Sharif Digital Repository

A statistical algorithm is introduced to improve the false inference of active loci, in the population in which members are admixture. The algorithm uses an advanced clustering algorithm based on a Bayesian approach. The proposed algorithm simultaneously infers the hidden structure of the population. In this regard, the Monte Carlo Markov Chain (MCMC) algorithm has been used to evaluate the posterior probability distribution of the model parameters. The proposed algorithm is implemented in a bundle, and then its performance is widely evaluated in a number of artificial databases. The accuracy of the clustering algorithm is compared with the STRUCTURE method based on certain criterion. © 2019... 

Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the associated sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease-associated factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability... 

In the problems of Genome-Wide Association Study (GWAS), the objective is to associate subsequences of individual's genomes to the observable characteristics called phenotypes. The genome containing the biological information of an individual can be represented by a sequence of length G. Many observable characteristics of the individuals can be related to a subsequence of a given length L, called causal subsequence. The environmental affects make the relation between the causal subsequence and the observable characteristics a stochastic function. Our objective in this paper is to detect the causal subsequence of a specific phenotype using a dataset of N individuals and their observed... 

In this paper, we study the problem of causal features detection in a linear model. In a mathematical model, we consider a dataset of N samples, each represented by a sequence of G binary features. Associated to each sample, there is a binary label. It is assumed that the labels are related to a latent subset of the features, called causal features, via a linear function. More precisely, in our model, each label is the result of a noisy observation of a linear function of the causal features. We assume that the number of the causal features is bounded by L, where L is a given positive integer. In this paper, our objective is to detect the set of the causal features. In this way, at the... 

DNA sequencing has faced a huge demand since it was first introduced as a service to the public. This service is often offloaded to the sequencing companies who will have access to full knowledge of individuals' sequences, a major violation of privacy. To address this challenge, we propose a solution, which is based on separating the process of reading the fragments of sequences, which is done at a sequencing machine, and assembling the reads, which is done at a trusted local data collector. To confuse the sequencer, in a pooled sequencing scenario, in which multiple sequences are going to be sequenced simultaneously, for each target individual, we add fragments of one non-target individual,... 

The objective of a genome-wide association study (GWAS) is to associate subsequences of individuals' genomes to the observable characteristics called phenotypes (e.g., high blood pressure). Motivated by the GWAS problem, in this paper we introduce the information-theoretic problem of associated subsequence retrieval, where a dataset of N (possibly high-dimensional) sequences of length G, and their corresponding observable (binary) characteristics is given. The sequences are chosen independently and uniformly at random from XG , where X is a finite alphabet. The observable (binary) characteristic is only related to a specific unknown subsequence of length L of the sequences, called associated... 

In this paper, the metric theory of Diophantine approximation associated with mixed type small linear forms is investigated. We prove Khintchine–Groshev type theorems for both the real and complex number systems. The motivation for these metrical results comes from their applications in signal processing. One such application is discussed explicitly. © Springer Nature Switzerland AG 2020  

Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the associated sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease-associated factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability... 

This paper is concerned with estimation and stability of control systems over communication links subject to limited capacity, power constraint, fading, noisy feedback, and different transmission rate rather than system sampling rate. A key issue addressed in this paper is that in the presence of noisy feedback associated with channel, which models transmission of finite number of bits over such links as is the case in most practical scenarios, the well-known eigenvalues rate condition is still a tight bound for stability. Based on an information theoretic analysis, necessary conditions are derived for stability of discrete-time linear control systems via the distant controller in the mean... 

Remote observation of the state trajectory of nonlinear dynamic systems over limited capacity communication channels is studied. It is shown that two extreme cases are possible: Either the system is fully observable or the error in estimation blows up. The key observation is that such behavior is determined by the relationship between the Shannon capacity and the Lyapunov exponents; the well-known characterizing parameters of a communication channel on one side, and a dynamic system from the other side. In particular, it is proved that for nonlinear systems with initial state x0, the minimum capacity of an additive white Gaussian noise channel required for full observation of the system in... 

Current techniques in sequencing a genome allow a service provider (e.g. a sequencing company) to have full access to the genome information, and thus the privacy of individuals regarding their lifetime secret is violated. In this paper, we introduce the problem of private DNA sequencing, where the goal is to keep the DNA sequence private to the sequencer. We propose an architecture, where the task of reading fragments of DNA and the task of DNA assembly are separated, the former is done at the sequencer(s), and the later is completed at a local trusted data collector. To satisfy the privacy constraint at the sequencer and reconstruction condition at the data collector, we create an... 

