Genes act as the blueprint for regulating all activities of a living system. Genes produce proteins, which in turn, sit on the promoter regions of other genes to regulate their activity. Thus, a gene regulatory network is formed. This network is critical in disclosing the various mysteries in the operations of living systems. Often it is very difficult to find these networks in the Wet Lab. As a result, various computational approaches have been used to reconstruct these networks from gene ex-pression data. The techniques primarily used for this purpose include Bayesian networks, Boolean networks, recurrent neural networks, S-systems, and mutual information based methods. The contemporary literature indicates that these techniques often fail to reliably reconstruct real-life networks. In this paper, we have proposed a new technique based on a modified recurrent neural network strategy that is augmented by mutual information. The proposed methodology has been implemented on an 8-gene network of Escherichia coli and a lO-gene network, which have been extensively used by other researchers. The experimental results indicate that the proposed technique achieves satisfactory results when compared to other such techniques developed by contemporary researchers.

The revolution in sequencing technologies has enabled human genomes to be sequenced at a very low cost and time leading to exponential growth in the availability of whole-genome sequences. However, the complete understanding of our genome and its association with cancer is a far way to go. Researchers are striving hard to detect new variants and find their association with diseases, which further gives rise to the need for aggregation of this Big Data into a common standard scalable platform. In this work, a database named Enlightenment has been implemented which makes the availability of genomic data integrated from eight public databases, and DNA sequencing profiles of H. sapiens in a single platform. Annotated results with respect to cancer specific biomarkers, pharmacogenetic biomarkers and its association with variability in drug response, and DNA profiles along with novel copy number variants are computed and stored, which are accessible through a web interface. In order to overcome the challenge of storage and processing of NGS technology-based whole-genome DNA sequences, Enlightenment has been extended and deployed to a flexible and horizontally scalable database HBase, which is distributed over a hadoop cluster, which would enable the integration of other omics data into the database for enlightening the path towards eradication of cancer.

Purpose: The main objective of this study is to propose a Keystroke dynamics (KD) based Parkinson’s disease (PD) indicator using both fixed-text and free-text typing habits on conventional keyboards and homogeneous ensemble learning with several fully balanced bootstrapped training sets for more accurate and robust eHealth applications. Furthermore, this study addresses the six key hypotheses related to our main objective of understanding the risks in this screening process that were previously unknown but important for further improvement. Methods: For a predetermined window length, the Colin Bannard and neuroQWERTY MIT-CSXPD datasets were used to extract a series of key hold times and the time gaps between two consecutive presses and releases as a feature set. A wide range of statistical tools were employed to create a machine learning-ready feature arrangement for continuously generated patterns. A homogeneous ensemble learning approach was then developed using bootstrapping and under-sampling while retaining any rare samples. This model was validated using fifteen fixed-text and free-text inputs obtained from early-stage PD patients, De-novo PD patients, and healthy controls. Results: In the leave-one-user-out cross-validation (LOUOCV) evaluation, the maximum observed area under curve (AUC) is 85.99% ± 0.41 for fixed-text input. However, for free-text inputs, AUC is 78.3% ± 0.86, with a sensitivity/specificity of 74.46%/82.13%. The AUC for detecting De-novo patients is 79.83% ± 1.26, which is somewhat lower than the AUC for determining the early stage of the disease, which is 83.81% ± 0.83. Conclusion: The proposed model is more robust, usable (covert way of data acquisition), fast, and has ease of integration into conventional desktops/laptops suitable for real-life eHealth that could help for better diagnosis, early detection in a home environment, future reference, and treatment or therapy management. However, the subject size, severity levels of the disease, typing duration, feature composition, error in typing, and machine learning (ML) method selection influence the performance of this model. Therefore, careful attention is necessary while designing PD indicators using the proposed approach.

