; (2) demonstration of potential benefits of spatiotemporally patterned optogenetic stimulation of the motor cortex by characterizing the spatiotemporal dynamics of pathological cortical beta-band activity. In 6-OHDA-induced hemi-Parkinsonian rat model, we used excitatory opsins (ChR2 and C1V1) or inhibitory opsins (iC1C2 and NpHR) to excite or inhibit the subthalamic nucleus. Neural activity across motor cortex was recorded with microelectrode arrays (MEAs, 400μm electrode pitch) implanted unilaterally (6×6 MEA) or bilaterally (two 5×5 MEAs) into the anterior forelimb area of motor cortices. Recording/stimulation sessions were performed during free behavior or during behavioral assays (e.g. amphetamine-induced rotation and mobility test) to quantify and compare therapeutic efficacies of optogenetic stimulation and eDBS. The spatiotemporal dynamics of LFPs were examined with spectral, correlation, and coherence analyses. Our data confirmed the motor deficits such as akinesia and rotational bias in h-P rats. eDBS of subthalamic nucleus improved these motor deficits t
{"title":"Optogenetics-based neuromodulation for the treatment of Parkinson's disease","authors":"Zeyang Yu, Wael Asaad, A. Nurmikko, I. Ozden","doi":"10.1109/ICIIBMS.2017.8279731","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279731","url":null,"abstract":"Parkinson's disease is a neurodegenerative disorder with severe motor deficits such as bradykinesia, muscle rigidity, tremor at rest and abnormal posture. From neurophysiological perspective, the most prominent feature of Parkinsonian pathophysiology is enhanced beta-band power (1340 Hz beta oscillations) in the local field potentials (LFPs) in motor cortex and in several basal ganglia nuclei. Currently, the most effective treatment for advanced Parkinson's disease is the electrical deep brain stimulation (eDBS) targeting at the subthalamic nucleus or internal globus pallidus, in which electrical current at about 125 Hz is continuously injected in to the target area. Even though eDBS significantly alleviates motor symptoms of the disease, it does not provide a complete cure. Therefore, there have been ongoing efforts to develop more effective brain stimulation paradigms, e.g. exploration of alternative areas for stimulation, or employment of advanced stimulation paradigms. A major obstacle against these efforts has been the ambiguities associated with electrical stimulation. Due to nonspecific nature of electrical stimulation and its incompatibility with simultaneous electrophysiology, it has been challenging to fine tune stimulation parameters and target specific neuronal groups or circuits with eDBS. In this work, we demonstrate optogenetics-based brain stimulation as a potential alternative to electrical brain stimulation in the treatment of Parkinson's disease. Optogenetics, with its cellular specificity and compatibility with electrophysiology, offers unique opportunities to monitor the neural activity while modulating the activity of targeted neuronal populations. In our study, we address two important premises for assessment of an optogenetics-based therapeutic brain stimulation paradigm: (1) validation of therapeutic value of precisely targeted deep brain optogenetic modulation; (2) demonstration of potential benefits of spatiotemporally patterned optogenetic stimulation of the motor cortex by characterizing the spatiotemporal dynamics of pathological cortical beta-band activity. In 6-OHDA-induced hemi-Parkinsonian rat model, we used excitatory opsins (ChR2 and C1V1) or inhibitory opsins (iC1C2 and NpHR) to excite or inhibit the subthalamic nucleus. Neural activity across motor cortex was recorded with microelectrode arrays (MEAs, 400μm electrode pitch) implanted unilaterally (6×6 MEA) or bilaterally (two 5×5 MEAs) into the anterior forelimb area of motor cortices. Recording/stimulation sessions were performed during free behavior or during behavioral assays (e.g. amphetamine-induced rotation and mobility test) to quantify and compare therapeutic efficacies of optogenetic stimulation and eDBS. The spatiotemporal dynamics of LFPs were examined with spectral, correlation, and coherence analyses. Our data confirmed the motor deficits such as akinesia and rotational bias in h-P rats. eDBS of subthalamic nucleus improved these motor deficits t","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133478442","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}