In this paper, we develop the machinery of real interference alignment. This machinery is extremely powerful in achieving the sum degrees of freedom (DoF) of single antenna systems. The scheme of real interference alignment is based on designing single-layer and multilayer constellations used for modulating information messages at the transmitters. We show that constellations can be aligned in a similar fashion as that of vectors in multiple antenna systems and space can be broken up into fractional dimensions. The performance analysis of the signaling scheme makes use of a recent result in the field of Diophantine approximation, which states that the convergence part of the... 

Karyotype is a genetic test that is used for detection of chromosomal defects. In a karyotype test, an image is captured from chromosomes during the cell division. The captured images are then analyzed by cytogeneticists in order to detect possible chromosomal defects. In this paper, we have proposed an automated pipeline for analysis of karyotype images. There are three main steps for karyotype image analysis: image enhancement, image segmentation and chromosome classification. In this paper, we have proposed a novel chromosome segmentation algorithm to decompose overlapped chromosomes. We have also proposed a CNN-based classifier which outperforms all the existing classifiers. Our... 

Polyols were used as model ligands for Mg2+, Ca2+, and Zn2+ complexes to study the role of the hydrogen bond network on the metal binding affinity and modulation of successive pKa values using density functional theory. The results confirm that the acidity of polyols dramatically increases upon metal complexation in the order Zn2+ > Mg2+ > Ca2+. For example, the three H-site positions in the hydroxyl groups of the heptaol, bound to Zn2+, are 11.2, 29.9, and 30.9 pKa units (in methanol) more acidic than those of pure heptaol. This acidity enhancement leads to making polyols as good ligands toward complexation. For instance, the formation constants of the heptaol in the presence of Zn2+, Mg2+,... 

Using density functional theory, polyols were used as model ligands for Cu+/Cu2+ complexes to study the role of the hydrogen bond network on the metal binding affinity. In addition to the gas phase studies, the calculations were performed in 1-decanol and DMSO solvents. The Cu2+ complexes were the most stable complexes with the highest bond dissociation energies (BDE). The presence of three H-bonds in the first shell increased BDE values up to 17.99 and 57.07 kcal/mol for Cu+ and Cu2+ complexes in the gas phase, respectively, whereas the presence of another three H-bonds in the second shell increased BDE values up to 7.27 and 24.35 kcal/mol for Cu+ and Cu2+ complexes in the gas phase,... 

We study the sample complexity of learning a high-dimensional simplex from a set of points uniformly sampled from its interior. Learning of simplices is a long studied problem in computer science and has applications in computational biology and remote sensing, mostly under the name of “spectral unmixing.” We theoretically show that a sufficient sample complexity for reliable learning of a K-dimensional simplex up to a total-variation error of ε is O(Kε2 log Kε ), which yields a substantial improvement over existing bounds. Based on our new theoretical framework, we also propose a heuristic approach for the inference of simplices. Experimental results on synthetic and real-world datasets... 

Genome-Wide Association Study (GWAS) addresses the problem of associating subsequences of individuals' genomes to the observable characteristics called phenotypes. In a genome of length G, it is observed that each characteristic is only related to a specific subsequence of it with length L, called the causal subsequence. The objective is to recover the causal subsequence, using a dataset of N individuals' genomes and their observed characteristics. Recently, the problem has been investigated from an information theoretic point of view in [1]. It has been shown that there is a threshold effect for reliable learning of the causal subsequence at Gh ( N L/G ) by characterizing the capacity of... 

A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from a dataset distributed across multiple local machines. The local machines can communicate with the central machine through a wireless multiple access channel. In this paper, we are interested in designing effective strategies where reliable learning is feasible under power and bandwidth limitations. Two approaches are proposed: Signs and Uncoded methods. In the Signs method, the local machines quantize their data into binary vectors and an optimal channel coding scheme is used to reliably send the vectors to the central machine where the structure is learned from the received... 

Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure... 

We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially robust or ii) non-robust loss functions have been considered. Notably, we allow the unlabeled samples to deviate slightly (in total variation sense) from the in-domain distribution. The core idea behind our framework is to combine Distributionally Robust Optimization (DRO) with self-supervised training. As a result, we also leverage efficient polynomial-time algorithms for the training stage. From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in Rd, where...