In this paper, we have proposed a stochastic Knapsack Problem (KP) based mathematical model for small-scale vegetable sellers in India and solved it by an advanced Genetic Algorithm. The knapsack problem considered here is a bounded one, where vegetables are the objects. In this model, we have assumed that different available vegetables (objects) have different weights (that are available), purchase costs, and profits. The maximum weight of vegetables that can be transported by a seller is limited by the carrying capacity of the vegetable carrier and the business capital of the seller is also limited. The aim of the proposed mathematical model is to maximize the total profit of the loaded/traded items, with a set of predefined constraints on the part of the vegetable seller or retailer. This problem has been solved in a Type-2 fuzzy environment and the Critical Value (CV) reduction method is utilized to defuzzify the objective value. We have projected an improved genetic algorithm based approach, where we have incorporated two features, namely refinement and immigration. We have initially considered benchmark instances and subsequently some redefined cases for experimentation. Moreover, we have solved some randomly generated proposed KP instances in Type-2 fuzzy environment.

Gene regulatory networks are biologically robust, which imparts resilience to living systems against most external perturbations affecting them. However, there is a limit to this and disturbances beyond this limit can impart unwanted signalling on one or more master regulators in a network. Certain disturbances may affect the functioning of other constituent genes of the same network. In most cases, this phenomenon can have some effect on the functioning of the living organism. In this investigation, we have proposed a methodology to mitigate the effects of external perturbations on a genetic network using a proportional-integral-derivative controller. The proposed controller has been used to perturb one or more of the other unaffected master regulators such that the most affected gene/s of the network revert to their normal state. The only required condition of such type of manoeuvring is that there should be multiple master regulators in a network. The proposed technique has been experimented on a 10-gene DREAM4 benchmark network and also on a larger 20-gene network, where only downregulation has been considered due to data constraints. Simulation results indicate that the most vulnerable genes can be reverted to their normal expression levels in 10 out of the 16 simulations performed.

Identifying intragenic as well as intergenic sequences of the DNA, having structural alterations, is a significantly important research area, since this may be the root cause of many neurological and autoimmune diseases, including cancer. Working with whole genome NGS data has provided a new insight in this regard, but has lead to huge explosion of data that is growing exponentially. Hence, the challenges lie in efficient means of storage and processing this big data. In this study, we have developed a novel segmentation algorithm, called GenSeg, and its parallel MapReduce based algorithm, called MR-GenSeg, for detecting copy number variations. In order to annotate CNVs (variants), segments formed by GenSeg/MR-GenSeg have been represented in a novel way using a binary tree, where each node is a CNV event. GenSeg considers each position specific data of whole genome DNA sequence, so that precise identification of breakpoints is possible. GenSeg/MR-GenSeg has been compared with twelve popular CNV detection algorithms, where it has outperformed the others in terms of sensitivity, and has achieved a good F-score value. MR-GenSeg has excelled in terms of SpeedUp, when compared with these algorithms. The effect of CNVs on immunoglobulin (IG) genes has also been analysed in this study. Availability: The source codes are available at https://github.com/rituparna-sinha/MapReduce-GENSEG.

In this work, a computational approach has been proposed based on the hybridisation of two modelling formalisms, recurrent neural networks and half-systems, for the reconstruction of gene regulatory networks from time-series gene expression datasets. To the best of our knowledge, the proposed hybridisation has not been attempted previously in this domain. Here, recurrent neural networks and half-systems have been hybridised to capture the underlying dynamics present in the temporal gene expression profiles. The motivation behind this work is to integrate the advantages of both the techniques in the proposed model such that the problem of reverse engineering of gene regulatory networks can be resolved more efficiently. Artificial bee colony optimisation has been used for the estimation of the model parameters. We have implemented the proposed hybrid methodology on the real-world experimental datasets (in vivo) of the SOS DNA Repair network of Escherichia coli. The obtained results are comparable to or better than that of other reverse engineering methodologies present in contemporary literature.

Gene regulatory networks are generally robust in nature. However, unwanted perturbations arising out of extreme environmental conditions or external pathogen attacks may lead them to malfunction. Potentially, this can have an adverse effect on the biochemical functions of a living system. In this work, we have proposed a computational model based on negative feedback control to eliminate the effects of such unwanted perturbations. We have implemented the recurrent neural network formalism for modelling the underlying network dynamics from a given time-series gene expression dataset. The artificial bee colony optimisation technique has been employed for model parameter estimation. The controller used in this work is of the proportional-integral-derivative type. To the best of our knowledge, this is one of the first research works in this domain to consider a completely non-linear scenario. A 10-gene DREAM4 benchmark network has been considered in this work. The results obtained herein show that the proposed formalism can mitigate the unwanted effects of external disturbances effectively.

