Computational annotation of UTR cis-regulatory modules through Frequent Pattern Mining

<p>Abstract</p> <p>Background</p> <p>Many studies report about detection and functional characterization of cis-regulatory motifs in untranslated regions (UTRs) of mRNAs but little is known about the nature and functional role of their distribution. To address this issue we have developed a computational approach based on the use of data mining techniques. The idea is that of mining frequent combinations of translation regulatory motifs, since their significant co-occurrences could reveal functional relationships important for the post-transcriptional control of gene expression. The experimentation has been focused on targeted mitochondrial transcripts to elucidate the role of translational control in mitochondrial biogenesis and function.</p> <p>Results</p> <p>The analysis is based on a two-stepped procedure using a sequential pattern mining algorithm. The first step searches for frequent patterns (FPs) of motifs without taking into account their spatial displacement. In the second step, frequent sequential patterns (FSPs) of spaced motifs are generated by taking into account the conservation of spacers between each ordered pair of co-occurring motifs. The algorithm makes no assumption on the relation among motifs and on the number of motifs involved in a pattern. Different FSPs can be found depending on different combinations of two parameters, i.e. the threshold of the minimum percentage of sequences supporting the pattern, and the granularity of spacer discretization. Results can be retrieved at the UTRminer web site: <url>http://utrminer.ba.itb.cnr.it/</url>. The discovered FPs of motifs amount to 216 in the overall dataset and to 140 in the human subset. For each FP, the system provides information on the discovered FSPs, if any. A variety of search options help users in browsing the web resource. The list of sequence IDs supporting each pattern can be used for the retrieval of information from the UTRminer database.</p> <p>Conclusion</p> <p>Computational prediction of structural properties of regulatory sequences is not trivial. The presented data mining approach is able to overcome some limits observed in other competitive tools. Preliminary results on UTR sequences from nuclear transcripts targeting mitochondria are promising and lead us to be confident on the effectiveness of the approach for future developments.</p

INSOMNIA:Towards Concept-Drift Robustness in Network Intrusion Detection

Despite decades of research in network traffic analysis and incredible advances in artificial intelligence, network intrusion detection systems based on machine learning (ML) have yet to prove their worth. One core obstacle is the existence of concept drift, an issue for all adversary-facing security systems. Additionally, specific challenges set intrusion detection apart from other ML-based security tasks, such as malware detection. In this work, we offer a new perspective on these challenges. We propose INSOMNIA, a semi-supervised intrusion detector which continuously updates the underlying ML model as network traffic characteristics are affected by concept drift. We use active learning to reduce latency in the model updates, label estimation to reduce labeling overhead, and apply explainable AI to better interpret how the model reacts to the shifting distribution. To evaluate INSOMNIA, we extend TESSERACT - a framework originally proposed for performing sound time-aware evaluations of ML-based malware detectors - to the network intrusion domain. Our evaluation shows that accounting for drifting scenarios is vital for effective intrusion detection systems

Multi-Channel Deep Feature Learning for Intrusion Detection

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Low Incidence Rate of Opportunistic and Viral Infections During Imatinib Treatment in Chronic Myeloid Leukemia Patients in Early and Late Chronic Phase.

&lt;!--StartFragment--&gt; &lt;p class="MsoNormal" style="text-align: justify; line-height: 150%;"&gt;&lt;span style="font-family: Arial; mso-ansi-language: EN-GB;" lang="EN-GB"&gt;Background: Imatinib has become first line therapy in chronic myeloid leukemia patients. Little is known about the infective consequences during the treatment with this drug in large series of chronic phase patients. &lt;/span&gt;&lt;/p&gt; &lt;p class="MsoNormal" style="text-align: justify; line-height: 150%;"&gt;&lt;span style="font-family: Arial; mso-ansi-language: EN-GB;" lang="EN-GB"&gt;Material and methods: From January 2001 to September 2006 we treated with imatinib 250 patients in first line (early CP) or after interferon failure (late CP), out of clinical trials and recorded all the bacterial and viral infections occurred.&lt;/span&gt;&lt;/p&gt; &lt;p class="MsoNormal" style="text-align: justify; line-height: 150%;"&gt;&lt;span style="font-family: Arial; mso-ansi-language: EN-GB;" lang="EN-GB"&gt;Results: We recorded a similar incidence of bacterial and viral infections both in first line and late CP patients (respectively, 16% and 13%) during 3.5 years of follow-up. Analysis of presenting features predisposing to infections revealed differences only in late CP patients, with elevated percentage of high Sokal risk patients and a more longer median time from diagnosis to start of imatinib.&lt;/span&gt;&lt;/p&gt; &lt;p class="MsoNormal" style="text-align: justify; line-height: 150%;"&gt;&lt;span style="font-family: Arial; mso-ansi-language: EN-GB;" lang="EN-GB"&gt;Conclusions: Opportunistic infections and reactivation of Herpes Zoster are observed during imatinib therapy at very low incidence.&lt;/span&gt;&lt;/p&gt; &lt;!--EndFragment--&gt

Trustworthiness of Context-Aware Urban Pollution Data in Mobile Crowd Sensing

Urban pollution is usually monitored via fixed stations that provide detailed and reliable information, thanks to equipment quality and effective measuring protocols, but these sampled data are gathered from very limited areas and through discontinuous monitoring campaigns. Currently, the spread of mobile devices has fostered the development of new approaches, like Mobile Crowd Sensing (MCS), increasing the chances of using smartphones as suitable sensors in the urban monitoring scenario, because it potentially contributes massive ubiquitous data at relatively low cost. However, MCS is useless (or even counter-productive), if contributed data are not trustworthy, due to wrong data-collection procedures by non-expert practitioners. Contextualizing monitored data with those coming from phone-embedded sensors and from time/space proximity can improve data trustworthiness. This work focuses on the development of an algorithm that exploits context awareness to improve the reliability of MCS collected data. It has been validated against some real use cases for noise pollution and promises to improve the trustworthiness of end users generated data

Using interactions and dynamics for mining groups of moving objects from trajectory data

Advances in tracking technology enable the gathering of spatio-temporal data in the form of trajectories, which when analysed can convey useful knowledge. In particular, discovering groups of moving objects is a valuable means for a wide class of problems related to mobility. The task of group mining has been investigated by considering mostly the spatial closeness and similarity of the trajectories, while little attention has been paid to the relationships between the trajectories and time-changing nature of the trajectories. The relationships may provide evidence of interactions between the moving objects. The time-changing nature may provide evidence of dynamics of the movements. Therefore, interactions and dynamics can be sources of information to be considered in order to discover new forms of groups.  Motivated by this, we introduce the concept of crews and propose a method to discover crews. A crew gathers moving objects with similar interactions and similar dynamics. The proposed method relies on i) new movement parameters, which explicitly consider interactions and dynamics, and ii) a distance-free clustering algorithm, which groups objects based on the similarity of the movement parameters. We conduct extensive experiments, which include a quantitative evaluation of the quality of the crews and comparison with alternative solutions