Pub Date : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279690
Masakazu Higuchi, Shuji Shinohara, M. Nakamura, S. Mitsuyoshi, S. Tokuno, Y. Omiya, Naoki Hagiwara, Takeshi Takano
In stressful modern society, mental health care is one of important issues. The authors have been developing methods to assess mental health status by voice. Analysis using voice has benefits such as, noninvasive, not necessary any specialized device, easy use, and remote-able monitoring. We focused on the pattern of voice frequency during in daily life telephone calls, and developed the Mind Monitoring System (MIMOSYS) which is the smartphone application to monitor the mental health status by voice during telephone calls. MIMOSYS uses voice emotion recognition technology (ST: Sensibility Technology) and outputs “Vitality” which is the indicator to denote the health status immediately after the telephone call and “Mental Activity” which is the indicator to denote the mid-to long-term health status. Higher vitality and Mental Activity values represent a better mental health status. We expect that the user can avoid behavior mental condition due to inducing behavior change, for example depression state, by monitoring mental health status daily using MIMOSYS. When using MIMOSYS, it is desirable to avoid noise as much as possible during telephone calls because empirically at least 7 utterances or more are appropriate for calculating the vitality and it is difficult to correctly detect utterances if noise is contained in the voice. However, environmental sounds will be included when talking in a hands-free manner, and it may cause analyzed results of incorrect mental health status because of unreliable vitality. In this study, we investigate the impact of various noises on the mental health status output by our voice analysis method. We used the sound corpus CENSREC-1-C provided by Speech Resources Consortium in the experiment. This corpus consists of two kinds of data, the simulated data by the noise-addition and the recording data in real environments. One voice data is a numeric string vocalized with several intervals and includes nine or ten utterances. The simulated data includes eight kinds of noise, such as Subway, Babble, Car, Exhibition, Restaurant, Street, Airport and Station. In each noisy environment, noises at SNRs from 20dB to −5dB every 5 dB increments are artificially added to clean voice data without noise. The number of speaker is 104 in this data set. The real environmental data includes two real-noisy environments, such as the university restaurant and the vicinity of highway. In each real environment, there are two SNR conditions, the lower and higher SNR conditions. Furthermore, voice data was recorded with close microphone and remote microphone synchronously in real environments. The number of speaker is 10 in this data set, Voice analysis was performed for both voice data sets. We used only “vitality” for this research because the data have only one time point data. As a result for the simulated data, the mean of vitality values for voice data at SNR of 20dB was lower than it for clean voice data in each environment, and the me
在紧张的现代社会，心理健康是一个重要的问题。作者一直在开发通过声音评估心理健康状况的方法。使用语音进行分析具有非侵入性、不需要任何专门设备、易于使用和远程监控等优点。我们专注于日常生活中电话通话时的语音频率模式，开发了一款智能手机应用程序Mind Monitoring System (MIMOSYS)，通过语音来监测电话通话时的心理健康状况。MIMOSYS使用语音情感识别技术(ST: Sensibility technology)，输出“Vitality”(活力)和“Mental Activity”(精神活动)(精神活动)，这两个指标分别表示通话后立即的健康状态和中长期的健康状态。较高的活力值和心理活动量值代表较好的心理健康状况。我们期望通过每天使用MIMOSYS监测心理健康状况，用户可以避免由于诱导行为改变而导致的行为心理状况，例如抑郁状态。在使用MIMOSYS时，希望在通话过程中尽可能避免噪声，因为经验上至少有7个或更多的话语适合计算活力，如果声音中包含噪声，则难以正确检测话语。但是，免提通话时，会包含环境声音，并且由于生命力不可靠，可能会导致心理健康状况的分析结果不正确。在本研究中，我们用我们的声音分析方法来研究各种噪音对心理健康状态输出的影响。实验使用语音资源联盟提供的语音语料库CENSREC-1-C。该语料库由两类数据组成，一类是加噪后的模拟数据，另一类是真实环境下的记录数据。一个语音数据是由几个间隔发出的数字字符串，包括九个或十个语音。模拟数据包括地铁、嘈杂声、汽车、展览、餐厅、街道、机场和车站等8种噪声。在每个噪声环境中，人为添加信噪比为20dB ~ - 5dB的噪声，每增加5dB，以清洁无噪声的语音数据。在这个数据集中，说话人的数量是104。真实环境数据包括两种真实噪声环境，如大学餐厅和高速公路附近。在每个实际环境中，都存在两种信噪比条件:低信噪比条件和高信噪比条件。在真实环境中，用近端麦克风和远端麦克风同步录制语音数据。该数据集中的说话人数量为10，对两个语音数据集进行语音分析。由于数据只有一个时间点数据，所以我们只使用了“vitality”。结果表明，对于模拟数据，各环境中信噪比为20dB的语音数据的活力均值均低于清洁语音数据，噪声水平高于20dB的语音数据的活力均值在5个环境(汽车、餐厅、街道、机场和车站)中呈现出较高的趋势。结果表明，对于真实环境数据，在两种噪声环境和两种信噪比条件的任意组合下，远端麦克风记录的语音数据的活力值均值均高于近端麦克风记录的语音数据。另一方面，在模拟数据的所有环境中，对于干净的语音数据和信噪比为20dB的语音数据，语音分析检测到的话语几乎都能正确检测到，但在噪声水平大于20dB的情况下，检测精度明显较差。在实际环境数据中也显示出类似的趋势，即在两种噪声环境和两种信噪比条件的任意组合下，远程麦克风记录的语音数据的话语检测精度都明显较差。这意味着噪声部分被错误地识别为话语，并进行包括虚假话语在内的语音分析。因此，在噪声环境下，心理健康状况可能无法正确计算。从以上结果可以看出，信噪比在20dB左右的噪声环境是在一定噪声下分析的极限。此外，还研究了模拟数据中由ST计算的情绪变化。结果表明，在所有噪声环境下，信噪比为20dB的语音数据的快乐和悲伤分量均值均高于干净语音数据的均值，而信噪比为20dB的语音数据的愤怒分量均值均低于干净语音数据的均值。这说明除了语音检测外，噪声本身也会影响心理健康状况的分析结果。在未来，我们将提高噪声环境下的话语检测的准确性，并验证适当的噪声滤波器将提高心理健康状态分析的准确性。
