Pub Date : 2021-10-13DOI: 10.1109/CIBCB49929.2021.9562885
D. Ashlock, J. A. Brown, W. Ashlock, Michael Dubé
This study compares a current representation for evolving networks to model epidemic spread with a novel representation also studied in a companion paper. This study applies a powerful diversity-friendly algorithm called ring optimization to this novel representation. The problem addressed is that the baseline method is found to optimize only locally; use of the novel representation improves the situation, but not much. The use of ring optimization yields similar or better performance for the ability of the evolved networks to model epidemics while substantially increasing the diversity of those networks.
{"title":"Ring Optimization of Epidemic Contact Networks","authors":"D. Ashlock, J. A. Brown, W. Ashlock, Michael Dubé","doi":"10.1109/CIBCB49929.2021.9562885","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562885","url":null,"abstract":"This study compares a current representation for evolving networks to model epidemic spread with a novel representation also studied in a companion paper. This study applies a powerful diversity-friendly algorithm called ring optimization to this novel representation. The problem addressed is that the baseline method is found to optimize only locally; use of the novel representation improves the situation, but not much. The use of ring optimization yields similar or better performance for the ability of the evolved networks to model epidemics while substantially increasing the diversity of those networks.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114268002","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 : 2021-10-13DOI: 10.1109/CIBCB49929.2021.9562847
D. Ashlock, Michael Dubé
Recent work in representation has developed small, evolvable structures called a complex string generator that generate infinite, aperiodic strings of characters. Such a string can be sectioned to provide an arbitrary list of parameters of indefinite length. Other work in evolving networks to model disease transmission has an issue common in many high-dimensional problems, evolution is less efficient when it must get a large number of parameter values correct. Specifying many parameters with a small evolvable object is a potential solution to this problem. In this study we compare three different implementations of representations, two of which employ complex string generators, to specify social contact graphs that plausibly explain the pattern of infection in a small epidemic. Representations that edit a starting network are found to have results that clump in network space while evolving the adjacency matrix provides increased diversity: none of the representations overlap in their results. The adjacency matrix based representation also generated outliers that outperform a baseline representation, probably because of its enhance diversity of solutions.
{"title":"A Comparison of Novel Representations for Evolving Epidemic Networks","authors":"D. Ashlock, Michael Dubé","doi":"10.1109/CIBCB49929.2021.9562847","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562847","url":null,"abstract":"Recent work in representation has developed small, evolvable structures called a complex string generator that generate infinite, aperiodic strings of characters. Such a string can be sectioned to provide an arbitrary list of parameters of indefinite length. Other work in evolving networks to model disease transmission has an issue common in many high-dimensional problems, evolution is less efficient when it must get a large number of parameter values correct. Specifying many parameters with a small evolvable object is a potential solution to this problem. In this study we compare three different implementations of representations, two of which employ complex string generators, to specify social contact graphs that plausibly explain the pattern of infection in a small epidemic. Representations that edit a starting network are found to have results that clump in network space while evolving the adjacency matrix provides increased diversity: none of the representations overlap in their results. The adjacency matrix based representation also generated outliers that outperform a baseline representation, probably because of its enhance diversity of solutions.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114497872","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 : 2021-10-13DOI: 10.1109/CIBCB49929.2021.9562805
T. Malepathirana, Damith A. Senanayake, V. Gautam, S. Halgamuge
Contemporary single-cell technologies produce data with a vast number of variables at a rapid pace, making large volumes of high-dimensional data available. The exploratory analysis of such high dimensional data can be aided by intuitive low dimensional visualizations. In this work, we investigate how both discrete and continuous structures in single cell data can be captured using the recently proposed dimensionality reduction method SONG, and compare the results with commonly used methods UMAP and PHATE. Using simulated and real-world datasets, we observed that SONG preserves a variety of patterns including discrete clusters, continuums, and branching structures. More importantly, SONG produced more/equally insightful visualizations compared to UMAP and PHATE in all considered datasets. We also quantitatively validate the high-dimensional pairwise distance preservation ability of these visualization methods in the low dimensional space for the generated visualizations.