The accurate reconstruction of gene regulatory networks for proper understanding of the intricacies of complex biological mechanisms still provides motivation for researchers. Due to accessibility of various gene expression data, we can now attempt to computationally infer genetic interactions. Among the established network inference techniques, S-system is preferred because of its efficiency in replicating biological systems though it is computationally more expensive. This provides motivation for us to develop a similar system with lesser computational load. In this work, we have proposed a novel methodology for reverse engineering of gene regulatory networks based on a new technique: half-system. Half-systems use half the number of parameters compared to S-systems and thus significantly reduce the computational complexity. We have implemented our proposed technique for reconstructing four benchmark networks from their corresponding temporal expression profiles: an 8-gene, a 10-gene, and two 20-gene networks. Being a new technique, to the best of our knowledge, there are no comparable results for this in the contemporary literature. Therefore, we have compared our results with those obtained from the contemporary literature using other methodologies, including the state-of-the-art method, GENIE3. The results obtained in this work stack favourably against the competition, even showing quantifiable improvements in some cases.

Paper-based digital microfluidic biochips (or P-DMFBs) are becoming highly impelling due to its low-cost and in-place fabrication of electrodes and control wiring on a single piece of paper having an inkjet printer and conductive ink. Despite enormous advantages, several complex design rules also subsist, such as avoidance of induced control interference, minimum separation among the control lines, and congestion-free wiring on a single layer, which is to be correlated leading toward overall feasibility of the design. Several cost raising issues, such as schedule length, control pin count, and wire length, must be considered for attaining a successful fluid-control codesign. Moreover, design gaps exist among the subtasks of the fluid level, control level, and fluid-control design as a whole, which undeniably impose expensive design cycles increasing overall cost. This article builds a machine learning-based model for the pin-constrained P-DMFBs to predict violation in control design beforehand and accordingly guides the fluid-control codesign to tackle important cost-driving issues while attaining congestion- and conflict-free wiring. This model effectively eliminates the design cycles producing a low-cost platform. The predictive model has been evaluated over a balanced data set. Several benchmarks for assessing the performance are studied.

The texture classification is a significant problem in the area of pattern recognition. This work proposes a novel Affine Differential Local Mean ZigZag Pattern (ADLMZP) descriptor for texture classification. The proposed method has two manifolds: first Local Mean ZigZag Pattern (LMZP) map is calculated by thresholding the 3 × 3 patch neighbor intensity values with respect to path mean but in a ZigZag weighting fashion, which provides a well discriminated descriptor compared to other local binary descriptors. The local micropattern is obtained by comparing neighbor intensity values with respect to path mean which makes the descriptor robust against noise and illumination variations. Secondly, in order to make it invariant to affine changes, we incorporated an affine differential transformation along with affine gradient magnitude information of a texture image which is differed from Euclidean Gradient. The final ADLMZP descriptor is generated by concatenating the histograms of all Affine Differential Local Mean ZigZag maps. The results are computed over well known KTH-TIPS, Brodatz, and CUReT texture datasets and compared with the state-of-the-art texture classification methods.

A gene regulatory network reveals the regulatory relationships among genes at a cellular level. The accurate reconstruction of such networks using computational tools, from time series genetic expression data, is crucial to the understanding of the proper functioning of a living organism. Investigations in this domain focused mainly on the identification of as many true regulations as possible. This has somewhat overshadowed the reduction of false predictions in inferred networks. In the present investigation, we have proposed a novel scheme, based on different swarm intelligence algorithms, to reduce the number of inferred false regulations. We have first applied our proposed methodology on the much studied, benchmark experimental datasets of the DNA SOS repair network of Escherichia Coli. Subsequently, we have experimented upon a larger, in silico network extracted from the GeneNetWeaver database. The obtained results suggest that the proposed methodology can reduce the number of false predictions, significantly, without using any supplementary biological information for larger gene regulatory networks.