Ashis Kumer Biswas

Efficient approaches for designing reversible Binary Coded Decimal adders

Reversible logic has become one of the most promising research areas in the past few decades and has found its applications in several technologies; such as low-power CMOS, nanocomputing and optical computing. This paper presents improved and efficient reversible logic implementations for Binary Coded Decimal (BCD) adder as well as Carry Skip BCD adder. It has been shown that the modified designs outperform the existing ones in terms of number of gates, number of garbage outputs, delay, and quantum cost. In order to show the efficiency of the proposed designs, lower bounds of the reversible BCD adders in terms of gates and garbage outputs are proposed as well.

A Novel Approach to Design BCD Adder and Carry Skip BCD Adder

Reversible logic has become one of the most promising research areas in the past few decades and has found its applications in several technologies; such as low power CMOS, nanocomputing and optical computing. This paper presents improved and efficient reversible logic implementations for Binary Coded Decimal (BCD) adder as well as Carry Skip BCD adder. It has been shown that the modified designs outperform the existing ones in terms of number of gates, number of garbage output and delay.

Infobox suggestion for Wikipedia entities

Given the sheer amount of work and expertise required in authoring Wikipedia articles, automatic tools that help Wikipedia contributors in generating and improving content are valuable. This paper presents our initial step towards building a full-fledged author assistant, particularly for suggesting infobox templates for articles. We build SVM classifiers to suggest infobox template types, among a large number of possible types, to Wikipedia articles without infoboxes. Different from prior works on Wikipedia article classification which deal with only a few label classes for named entity recognition, the much larger 337-class setup in our study is geared towards realistic deployment of infobox suggestion tool. We also emphasize testing on articles without infoboxes, due to that labeled and unlabeled data exhibit different distributions of features, which departs from the typical assumption that they are drawn from the same underlying population.

Inferring disease associations of the long non-coding RNAs through non-negative matrix factorization

Long non-coding RNAs (lncRNAs) have been implicated in various biological processes, and are linked in many dysregulations. Over the past decade, researchers reported a large number of human disease associations with the lncRNAs, both intergenic lncRNAs (lincRNAs) and non-intergenic lncRNAs. Thanks to the next generation sequencing platform, RNA-seq, through which researchers also were able to quantify expression profiles of each of the lncRNAs in human tissue samples. In this article we adapted the non-negative matrix factorization method to develop a low-rank computational model that can describe the existing knowledge about both non-intergenic and intergenic lncRNA-disease associations represented in a two dimensional association matrix as well as convey a way of ranking disease causing lncRNAs. We proposed several NMF formulations for the problem and we found that the sparsity-constrained NMF obtained the best model among all the other models. By exploiting the inherent bi-clustering ability of the NMF models, we extracted several lncRNA groups and disease groups that possess biological significance. Moreover, we proposed an integrative NMF formulation where we incorporated along with the coding gene and lincRNA disease association data, prior knowledge about relationship networks among the coding genes and lincRNAs, and the RNA-seq expression profile data to identify potential lincRNA-coding gene co-modules with which we further enhanced the lincRNA-disease associations and untangled mysteries about functional chemistry of the intergenic lncRNAs. Experimental results show the superiority of our proposed method over two state-of-the-art clustering algorithms—k-means and hierarchical clustering.

Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders

BackgroundInferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator.ResultsWe present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection.Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above.ConclusionsIn this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders.

CNCTDiscriminator: Coding and noncoding transcript discriminator - An excursion through hypothesis learning and ensemble learning approaches

The statistics about the open reading frames, the base compositions and the properties of the predicted secondary structures have potential to address the problem of discriminating coding and noncoding transcripts. Again, the Next Generation Sequencing platform, RNA-seq, provides us bounty of data from which expression profiles of the transcripts can be extracted which urged us adding a new set of dimension in this classification task. In this paper, we proposed CNCTDiscriminator -- a coding and noncoding transcript discriminating system where we applied the integration of these four categories of features about the transcripts. The feature integration was done using both hypothesis learning and feature specific ensemble learning approaches. The CNCTDiscriminator model which was trained with composition and ORF features outperforms (precision 83.86%, recall 82.01%) other three popular methods -- CPC (precision 98.31%, recall 25.95%), CPAT (precision 97.74%, recall 52.50%) and PORTRAIT (precision 84.37%, recall 73.2%) when applied to an independent benchmark dataset. However, the CNCTDiscriminator model that was trained using the ensemble approach shows comparable performance (precision 89.85%, recall 71.08%).