{"title":"Robustness of Visualization Methods in Preserving the Continuous and Discrete Latent Structures of High-Dimensional Single-Cell Data","authors":"T. Malepathirana, Damith A. Senanayake, V. Gautam, S. Halgamuge","doi":"10.1109/CIBCB49929.2021.9562805","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562805","url":null,"abstract":"Contemporary single-cell technologies produce data with a vast number of variables at a rapid pace, making large volumes of high-dimensional data available. The exploratory analysis of such high dimensional data can be aided by intuitive low dimensional visualizations. In this work, we investigate how both discrete and continuous structures in single cell data can be captured using the recently proposed dimensionality reduction method SONG, and compare the results with commonly used methods UMAP and PHATE. Using simulated and real-world datasets, we observed that SONG preserves a variety of patterns including discrete clusters, continuums, and branching structures. More importantly, SONG produced more/equally insightful visualizations compared to UMAP and PHATE in all considered datasets. We also quantitatively validate the high-dimensional pairwise distance preservation ability of these visualization methods in the low dimensional space for the generated visualizations.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121431387","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 : 2021-08-06DOI: 10.26434/chemrxiv.14498688.v1
Shunya Sugita, M. Ohue
In the pursuit of research and development of drug discovery, the computational prediction of the target affinity of a drug candidate is useful for screening compounds at an early stage and for verifying the binding potential to an unknown target. The chemogenomics-based method has attracted increased attention as it integrates information pertaining to the drug and target to predict drug-target affinity (DTA). However, the compound and target spaces are vast, and without sufficient training data, proper DTA prediction is not possible. If a DTA prediction is made in this situation, it will potentially lead to false predictions. In this study, we propose a DTA prediction method that can advise whether/when there are insufficient samples in the compound/target spaces based on the concept of the applicability domain (AD) and the data density of the training dataset. AD indicates a data region in which a machine learning model can make reliable predictions. By preclassifying the samples to be predicted by the constructed AD into those within (In-AD) and those outside the AD (Out-AD), we can determine whether a reasonable prediction can be made for these samples. The results of the evaluation experiments based on the use of three different public datasets showed that the AD constructed by the k-nearest neighbor (k-NN) method worked well, i.e., the prediction accuracy of the samples classified by the AD as Out-AD was low, while the prediction accuracy of the samples classified by the AD as In-AD was high.
{"title":"Drug-target affinity prediction using applicability domain based on data density","authors":"Shunya Sugita, M. Ohue","doi":"10.26434/chemrxiv.14498688.v1","DOIUrl":"https://doi.org/10.26434/chemrxiv.14498688.v1","url":null,"abstract":"In the pursuit of research and development of drug discovery, the computational prediction of the target affinity of a drug candidate is useful for screening compounds at an early stage and for verifying the binding potential to an unknown target. The chemogenomics-based method has attracted increased attention as it integrates information pertaining to the drug and target to predict drug-target affinity (DTA). However, the compound and target spaces are vast, and without sufficient training data, proper DTA prediction is not possible. If a DTA prediction is made in this situation, it will potentially lead to false predictions. In this study, we propose a DTA prediction method that can advise whether/when there are insufficient samples in the compound/target spaces based on the concept of the applicability domain (AD) and the data density of the training dataset. AD indicates a data region in which a machine learning model can make reliable predictions. By preclassifying the samples to be predicted by the constructed AD into those within (In-AD) and those outside the AD (Out-AD), we can determine whether a reasonable prediction can be made for these samples. The results of the evaluation experiments based on the use of three different public datasets showed that the AD constructed by the k-nearest neighbor (k-NN) method worked well, i.e., the prediction accuracy of the samples classified by the AD as Out-AD was low, while the prediction accuracy of the samples classified by the AD as In-AD was high.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130712141","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 : 2021-05-17DOI: 10.1109/CIBCB49929.2021.9562821
Alberto Zancanaro, Giulia Cisotto, J. Paulo, G. Pires, U. Nunes
The accurate detection of motor imagery (MI) from electroencephalography (EEG) is a fundamental, as well as challenging, task to provide reliable control of robotic devices to support people suffering from neuro-motor impairments, e.g., in brain-computer interface (BCI) applications. Recently, deep learning approaches have been able to extract subject-independent features from EEG, to cope with its poor SNR and high intra-subject and cross-subject variability. In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance. Second, we propose DynamicNet, a Python-based tool for quick and flexible implementations of deep learning models based on convolutional neural networks. We showcase the potentiality of DynamicNet by implementing EEGNet, a well-established architecture for effective EEG classification. Finally, we compare its performance with the filter bank common spatial pattern (FBCSP) in a 4-class MI task (data from a public dataset). To infer cross-subject classification performance, we applied three different cross-validation schemes. From our results, we show that EEGNet implemented with DynamicNet outperforms FBCSP by about 25 %, with a statistically significant difference when cross-subject validation schemes are applied. We conclude that deep learning approaches might be particularly helpful to provide higher cross-subject classification performance in multiclass MI classification scenarios. In the future, it is expected to improve DynamicNet to implement new architectures to further investigate cross-subject classification of MI tasks in real-world scenarios.
