Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics最新文献_第2页

RBFNN-based Modelling and Analysis for the Signal Reconstruction of Peripheral Nerve Tissue 基于rbfnn的周围神经组织信号重建建模与分析

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics Pub Date : 2017-08-20 DOI: 10.1145/3107411.3107478

Qichun Zhang, F. Sepulveda

引用次数: 4

Use of Structural Properties of Underlying Graphs in Pathway Enrichment Analysis of Genomic Data 底层图的结构特性在基因组数据通路富集分析中的应用

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics Pub Date : 2017-08-20 DOI: 10.1145/3107411.3107488

Pourya Naderi Yeganeh, M. Mostafavi

{"title":"Use of Structural Properties of Underlying Graphs in Pathway Enrichment Analysis of Genomic Data","authors":"Pourya Naderi Yeganeh, M. Mostafavi","doi":"10.1145/3107411.3107488","DOIUrl":"https://doi.org/10.1145/3107411.3107488","url":null,"abstract":"Common methods for the functional inference of genomic data, such as Gene Sent Enrichment Analysis (GSEA) and Over Representation Analysis (ORA), often discard the interactions between the biomolecular entities. Recent studies have explored this issue through a variety of techniques and show that using evidence from the interactions produces a more relevant and insightful inference. In this article, we introduce a method, referred to as Causal Disturbance (Cdist), for enrichment analysis. Cdist utilizes the underlying graph of pathways in combination with experimental data to detect the pathway dysregulations. To test our methodology, we utilized a public microarray data from colorectal cancer. We show that Cdist identifies the dysregulated pathways of colorectal cancer that are not detectable by other conventional methods. Some of the detected pathways by Cdist, such as apoptosis and Ras signaling, are critical for their roles in cancer. We conclude that our method facilitates a more informative inference of the disease data by incorporating the topological features of the pathway graphs. Using these features will help to detect the pathway dysregulations that are not observable through conventional models.","PeriodicalId":246388,"journal":{"name":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127952494","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}

引用次数: 3

Discovery of Regular Domains in Large DNA Data Sets 大型DNA数据集中规则域的发现

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics Pub Date : 2017-08-20 DOI: 10.1145/3107411.3110419

F. Bertacchini, E. Bilotta, Pietro S. Pantano

{"title":"Discovery of Regular Domains in Large DNA Data Sets","authors":"F. Bertacchini, E. Bilotta, Pietro S. Pantano","doi":"10.1145/3107411.3110419","DOIUrl":"https://doi.org/10.1145/3107411.3110419","url":null,"abstract":"To analyze large DNA data sets, we hypothesized that the organization of repeated bases within DNA follows rules similar to Cellular Automata (CA). These sequences could be defined as regular domains. By considering DNA strings as a finite one-dimensional cell automated, consisting of a finite (numerable) set of cells spatially aligned on a straight line and adopting a color code that transforms the DNA bases (A, C, T, G) in numbers, we analyzed DNA strings in the approach of computational mechanics. In this approach, a regular domain is a space-time region consisting of sequences in the same regular language (the particular rule of system evolution, which gives rise to a formal language) that creates patterns computationally homogeneous and simple to describe. We discovered that regular domain exists. Results revealed the exact number of strings of given lengths, establishing their limit in length, their precise localizations in all the human chromosomes and their complex numerical organization. Furthermore, the distribution of these domains is not at random, nor chaotic neither probabilistic, but there are numeric attractors around which the number of these domains are distributed. This leads us to think that all these domains within the DNA are connected to each other and cannot be casually distributed, but they follow some combinatorics rules.","PeriodicalId":246388,"journal":{"name":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132474245","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}

引用次数: 1

A Physiological Thermal Regulation Model with Application to the Diagnosis of Diabetic Peripheral Neuropathy 生理热调节模型在糖尿病周围神经病变诊断中的应用

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics Pub Date : 2017-08-20 DOI: 10.1145/3107411.3107512

