A look ahead approach to secure multi-party protocols

Secure multi-party protocols have been proposed to enable non-colluding parties to cooperate without a trusted server. Even though such protocols prevent information disclosure other than the objective function, they are quite costly in computation and communication. Therefore, the high overhead makes it necessary for parties to estimate the utility that can be achieved as a result of the protocol beforehand. In this paper, we propose a look ahead approach, specifically for secure multi-party protocols to achieve distributed k-anonymity, which helps parties to decide if the utility benefit from the protocol is within an acceptable range before initiating the protocol. Look ahead operation is highly localized and its accuracy depends on the amount of information the parties are willing to share. Experimental results show the effectiveness of the proposed methods

Ensuring location diversity in privacy preserving spatio-temporal data mining

The rise of mobile technologies in the last decade has lead to vast amounts of location information generated by individuals. From the knowledge discovery point of view, this data is quite valuable as it has commercial value, but the inherent personal information in the data raises privacy concerns. There exist many algorithms in the literature to satisfy the privacy requirements of individuals, by generalizing, perturbing, and suppressing data. The algorithms that try to ensure a level of indistinguishability between trajectories in the dataset, fail when there is not enough diversity among sensitive locations visited by those users. We propose an approach that ensures location diversity named as (c,p)- confidentiality, which bounds the probability of visiting a sensitive location given the background knowledge of the adversary. Instead of grouping the trajectories, we anonymize the underlying map structure. We explain our algorithm and show the performance of our approach. We also compare the performance of our algorithm with an existing technique and show that location diversity can be satisfied efficiently

Robust inference of kinase activity using functional networks

Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer’s disease and Parkinson’s disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io

SPADIS: an algorithm for selecting predictive and diverse SNPs in GWAS

Motivation: Phenotypic heritability of complex traits and diseases is seldom explained by individual genetic variants identified in genome-wide association studies (GWAS). Many methods have been developed to select a subset of variant loci, which are associated with or predictive of the phenotype. Selecting connected SNPs on SNP-SNP networks have been proven successful in finding biologically interpretable and predictive SNPs. However, we argue that the connectedness constraint favors selecting redundant features that affect similar biological processes and therefore does not necessarily yield better predictive performance. Results: In this paper, we propose a novel method called SPADIS that favors the selection of remotely located SNPs in order to account for their complementary effects in explaining a phenotype. SPADIS selects a diverse set of loci on a SNP-SNP network. This is achieved by maximizing a submodular set function with a greedy algorithm that ensures a constant factor approximation to the optimal solution. We compare SPADIS to the state-of-the-art method SConES, on a dataset of Arabidopsis Thaliana with continuous flowering time phenotypes. SPADIS has better average phenotype prediction performance in 15 out of 17 phenotypes when the same number of SNPs are selected and provides consistent improvements across multiple networks and settings on average. Moreover, it identifies more candidate genes and runs faster. We also investigate the use of Hi-C data to construct SNP-SNP network in the context of SNP selectio

Enhancing the power factor of p-type BiSbTe films via deposited with/without Cr seed layer

The thermoelectric effect is an efficient method to use waste heat as a primary source of electrical energy. Being a room temperature thermoelectric thin film, p-type BiSbTe is one of the best candidates owing to the combined high efficiency and large power factor for future technological applications. Novel approaches have emerged in recent decades with the aim of enhancing the thermoelectric properties of BiSbTe thin films. The method involves using Cr as an adhesion and seed layer for controlling microstructure and transport properties via the energy filtering of high-energy carriers. The heterostructure of Cr/BiSbTe film demonstrates the best electrical transport performance, where the Seebeck coefficient and the electrical conductivity are 425 mu V/K and 25 S/m* 10(3) in the vicinity of room temperature. The power factor of Cr/BiSbTe was reported to be 6.8 mW/mK(2) at 375 K, which was approximately seven times higher than the film without the Cr layer. We conclude that the inclusion of the Cr seed layer can notably improve the electrical transport properties of p-type BiSbTe films. (C) 2021 Elsevier B.V. All rights reserved