{"title":"An effect of noise on mental health indicator using voice","authors":"Masakazu Higuchi, Shuji Shinohara, M. Nakamura, S. Mitsuyoshi, S. Tokuno, Y. Omiya, Naoki Hagiwara, Takeshi Takano","doi":"10.1109/ICIIBMS.2017.8279690","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279690","url":null,"abstract":"In stressful modern society, mental health care is one of important issues. The authors have been developing methods to assess mental health status by voice. Analysis using voice has benefits such as, noninvasive, not necessary any specialized device, easy use, and remote-able monitoring. We focused on the pattern of voice frequency during in daily life telephone calls, and developed the Mind Monitoring System (MIMOSYS) which is the smartphone application to monitor the mental health status by voice during telephone calls. MIMOSYS uses voice emotion recognition technology (ST: Sensibility Technology) and outputs “Vitality” which is the indicator to denote the health status immediately after the telephone call and “Mental Activity” which is the indicator to denote the mid-to long-term health status. Higher vitality and Mental Activity values represent a better mental health status. We expect that the user can avoid behavior mental condition due to inducing behavior change, for example depression state, by monitoring mental health status daily using MIMOSYS. When using MIMOSYS, it is desirable to avoid noise as much as possible during telephone calls because empirically at least 7 utterances or more are appropriate for calculating the vitality and it is difficult to correctly detect utterances if noise is contained in the voice. However, environmental sounds will be included when talking in a hands-free manner, and it may cause analyzed results of incorrect mental health status because of unreliable vitality. In this study, we investigate the impact of various noises on the mental health status output by our voice analysis method. We used the sound corpus CENSREC-1-C provided by Speech Resources Consortium in the experiment. This corpus consists of two kinds of data, the simulated data by the noise-addition and the recording data in real environments. One voice data is a numeric string vocalized with several intervals and includes nine or ten utterances. The simulated data includes eight kinds of noise, such as Subway, Babble, Car, Exhibition, Restaurant, Street, Airport and Station. In each noisy environment, noises at SNRs from 20dB to −5dB every 5 dB increments are artificially added to clean voice data without noise. The number of speaker is 104 in this data set. The real environmental data includes two real-noisy environments, such as the university restaurant and the vicinity of highway. In each real environment, there are two SNR conditions, the lower and higher SNR conditions. Furthermore, voice data was recorded with close microphone and remote microphone synchronously in real environments. The number of speaker is 10 in this data set, Voice analysis was performed for both voice data sets. We used only “vitality” for this research because the data have only one time point data. As a result for the simulated data, the mean of vitality values for voice data at SNR of 20dB was lower than it for clean voice data in each environment, and the me","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132698801","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}
Pub Date : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279749
Yoichi Murakami, K. Mizuguchi
