Machine learning in bioinformatics: A brief survey and recommendations for practitioners

Bhaskar, Harish; Hoyle, David C.; Singh, Sameer

doi:10.1016/j.compbiomed.2005.09.002

Cited by 95 publications

(65 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…9 Combining the information from multivariate HCS readouts increases the accuracy of decisions. 10 The method is still in its infancy, although automated parameter selection and machine learning methods such as support vector machines and other classifiers 11 are being successfully applied to this end. 12 We demonstrate such a technique for multiparametric hit selection from a high-content screen, leveraging HCS information and hereby significantly increasing precision and hit prediction.…”

Section: Multivariate Data Analysis On Hcs Screensmentioning

confidence: 99%

Robust Hit Identification by Quality Assurance and Multivariate Data Analysis of a High-Content, Cell-Based Assay

Dürr¹,

Duval²,

Nichols³

et al. 2007

SLAS Discovery

View full text Add to dashboard Cite

Recent technological advances in high-content screening instrumentation have increased its ease of use and throughput, expanding the application of high-content screening to the early stages of drug discovery. However, high-content screens produce complex data sets, presenting a challenge for both extraction and interpretation of meaningful information. This shifts the high-content screening process bottleneck from the experimental to the analytical stage. In this article, the authors discuss different approaches of data analysis, using a phenotypic neurite outgrowth screen as an example. Distance measurements and hierarchical clustering methods lead to a profound understanding of different high-content screening readouts. In addition, the authors introduce a hit selection procedure based on machine learning methods and demonstrate that this method increases the hit verification rate significantly (up to a factor of 5), compared to conventional hit selection based on single readouts only. (Journal of Biomolecular

show abstract

Section: Multivariate Data Analysis On Hcs Screensmentioning

confidence: 99%

Robust Hit Identification by Quality Assurance and Multivariate Data Analysis of a High-Content, Cell-Based Assay

Dürr¹,

Duval²,

Nichols³

et al. 2007

SLAS Discovery

View full text Add to dashboard Cite

show abstract

“…Classification, or supervised learning, is a vastly studied topic in machine learning [23] and it finds application in fields such as, for example, computer vision [130], text classification [129], computer aided diagnosis and medical imaging [61], bioinformatics [12] and intrusion detection [87]. A common problem among all the classification approaches is the need for validation data, and more specifically, data for which the correct classification is known.…”

Section: Manual Ground Truth Generationmentioning

confidence: 99%

“…Correlate F1 to A ; 16: if F2 exists then 17: Correlate F2 to A ; L.Corr ← true ; 18: end if 19: end if 20: end for After searching for a response flow, the algorithm proceeds with retrieving the best matching log event (lines [8][9][10][11][12]. The log event must match the flow characteristics.…”

mentioning

confidence: 99%

Flow-based intrusion detection

Sperotto

Pras

2011

12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops

View full text Add to dashboard Cite

The spread of 1-10Gbps technology has in recent years paved the way to a flourishing landscape of new, high-bandwidth Internet services. As users, we depend on the Internet in our daily life for simple tasks such as checking emails, but also for managing private and financial information. However, entrusting such information to the Internet also means that the network has become an alluring place for hackers. To this threat, the research community has answered with an increased interest in intrusion detection. With the number of attacks almost exponentially increasing, and the attackers' motivations moving from ideological to economical, the researchers' attention is focused on developing new techniques to timely detect intruders and prevent damage. Our studies in the field of intrusion detection, however, made us realize that additional research is needed, in particular: the creation of shared data sets to validate Intrusion Detection Systems (IDSs) and the development of automatic procedures to tune the parameters of IDSs.The contribution of this thesis is that it develops a structured approach to intrusion detection that focuses on (i) shared ground-truth data sets and (ii) automatic parameter tuning. We develop our approach by focusing on network flows. Flows offer an aggregated view of network traffic, by reporting on the amount of packets and bytes exchanged over the network. Therefore, flows drastically reduce the amount of data to be analyzed. In this thesis, we aim at detecting anomalies in flow-based time series, describing how the number of flows, packets and bytes changes over time.Ground truth data sets are fundamental in the development phase, for validation purposes and, if publicly available, for comparison between different IDSs. We attack the problem of ground truth generation in two complementary manners.First, we obtain ground truth information for flow-based intrusion detection by manually creating it. We do so by means of a honeypot-based data collection and monitoring setup, specifically tuned to (i) offer an attracting platform for attackers, and (ii) include enhanced logging capabilities to support the vi labeling of the collected data. The outcome of our research has been a publicly released flow-based labeled data set. To the best of our knowledge, no such data set already exists.Second, we generate ground truth information in an automatic manner. We do this by generating artificial flow, packet and byte time series for benign and attack traffic. In this thesis, we rely upon Hidden Markov Models (HMMs), which allow for probabilistic and compact representations of flow-based time series and can be used for generation purposes.Finally, we approach the problem of automatic tuning of IDSs. The performance of an IDS is governed by the trade-off between detecting all anomalies (at the expense of raising alarms too often), and missing anomalies (but not issuing many false alarms). We developed an optimization procedure that aims to mathematically treat such trade-off in a systematic m...

show abstract

“…Their rapid uptake in bioinformatics is due to their reliability, accuracy, and computational efficiency 5 . More specifically, we made use of the weighted Least Squares Support Vector Machine (LS-SVM), taking the unbalancedness of data sets into account [6][7] .…”

Section: Introductionmentioning

confidence: 99%

Supervised Classification of Array CGH Data With HMM-Based Feature Selection

et al. 2008

View full text Add to dashboard Cite

Motivation: For different tumour types, extended knowledge about the molecular mechanisms involved in tumorigenesis is lacking. Looking for copy number variations (CNV) by Comparative Genomic Hybridization (CGH) can help however to determine key elements in this tumorigenesis. As genome-wide array CGH gives the opportunity to evaluate CNV at high resolution, this leads to huge amount of data, necessitating adequate mathematical methods to carefully select and interpret these data. Results: Two groups of patients differing in cancer subtype were defined in two publicly available array CGH data sets as well as in our own data set on ovarian cancer. Chromosomal regions characterizing each group of patients were gathered using recurrent hidden Markov Models (HMM). The differential regions were reduced to a subset of features for classification by integrating different univariate feature selection methods. Weighted Least Squares Support Vector Machines (LS-SVM), a supervised classification method which takes unbalancedness of data sets into account, resulted in leave-one-out or 10-fold cross-validation accuracies ranging from 88 to 95.5%. Conclusion: The combination of recurrent HMMs for the detection of copy number alterations with LS-SVM classifiers offers a novel methodological approach for classification based on copy number alterations. Additionally, this approach limits the chromosomal regions that are necessary to classify patients according to cancer subtype.

show abstract

Machine learning in bioinformatics: A brief survey and recommendations for practitioners

Cited by 95 publications

References 63 publications

Robust Hit Identification by Quality Assurance and Multivariate Data Analysis of a High-Content, Cell-Based Assay

Robust Hit Identification by Quality Assurance and Multivariate Data Analysis of a High-Content, Cell-Based Assay

Flow-based intrusion detection

Supervised Classification of Array CGH Data With HMM-Based Feature Selection

Contact Info

Product

Resources

About