SPADIS: an algorithm for selecting predictive and diverse SNPs in GWAS

Phenotypic heritability of complex traits and diseases is seldom explained by individual genetic variants identified in genome-wide association studies (GWAS). Many methods have been developed to select a subset of variant loci, which are associated with or predictive of the phenotype. Selecting connected SNPs on SNP-SNP networks have been proven successful in finding biologically interpretable and predictive SNPs. However, we argue that the connectedness constraint favors selecting redundant features that affect similar biological processes and therefore does not necessarily yield better predictive performance. In this paper, we propose a novel method called SPADIS that favors the selection of remotely located SNPs in order to account for their complementary effects in explaining a phenotype. SPADIS selects a diverse set of loci on a SNP-SNP network. This is achieved by maximizing a submodular set function with a greedy algorithm that ensures a constant factor approximation to the optimal solution. We compare SPADIS to the state-of-the-art method SConES, on a dataset of Arabidopsis Thaliana with continuous flowering time phenotypes. SPADIS has better average phenotype prediction performance in 15 out of 17 phenotypes when the same number of SNPs are selected and provides consistent improvements. Moreover, it identifies more candidate genes and runs faster

A look-ahead approach to secure multiparty protocols

Secure multiparty protocols have been proposed to enable noncolluding parties to cooperate without a trusted server. Even though such protocols prevent information disclosure other than the objective function, they are quite costly in computation and communication. The high overhead motivates parties to estimate the utility that can be achieved as a result of the protocol beforehand. In this paper, we propose a look-ahead approach, specifically for secure multiparty protocols to achieve distributed k-anonymity, which helps parties to decide if the utility benefit from the protocol is within an acceptable range before initiating the protocol. The look-ahead operation is highly localized and its accuracy depends on the amount of information the parties are willing to share. Experimental results show the effectiveness of the proposed methods

Operational variable job scheduling with eligibility constraints: a randomized constraint-graph-based approach

In this study, we consider the problem of Operational Variable Job Scheduling, also referred to as parallel machine scheduling with time windows. The problem is a more general version of the Fixed Job Scheduling problem, involving a Lime window for each job larger than its processing time. The objective is to find the optimal subset of the jobs that can be processed. An interesting application area ties in Optimal Berth Allocation, which involves the assignment of vessels arriving at the port to appropriate berths within their time windows, while maximizing the total profit from the served vessels. Eligibility constraints are also taken into consideration. We develop an integer programming model for the problem. We show that the problem is NP-hard, and develop a constraint-graph-based construction algorithm for generating near-optimal solutions. We use genetic algorithm and other improvement algorithms to enhance the solution. Computational experimentation reveals that our algorithm generates very high quality solutions in very small computation times

Revisiting the complex architecture of ALS in Turkey: Expanding genotypes, shared phenotypes, molecular networks, and a public variant database

The last decade has proven that amyotrophic lateral sclerosis (ALS) is clinically and genetically heterogeneous, and that the genetic component in sporadic cases might be stronger than expected. This study investigates 1,200 patients to revisit ALS in the ethnically heterogeneous yet inbred Turkish population. Familial ALS (fALS) accounts for 20% of our cases. The rates of consanguinity are 30% in fALS and 23% in sporadic ALS (sALS). Major ALS genes explained the disease cause in only 35% of fALS, as compared with similar to 70% in Europe and North America. Whole exome sequencing resulted in a discovery rate of 42% (53/127). Whole genome analyses in 623 sALS cases and 142 population controls, sequenced within Project MinE, revealed well-established fALS gene variants, solidifying the concept of incomplete penetrance in ALS. Genome-wide association studies (GWAS) with whole genome sequencing data did not indicate a new risk locus. Coupling GWAS with a coexpression network of disease-associated candidates, points to a significant enrichment for cell cycle- and division-related genes. Within this network, literature text-mining highlightsDECR1, ATL1, HDAC2, GEMIN4, andHNRNPA3as important genes. Finally, information on ALS-related gene variants in the Turkish cohort sequenced within Project MinE was compiled in the GeNDAL variant browser (www.gendal.org).Bogazici University; Suna and Inan Kirac Foundatio