V. Chekh, P. Soliz, M. Burge, S. Luan

{"title":"A Physiological Thermal Regulation Model with Application to the Diagnosis of Diabetic Peripheral Neuropathy","authors":"V. Chekh, P. Soliz, M. Burge, S. Luan","doi":"10.1145/3107411.3107512","DOIUrl":"https://doi.org/10.1145/3107411.3107512","url":null,"abstract":"Diabetes afflicts an estimated over 400 million people worldwide. People with diabetes are at the risk of a wide range of devastating complications including diabetic peripheral neuropathy, which is commonly referred to as the \"diabetic foot\" and most often affects the lower extremities (i.e., leg and foot) and can lead to amputations. In this paper, we present a computer aided diagnostic system for diabetic foot. At the core of our system is an improved thermoregulation model that characterizes the thermal recovery process of the extremities of the body (e.g., foot) after a cold stress. The model consists of a series of differential equations which is developed based on physiological characterizations and yet also exhibits analytical solutions. The model has been shown to be accurate and robust. Based on the new thermal regulation model, we have developed a 2D Bayesian classifier. We have applied the classifier to a cohort of 49 subjects (35 with no diabetic peripheral neuropathy and 14 with diabetic peripheral neuropathy). The classifier can accurately diagnose 93% of the subjects with diabetic peripheral neuropathy with a false positive rate of only 6%. This significantly outperforms current clinical diagnostic methods which may miss 61% of the patients with diabetic peripheral neuropathy.","PeriodicalId":246388,"journal":{"name":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131077346","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}

引用次数: 2

Tensor-Factorization-Based Phenotyping using Group Information: Case Study on the Efficacy of Statins 基于张量因子的表型分析使用组信息:他汀类药物疗效的案例研究

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics Pub Date : 2017-08-20 DOI: 10.1145/3107411.3107423

Jingyun Choi, Yejin Kim, Hun‐Sung Kim, I. Choi, Hwanjo Yu

{"title":"Tensor-Factorization-Based Phenotyping using Group Information: Case Study on the Efficacy of Statins","authors":"Jingyun Choi, Yejin Kim, Hun‐Sung Kim, I. Choi, Hwanjo Yu","doi":"10.1145/3107411.3107423","DOIUrl":"https://doi.org/10.1145/3107411.3107423","url":null,"abstract":"To automatically extract medical concepts from raw electronic health records (EHRs), several applications based on machine learning techniques have been proposed. Among the various techniques, tensor factorization methods have attracted considerable attention because tensor representations can capture interactions among high-dimensional EHRs. Most of the existing tensor factorization methods for computational phenotyping are only designed to derive individual phenotypes that approximate the original data. However, deriving grouped phenotypes is desirable because patients form natural groups of interest (i.e., efficacy of treatment and disease categories). In this paper, we propose Supervised Non-negative Tensor Factorization with Multinomial Logistic Regression (SNTFL) to derive grouped phenotypes that are discriminative. We define a discriminative constraint to derive grouped phenotypes and jointly optimize a multinomial logistic regression during the tensor factorization process. Our case study on a hyperlipidemia dataset demonstrates that our proposed method obtains better discrimination on patient groups compared to the baselines and successfully discovers meaningful patient subgroups.","PeriodicalId":246388,"journal":{"name":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132173437","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}

引用次数: 4

Automated Breast Cancer Diagnosis Using Deep Learning and Region of Interest Detection (BC-DROID) 基于深度学习和感兴趣区域检测的乳腺癌自动诊断(BC-DROID)

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics Pub Date : 2017-08-20 DOI: 10.1145/3107411.3107484

Richard Platania, Shayan Shams, Seungwon Yang, Jian Zhang, Kisung Lee, Seung-Jong Park