Low-power and area efficient binary coded decimal adder design using a look up table-based field programmable gate array

The binary coded decimal (BCD) system is suitable for digital communication, which can be designed by field programmable gate array (FPGA) technology, where look up table (LUT) is one of the major components of FPGA. In this study, the authors proposed a low power and area efficient LUT-based BCD adder which is constructed basically in three steps: First, a new technique is introduced for the BCD addition to obtain the correct BCD digit. Second, a new controller circuit of LUT is presented which is designed to select and send Read/Write voltage to memory cell for performing Read or Write operation. Finally, a compact BCD adder is designed using the proposed LUT. Their proposed 2-input LUT outperforms the existing best one providing 65.8% improvement in terms of area, 44.1% for Read operation and 43.5% for Write operation in power consumption. The proposed BCD adder using FPGA gains a radical achievement compared with the existing best-known LUT-based BCD adder providing prominent better performance of 65.6% in area and 48.3% less power consumption.

NMF-Based LncRNA-Disease Association Inference and Bi-Clustering

Long non-coding RNAs (lncRNAs) have been implicated in various biological processes, and are linked in many dysregulations. Researchers have reported large number of lncRNA associated human diseases over the past decade. In this article we employed the Non-negative Matrix Factorization method to develop a low-dimensional computational model that can describe the existing knowledge about lncRNA-disease associations represented in a two dimensional association matrix. The non-negativity constraints of the matrix and its corresponding factors ensure that each lncRNAs disease profile can be represented as an additive linear combination of the latent coordinates. To learn such a constrained model from an incomplete association matrix, several NMF formulations were developed. Based on our experiments, we found that the Sparse NMF obtained the best model among all the other models. Moreover, by exploiting the inherent bi-clustering ability of the NMF models, we extracted several lncRNA groups and disease groups that possess biological significance.

Multi-Block Bipartite Graph for Integrative Genomic Analysis

Human diseases involve a sequence of complex interactions between multiple biological processes. In particular, multiple genomic data such as Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), DNA Methylation (DM), and their interactions simultaneously play an important role in human diseases. However, despite the widely known complex multi-layer biological processes and increased availability of the heterogeneous genomic data, most research has considered only a single type of genomic data. Furthermore, recent integrative genomic studies for the multiple genomic data have also been facing difficulties due to the high-dimensionality and complexity, especially when considering their intra- and inter-block interactions. In this paper, we introduce a novel multi-block bipartite graph and its inference methods, MB2I and sMB2I, for the integrative genomic study. The proposed methods not only integrate multiple genomic data but also incorporate intra/inter-block interactions by using a multi-block bipartite graph. In addition, the methods can be used to predict quantitative traits (e.g., gene expression, survival time) from the multi-block genomic data. The performance was assessed by simulation experiments that implement practical situations. We also applied the method to the human brain data of psychiatric disorders. The experimental results were analyzed by maximum edge biclique and biclustering, and biological findings were discussed.

Robust Inductive Matrix Completion strategy to explore associations between lincRNAs and human disease phenotypes

Long intergenic non-coding RNAs (lincRNAs) are associated with a wide variety of human diseases. Piles of data about the lincRNAs are becoming available, thanks to the High Throughput Sequencing (HTS) platforms, which open opportunity for cutting-edge machine learning and data mining approaches to analyze the disease association better. However, there are only a few in silico association inference tools available to date, and none of them utilizes the heterogeneous data about the lincRNAs and diseases. The standard Inductive Matrix Completion (IMC) technique provides with a platform among the two entities considering respective side information. But, it has two major issues pertaining to the noise and sparsity in the dataset. Thus, a robust version of IMC is needed to adequately address the issues. In this paper, we propose Robust Inductive Matrix Completion (RIMC) to address these challenges. Then, we applied RIMC to the available association dataset between the lincRNAs and OMIM disease phenotypes with a diverse set of side information of the both. The proposed method performs better than the state-of-the-art methods in terms of precision@k and recall@k at the top-k disease prioritization to the subject lincRNAs. Moreover, with an induction experiment we showed that RIMC performs superior than the standard IMC for ranking unexplored disease phenotypes to a set of known lincRNAs.