{"title":"CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From the State-of-The-Art to DynamicNet","authors":"Alberto Zancanaro, Giulia Cisotto, J. Paulo, G. Pires, U. Nunes","doi":"10.1109/CIBCB49929.2021.9562821","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562821","url":null,"abstract":"The accurate detection of motor imagery (MI) from electroencephalography (EEG) is a fundamental, as well as challenging, task to provide reliable control of robotic devices to support people suffering from neuro-motor impairments, e.g., in brain-computer interface (BCI) applications. Recently, deep learning approaches have been able to extract subject-independent features from EEG, to cope with its poor SNR and high intra-subject and cross-subject variability. In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance. Second, we propose DynamicNet, a Python-based tool for quick and flexible implementations of deep learning models based on convolutional neural networks. We showcase the potentiality of DynamicNet by implementing EEGNet, a well-established architecture for effective EEG classification. Finally, we compare its performance with the filter bank common spatial pattern (FBCSP) in a 4-class MI task (data from a public dataset). To infer cross-subject classification performance, we applied three different cross-validation schemes. From our results, we show that EEGNet implemented with DynamicNet outperforms FBCSP by about 25 %, with a statistically significant difference when cross-subject validation schemes are applied. We conclude that deep learning approaches might be particularly helpful to provide higher cross-subject classification performance in multiclass MI classification scenarios. In the future, it is expected to improve DynamicNet to implement new architectures to further investigate cross-subject classification of MI tasks in real-world scenarios.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122333710","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 : 2021-04-28DOI: 10.26434/CHEMRXIV.14489769.V1
Takatsugi Kosugi, M. Ohue
The quantification of drug-likeness is very useful for screening drug candidates. The quantitative estimate of drug-likeness (QED) is the most commonly used quantitative drug efficacy assessment method proposed by Bickerton et al. However, QED is not considered suitable for screening compounds that target protein-protein interactions (PPI), which have garnered significant interest in recent years. Therefore, we developed a method called the quantitative estimate of protein-protein interaction targeting drug-likeness (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs and developed using the QED concept, involving modeling physicochemical properties based on the information available on the drug. QEPPI models the physicochemical properties of compounds that have been reported in the literature to act on PPIs. Compounds in iPPI-DB, which comprises PPI inhibitors and stabilizers, and FDA-approved drugs were evaluated using QEPPI. The results showed that QEPPI is more suitable for the early screening of PPI-targeting compounds than QED. QEPPI was also considered an extended concept of “Rule-of-Four” (RO4), a PPI inhibitor index proposed by Morelli et al. We have been able to turn a discrete value indicator into a continuous value indicator. To compare the discriminatory performance of QEPPI and RO4, we evaluated their discriminatory performance using the datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. Results of the F-score of RO4 and QEPPI were 0.446 and 0.499, respectively. QEPPI demonstrated better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it could be used as an initial filter for efficient screening of PPI-targeting compounds, which has been difficult in the past.
药物相似度的量化是筛选候选药物的重要手段。定量估计药物相似度(quantitative estimate of drug-likeness, QED)是Bickerton等人提出的最常用的药物疗效定量评价方法。然而，QED被认为不适合筛选靶向蛋白-蛋白相互作用(PPI)的化合物，这在近年来已经引起了很大的兴趣。因此，我们开发了一种称为定量估计蛋白质-蛋白质相互作用靶向药物相似性(QEPPI)的方法，专门用于早期筛选靶向ppi的化合物。QEPPI是针对ppi靶向药物的QED方法的扩展，并使用QED概念开发，涉及基于药物可用信息的物理化学性质建模。QEPPI模拟了文献中报道的作用于ppi的化合物的物理化学性质。使用QEPPI对iPPI-DB中的化合物(包括PPI抑制剂和稳定剂)和fda批准的药物进行了评估。结果表明，QEPPI比QED更适合于ppi靶向化合物的早期筛选。QEPPI也被认为是Morelli等人提出的PPI抑制剂指数“Rule-of-Four”(RO4)概念的扩展。我们已经能够把一个离散值指标变成一个连续值指标。为了比较QEPPI和RO4的区分性能，我们使用ppi靶点化合物和fda批准药物的数据集，使用F-score等指标评估它们的区分性能。RO4和QEPPI的f得分分别为0.446和0.499。QEPPI表现出更好的性能，并为早期PPI药物发现提供了药物相似性的量化。因此，它可以作为有效筛选ppi靶向化合物的初始过滤器，这在过去是很困难的。
{"title":"Quantitative Estimate of Protein-Protein Interaction Targeting Drug-likeness","authors":"Takatsugi Kosugi, M. Ohue","doi":"10.26434/CHEMRXIV.14489769.V1","DOIUrl":"https://doi.org/10.26434/CHEMRXIV.14489769.V1","url":null,"abstract":"The quantification of drug-likeness is very useful for screening drug candidates. The quantitative estimate of drug-likeness (QED) is the most commonly used quantitative drug efficacy assessment method proposed by Bickerton et al. However, QED is not considered suitable for screening compounds that target protein-protein interactions (PPI), which have garnered significant interest in recent years. Therefore, we developed a method called the quantitative estimate of protein-protein interaction targeting drug-likeness (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs and developed using the QED concept, involving modeling physicochemical properties based on the information available on the drug. QEPPI models the physicochemical properties of compounds that have been reported in the literature to act on PPIs. Compounds in iPPI-DB, which comprises PPI inhibitors and stabilizers, and FDA-approved drugs were evaluated using QEPPI. The results showed that QEPPI is more suitable for the early screening of PPI-targeting compounds than QED. QEPPI was also considered an extended concept of “Rule-of-Four” (RO4), a PPI inhibitor index proposed by Morelli et al. We have been able to turn a discrete value indicator into a continuous value indicator. To compare the discriminatory performance of QEPPI and RO4, we evaluated their discriminatory performance using the datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. Results of the F-score of RO4 and QEPPI were 0.446 and 0.499, respectively. QEPPI demonstrated better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it could be used as an initial filter for efficient screening of PPI-targeting compounds, which has been difficult in the past.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115694025","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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