{"title":"Automated Breast Cancer Diagnosis Using Deep Learning and Region of Interest Detection (BC-DROID)","authors":"Richard Platania, Shayan Shams, Seungwon Yang, Jian Zhang, Kisung Lee, Seung-Jong Park","doi":"10.1145/3107411.3107484","DOIUrl":"https://doi.org/10.1145/3107411.3107484","url":null,"abstract":"Detection of suspicious regions in mammogram images and the subsequent diagnosis of these regions remains a challenging problem in the medical world. There still exists an alarming rate of misdiagnosis of breast cancer. This results in both over treatment through incorrect positive diagnosis of cancer and under treatment through overlooked cancerous masses. Convolutional neural networks have shown strong applicability to various image datasets, enabling detailed features to be learned from the data and, as a result, the ability to classify these images at extremely low error rates. In order to overcome the difficulty in diagnosing breast cancer from mammogram images, we propose our framework for automated breast cancer detection and diagnosis, called BC-DROID, which provides automated region of interest detection and diagnosis using convolutional neural networks. BC-DROID first pretrains based on physician-defined regions of interest in mammogram images. It then trains based on the full mammogram image. The resulting network is able to detect and classify regions of interest as cancerous or benign in one step. We demonstrate the accuracy of our framework's ability to both locate the regions of interest as well as diagnose them. Our framework achieves a detection accuracy of up to 90% and a classification accuracy of 93.5% (AUC of 92.315%). To the best of our knowledge, this is the first work enabling both automated detection and diagnosis of these areas in one step from full mammogram images. Using our framework's website, a user can upload a single mammogram image, visualize suspicious regions, and receive the automated diagnoses of these regions.","PeriodicalId":246388,"journal":{"name":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128880527","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}

引用次数: 53

Building a Molecular Taxonomy of Disease 建立疾病的分子分类学

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics Pub Date : 2017-08-20 DOI: 10.1145/3107411.3108236

Jisoo Park, Benjamin J. Hescott, D. Slonim

引用次数: 0

Tailoring Training for Obese Individuals with Case-Based Reasoning 基于案例推理的肥胖个体剪裁训练

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics Pub Date : 2017-08-20 DOI: 10.1145/3107411.3107467

Fabiana Lorenzi, Rodrigo G. da Rosa, A. Peres, G. Dorneles, André Peres, F. Ricci

引用次数: 2

Session details: Session 7: Advancing Algorithms and Methods I 会议详情:第七部分:先进的算法和方法

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics Pub Date : 2017-08-20 DOI: 10.1145/3254550

Mukul S. Bansal

引用次数: 0

Predicting the Effect of Point Mutations on Protein Structural Stability 预测点突变对蛋白质结构稳定性的影响

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics Pub Date : 2017-08-20 DOI: 10.1145/3107411.3107492

R. Farhoodi, Max Shelbourne, Rebecca Hsieh, Nurit Haspel, Brian Hutchinson, F. Jagodzinski

{"title":"Predicting the Effect of Point Mutations on Protein Structural Stability","authors":"R. Farhoodi, Max Shelbourne, Rebecca Hsieh, Nurit Haspel, Brian Hutchinson, F. Jagodzinski","doi":"10.1145/3107411.3107492","DOIUrl":"https://doi.org/10.1145/3107411.3107492","url":null,"abstract":"Predicting how a point mutation alters a protein's stability can guide drug design initiatives which aim to counter the effects of serious diseases. Mutagenesis studies give insights about the effects of amino acid substitutions, but such wet-lab work is prohibitive due to the time and costs needed to assess the consequences of even a single mutation. Computational methods for predicting the effects of a mutation are available, with promising accuracy rates. In this work we study the utility of several machine learning methods and their ability to predict the effects of mutations. We in silico generate mutant protein structures, and compute several rigidity metrics for each of them. Our approach does not require costly calculations of energy functions that rely on atomic-level statistical mechanics and molecular energetics. Our metrics are features for support vector regression, random forest, and deep neural network methods. We validate the effects of our in silico mutations against experimental Delta Delta G stability data. We attain Pearson Correlations upwards of 0.69.","PeriodicalId":246388,"journal":{"name":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114498493","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}

引用次数: 12