A better understanding of biological processes, pathways and functions requires reliable information about protein-protein interactions (PPIs). However, it is still a difficult task to identify complete PPI-networks experimentally in a cell or organism. To supplement the limitations of current experimental techniques, we have proposed PSOPIA, a computational method to predict whether two proteins interact or not (http://mizuguchilab.org/PSOPIA/) [1]. The selection of datasets is a big issue for the PPI prediction [2, 3]. It is generally believed that increasing the size and diversity of examples makes the dataset more representative and reduces the noise effects; however, for many algorithms, it is impractical to use a large-scale dataset at the proteome level because of the memory and CPU time requirements. In this study, PSOPIA was retrained on a highly imbalanced large-scale dataset having a diverse set of examples at the proteome level. The dataset consisted of 43,060 high confidence direct physical PPIs obtained from TargetMine [4] (as positives being only 0.13% of the total) and 33,098,951 negative PPIs. As a result, the new prediction model achieved the higher AUC of 0.89 (pAUCfpr
{"title":"PSOPIA: Toward more reliable protein-protein interaction prediction from sequence information","authors":"Yoichi Murakami, K. Mizuguchi","doi":"10.1109/ICIIBMS.2017.8279749","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279749","url":null,"abstract":"A better understanding of biological processes, pathways and functions requires reliable information about protein-protein interactions (PPIs). However, it is still a difficult task to identify complete PPI-networks experimentally in a cell or organism. To supplement the limitations of current experimental techniques, we have proposed PSOPIA, a computational method to predict whether two proteins interact or not (http://mizuguchilab.org/PSOPIA/) [1]. The selection of datasets is a big issue for the PPI prediction [2, 3]. It is generally believed that increasing the size and diversity of examples makes the dataset more representative and reduces the noise effects; however, for many algorithms, it is impractical to use a large-scale dataset at the proteome level because of the memory and CPU time requirements. In this study, PSOPIA was retrained on a highly imbalanced large-scale dataset having a diverse set of examples at the proteome level. The dataset consisted of 43,060 high confidence direct physical PPIs obtained from TargetMine [4] (as positives being only 0.13% of the total) and 33,098,951 negative PPIs. As a result, the new prediction model achieved the higher AUC of 0.89 (pAUCfpr<o.5% = 0.24) than the previous model of PSOPIA. Furthermore, it was applied to the problem of filtering out protein pairs incorrectly determined as interacting (false positives) from a low-confidence human PPI dataset. Here, we suggest that a diverse set of large-scale examples is a key toward more reliable PPI prediction, demonstrating the performance of PSOPIA at the proteome level.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116192199","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}
Pub Date : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279735
Askarbek N. Orakov, N. Sakenova, Igor Goraynin, A. Sorokin
There is a great potential for the use of bacterial communities for bioremediation and waste treatment. Systems metabolic engineering for microbial community redesign is a promising approach. The successful development of synthetic microbial communities is essential to add value to the main challenges for sustainable development, such as difficulties to cultivate bacteria in isolation[1], diversity of microbial communities, and many uncharacterized bacteria. Some of these challenges could be addressed by advances in computational tools and modelling. However, current tools only provide separate taxonomic [2] and functional analyses [3]. Development of a metagenomic analysis tool to investigate metagenomes deeply with the help of integrated taxonomic, functional and metabolic pathways analyses may allow to make community modelling and study complex interspecific relations inside the community. This would help finding ways to increase the functional potential of microbial communities either by optimizing external conditions and nutrients or by regulating the taxonomic content and proportions of the bacterial composition.
