14032548
Profile-based string kernels for remote homology detection and motif extraction
We introduce novel profile-based string kernels for use with support vector machines (SVMs) for the problems of protein classification and remote homology detection. These kernels use probabilistic profiles, such as those produced by the PSI-BLAST algorithm, to define position-dependent mutation neighborhoods along protein sequences for inexact matching of k-length subsequences ("k-mers") in the data. By use of an efficient data structure, the kernels are fast to compute once the profiles have been obtained. For example, the time needed to run PSI-BLAST in order to build the profiles is significantly longer than both the kernel computation time and the SVM training time. We present remote homology detection experiments based on the SCOP database where we show that profile-based string kernels used with SVM classifiers strongly outperform all recently presented supervised SVM methods. We also show how we can use the learned SVM classifier to extract "discriminative sequence motifs" - short regions of the original profile that contribute almost all the weight of the SVM classification score - and show that these discriminative motifs correspond to meaningful structural features in the protein data. The use of PSI-BLAST profiles can be seen as a semi-supervised learning technique, since PSI-BLAST leverages unlabeled data from a large sequence database to build more informative profiles. Recently presented "cluster kernels " give general semi-supervised methods for improving SVM protein classification performance. We show that our profile kernel results are comparable to cluster kernels while providing much better scalability to large datasets.
38950979
Behavior Profiling of Email
This paper describes the forensic and intelligence analysis capabilities of the Email Mining Toolkit (EMT) under development at the Columbia Intrusion Detection (IDS) Lab. EMT provides the means of loading, parsing and analyzing email logs, including content, in a wide range of formats. Many tools and techniques have been available from the fields of Information Retrieval (IR) and Natural Language Processing (NLP) for analyzing documents of various sorts, including emails. EMT, however, extends these kinds of analyses with an entirely new set of analyses that model "user behavior". EMT thus models the behavior of individual user email accounts, or groups of accounts, including the "social cliques" revealed by a user's email behavior.
6868150
FLIPS: Hybrid Adaptive Intrusion Prevention
Intrusion detection systems are fundamentally passive and fail–open. Because their primary task is classification, they do nothing to prevent an attack from succeeding. An intrusion prevention system (IPS) adds protection mechanisms that provide fail–safe semantics, automatic response capabilities, and adaptive enforcement. We present FLIPS (Feedback Learning IPS), a hybrid approach to host security that prevents binary code injection attacks. It incorporates three major components: an anomaly-based classifier, a signature-based filtering scheme, and a supervision framework that employs Instruction Set Randomization (ISR). Since ISR prevents code injection attacks and can also precisely identify the injected code, we can tune the classifier and the filter via a learning mechanism based on this feedback. Capturing the injected code allows FLIPS to construct signatures for zero-day exploits. The filter can discard input that is anomalous or matches known malicious input, effectively protecting the application from additional instances of an attack – even zero-day attacks or attacks that are metamorphic in nature. FLIPS does not require a known user base and can be deployed transparently to clients and with minimal impact on servers. We describe a prototype that protects HTTP servers, but FLIPS can be applied to a variety of server and client applications.
304685
Anonymizing sequential releases
An organization makes a new release as new information become available, releases a tailored view for each data request, releases sensitive information and identifying information separately. The availability of related releases sharpens the identification of individuals by a global quasi-identifier consisting of attributes from related releases. Since it is not an option to anonymize previously released data, the current release must be anonymized to ensure that a global quasi-identifier is not effective for identification. In this paper, we study the sequential anonymization problem under this assumption. A key question is how to anonymize the current release so that it cannot be linked to previous releases yet remains useful for its own release purpose. We introduce the lossy join, a negative property in relational database design, as a way to hide the join relationship among releases, and propose a scalable and practical solution.
30385305
Genome-Wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes
Transcriptional interactions in a cell are modulated by a variety of mechanisms that prevent their representation as pure pairwise interactions between a transcription factor and its target(s). These include, among others, transcription factor activation by phosphorylation and acetylation, formation of active complexes with one or more co-factors, and mRNA/protein degradation and stabilization processes. 
 
This paper presents a first step towards the systematic, genome-wide computational inference of genes that modulate the interactions of specific transcription factors at the post-transcriptional level. The method uses a statistical test based on changes in the mutual information between a transcription factor and each of its candidate targets, conditional on the expression of a third gene. The approach was first validated on a synthetic network model, and then tested in the context of a mammalian cellular system. By analyzing 254 microarray expression profiles of normal and tumor related human B lymphocytes, we investigated the post transcriptional modulators of the MYC proto-oncogene, an important transcription factor involved in tumorigenesis. Our method discovered a set of 100 putative modulator genes, responsible for modulating 205 regulatory relationships between MYC and its targets. The set is significantly enriched in molecules with function consistent with their activities as modulators of cellular interactions, recapitulates established MYC regulation pathways, and provides a notable repertoire of novel regulators of MYC function. The approach has broad applicability and can be used to discover modulators of any other transcription factor, provided that adequate expression profile data are available.
12098916
Binary decision diagram with minimum expected path length
We present methods to generate a Binary Decision Diagram (BDD) with minimum expected path length. A BDD is a generic data structure which is widely used in several fields. One important application is the representation of Boolean functions. A BDD representation enables us to evaluate a Boolean function: Simply traverse the BDD from the root node to the terminal node and retrieve the value in the terminal node. For a BDD with minimum expected path length will be also minimized the evaluation time for the corresponding Boolean function. Three efficient algorithms for constructing BDDs with minimum expected path length are proposed.
31845675
Alternating Fixpoint Theory for Logic Programs with Priority
van Gelder's alternating fixpoint theory has proven to be a very useful tool for unifying and characterizing various semantics for logic programs without priority. In this paper we propose an extension of van Gelder's alternating fixpoint theory and show that it can be used as a general semantic framework for logic programs with priority. Specifically, we define three declarative and model-theoretic semantics in this framework for prioritied logic programs: prioritized answer sets, prioritized regular extensions and prioritized well-founded model. We show that all of these semantics are natural generalizations of the corresponding semantics for logic programs without priority. We also show that these semantics have some other desirable properties. In particular, they can handle conflicts caused indirectly by the priorities.
9884844
Parallel multilevel block ILU preconditioning techniques for large sparse linear systems
We present a class of parallel preconditioning strategies built on a multilevel block incomplete LU (ILU)factorization technique to solve large sparse linear systems on distributed memory parallel computers. The preconditioners are constructed by using the concept of block independent sets. Two algorithms for constructing block independent sets of a distributed sparse matrix are proposed. We compare a few implementations of the parallel multilevel ILU preconditioners with different block independent set algorithms and different coarse level solution strategies. We also use some diagonal thresholding and perturbation strategies to enhance factorization stability. Numerical experiments indicate that our parallel multilevel block ILU preconditioners are robust and efficient.