{"title":"A novel computational method to analyse metagenome for understanding of microbial community composition and functional potential","authors":"Askarbek N. Orakov, N. Sakenova, Igor Goraynin, A. Sorokin","doi":"10.1109/ICIIBMS.2017.8279735","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279735","url":null,"abstract":"There is a great potential for the use of bacterial communities for bioremediation and waste treatment. Systems metabolic engineering for microbial community redesign is a promising approach. The successful development of synthetic microbial communities is essential to add value to the main challenges for sustainable development, such as difficulties to cultivate bacteria in isolation[1], diversity of microbial communities, and many uncharacterized bacteria. Some of these challenges could be addressed by advances in computational tools and modelling. However, current tools only provide separate taxonomic [2] and functional analyses [3]. Development of a metagenomic analysis tool to investigate metagenomes deeply with the help of integrated taxonomic, functional and metabolic pathways analyses may allow to make community modelling and study complex interspecific relations inside the community. This would help finding ways to increase the functional potential of microbial communities either by optimizing external conditions and nutrients or by regulating the taxonomic content and proportions of the bacterial composition.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125658910","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}
Pub Date : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279710
Yawei Hou, Huailin Zhao
Neural network and depth learning have been widely used in the field of image processing. Good recognition results are often required for complex network models. But the complex network model makes training difficult and takes a long time. In order to obtain a higher recognition rate with a simple model, the BP neural network and the convolutional neural network are studied separately and verified on the MNIST data set. In order to improve the recognition results further, a combined depth network is proposed and validated on the MNIST dataset. The experimental results show that the recognition effect of the combined depth network is obviously better than that of a single network. A more accurate recognition result is achieved by the combined network.
{"title":"Handwritten digit recognition based on depth neural network","authors":"Yawei Hou, Huailin Zhao","doi":"10.1109/ICIIBMS.2017.8279710","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279710","url":null,"abstract":"Neural network and depth learning have been widely used in the field of image processing. Good recognition results are often required for complex network models. But the complex network model makes training difficult and takes a long time. In order to obtain a higher recognition rate with a simple model, the BP neural network and the convolutional neural network are studied separately and verified on the MNIST data set. In order to improve the recognition results further, a combined depth network is proposed and validated on the MNIST dataset. The experimental results show that the recognition effect of the combined depth network is obviously better than that of a single network. A more accurate recognition result is achieved by the combined network.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126428403","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}
Pub Date : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279757
Bangwon Seo
In this paper, we consider spatial beamforming for massive multiple-input multiple-output (MIMO) systems in frequency division duplexing (FDD). In order to reduce channel state information (CSI) feedback overhead of users, near-by users that have similar statistical channel correlation are put into a group and channel correlation matrix based precoding scheme is proposed. More specifically, we propose to employ two-stage precoder at a base station (BS): Outer precoder and inner precoder. The former is designed to minimize inter-group interference by considering the statistical channel correlation. The latter is designed to minimize intra-group interference by utilizing modified statistical channel correlation and instantaneous channel state information (ICSI). The inner precoder is composed of three steps: precoded pilot signal transmission phase at the BS, effective channel gain calculation and feedback stage at the mobile user, and data transmission stage at the BS.
{"title":"Downlink statistical precoding scheme for spatially correlated massive MIMO systems","authors":"Bangwon Seo","doi":"10.1109/ICIIBMS.2017.8279757","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279757","url":null,"abstract":"In this paper, we consider spatial beamforming for massive multiple-input multiple-output (MIMO) systems in frequency division duplexing (FDD). In order to reduce channel state information (CSI) feedback overhead of users, near-by users that have similar statistical channel correlation are put into a group and channel correlation matrix based precoding scheme is proposed. More specifically, we propose to employ two-stage precoder at a base station (BS): Outer precoder and inner precoder. The former is designed to minimize inter-group interference by considering the statistical channel correlation. The latter is designed to minimize intra-group interference by utilizing modified statistical channel correlation and instantaneous channel state information (ICSI). The inner precoder is composed of three steps: precoded pilot signal transmission phase at the BS, effective channel gain calculation and feedback stage at the mobile user, and data transmission stage at the BS.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127750455","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}
Pub Date : 2017-10-06DOI: 10.4172/2155-9937.1000136
S. Vesnin, I. Goryanin
The temperature of a malignant tumour is a universal indicator of the growth rate of the tumor. Tumor temperature can be used as a prediction of the benefit of individual therapies and in monitoring the efficacy of breast cancer treatment. Provide systematic analysis of the data available in the current literature on the role of microwave thermometry in diagnosis of breast pathology, in assessing the effect of neoadjuvant therapy for breast cancer treatment, and in screening programs. Various aspects of the use of microwave thermometry in onco-mammology are described: the diagnostic value of the method, the possibilities in differential diagnosis of dyshormonal hyperplasias, benign and malignant tumors, the prognostic role of microwave, the possibility of using the method in assessing the effect of preoperative chemotherapy in locally advanced breast cancer. It is shown, that microwave thermometry is a highly informative method of early diagnosis of various breast pathologies and can be widely used in screening programs to form risk groups for subsequent diagnostics using traditional methods (ultrasound, x-ray mammography, breast MRI, morphology). Microwave thermometry helps to assess the effect of ongoing neoadjuvant therapy of the primary non-operative forms of breast cancer in order to correct the personalized treatment.
{"title":"Modern microwave thermometry for breast cancer","authors":"S. Vesnin, I. Goryanin","doi":"10.4172/2155-9937.1000136","DOIUrl":"https://doi.org/10.4172/2155-9937.1000136","url":null,"abstract":"The temperature of a malignant tumour is a universal indicator of the growth rate of the tumor. Tumor temperature can be used as a prediction of the benefit of individual therapies and in monitoring the efficacy of breast cancer treatment. Provide systematic analysis of the data available in the current literature on the role of microwave thermometry in diagnosis of breast pathology, in assessing the effect of neoadjuvant therapy for breast cancer treatment, and in screening programs. Various aspects of the use of microwave thermometry in onco-mammology are described: the diagnostic value of the method, the possibilities in differential diagnosis of dyshormonal hyperplasias, benign and malignant tumors, the prognostic role of microwave, the possibility of using the method in assessing the effect of preoperative chemotherapy in locally advanced breast cancer. It is shown, that microwave thermometry is a highly informative method of early diagnosis of various breast pathologies and can be widely used in screening programs to form risk groups for subsequent diagnostics using traditional methods (ultrasound, x-ray mammography, breast MRI, morphology). Microwave thermometry helps to assess the effect of ongoing neoadjuvant therapy of the primary non-operative forms of breast cancer in order to correct the personalized treatment.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"642 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117096991","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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