Cheminformatics approach to identify andrographolide derivatives as dual inhibitors of methyltransferases (nsp14 and nsp16) of SARS-CoV-2.

The Covid-19 pandemic outbreak has accelerated tremendous efforts to discover a therapeutic strategy that targets severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to control viral infection. Various viral proteins have been identified as potential drug targets, however, to date, no specific therapeutic cure is available against the SARS-CoV-2. To address this issue, the present work reports a systematic cheminformatic approach to identify the potent andrographolide derivatives that can target methyltransferases of SARS-CoV-2, i.e. nsp14 and nsp16 which are crucial for the replication of the virus and host immune evasion. A consensus of cheminformatics methodologies including virtual screening, molecular docking, ADMET profiling, molecular dynamics simulations, free-energy landscape analysis, molecular mechanics generalized born surface area (MM-GBSA), and density functional theory (DFT) was utilized. Our study reveals two new andrographolide derivatives (PubChem CID: 2734589 and 138968421) as natural bioactive molecules that can form stable complexes with both proteins via hydrophobic interactions, hydrogen bonds and electrostatic interactions. The toxicity analysis predicts class four toxicity for both compounds with LD50 value in the range of 500-700 mg/kg. MD simulation reveals the stable formation of the complex for both the compounds and their average trajectory values were found to be lower than the control inhibitor and protein alone. MMGBSA analysis corroborates the MD simulation result and showed the lowest energy for the compounds 2734589 and 138968421. The DFT and MEP analysis also predicts the better reactivity and stability of both the hit compounds. Overall, both andrographolide derivatives exhibit good potential as potent inhibitors for both nsp14 and nsp16 proteins, however, in-vitro and in vivo assessment would be required to prove their efficacy and safety in clinical settings. Moreover, the drug discovery strategy aiming at the dual target approach might serve as a useful model for inventing novel drug molecules for various other diseases.

Single-Step Low-Temperature Synthesis of Carbon Dots for Advanced Multiparametric Bioimaging Probe Applications.

Carbon dots (CDs) have recently emerged in biomedical and agricultural domains, mainly for their probe applications in developing efficient sensors. However, the existing high-temperature approaches limit the industrial level scaling up to further translate them into different products by mass-scale fabrication of CDs. To address this, we have attempted to lower the synthesis temperature to 140 °C and synthesized different CDs using different organic acids and their combinations in a one-step approach (quantum yield 3.6% to 16.5%; average size 3 to 5 nm). Further, sensing applications of CDs have been explored in three different biological models, mainly Danio rerio (zebrafish) embryos, bacterial strains, and the Lactuca sativa (lettuce) plant. The 72 h exposure of D. rerio embryos to 0.5 and 1 mg/mL concentrations of CDs exhibited significant uptake without mortality, a 100% hatching rate, and nonsignificant alterations in heart rate. Bacterial bioimaging experiments revealed CD compatibility with Gram-positive (Bacillus subtilis) and Gram-negative (Serratia marcescens) strains without bactericidal effects. Furthermore, CDs demonstrated effective conduction and fluorescence within the vascular system of lettuce plants, indicating their potential as in vivo probes for plant tissues. The single-step low-temperature CD synthesis approach with efficient structural and optical properties enables the process as industrially viable to up-scale the technology readiness level. The bioimaging of CDs in different biological models indicates the possibility of developing a CD probe for diverse biosensing roles in diseases, metabolism, microbial contamination sensing, and more.

Screening 2D Materials for Their Nanotoxicity toward Nucleic Acids and Proteins: An In Silico Outlook.

Since the discovery of graphene, two-dimensional (2D) materials have been anticipated to demonstrate enormous potential in bionanomedicine. Unfortunately, the majority of 2D materials induce nanotoxicity via disruption of the structure of biomolecules. Consequently, there has been an urge to synthesize and identify biocompatible 2D materials. Before the cytotoxicity of 2D nanomaterials is experimentally tested, computational studies can rapidly screen them. Additionally, computational analyses can provide invaluable insights into molecular-level interactions. Recently, various "in silico" techniques have identified these interactions and helped to develop a comprehensive understanding of nanotoxicity of 2D materials. In this article, we discuss the key recent advances in the application of computational methods for the screening of 2D materials for their nanotoxicity toward two important categories of abundant biomolecules, namely, nucleic acids and proteins. We believe the present article would help to develop newer computational protocols for the identification of novel biocompatible materials, thereby paving the way for next-generation biomedical and therapeutic applications based on 2D materials.

Computational Assessment of the Biocompatibility of Two-Dimensional g-C3N3 Toward Lipid Membranes.

One of the most recent additions to the family of two-dimensional (2D) materials, graphitic C3N3 (g-C3N3), has been considered a viable contender for biomedical applications, although its potential toxicity remains elusive. We perform all-atom molecular dynamics simulations to decipher the interactions between model lipid membranes and g-C3N3 as a first step toward exploring the cytotoxicity induced at the nanoscale. We show that g-C3N3 can easily insert into the cellular membranes following a multistage mechanism consisting of simultaneous desolvation of the 2D material along with enrichment of nanomaterial-lipid interactions. Free energy calculations indicate that g-C3N3 is more stable in a membrane-bound state compared to an aqueous solution; however, the insertion of the material does not disturb the structural integrity of lipid membranes. After being inserted into a membrane, g-C3N3 is unlikely to be released into the cellular environment and is incapable of extracting lipid molecules from the membrane. The nature of interaction between the 2D material and membranes is found to be independent of the nanomaterial size. Also, the performance of g-C3N3 toward biomolecular delivery is shown to be significantly improved compared to the state-of-the-art 2D materials graphene and hexagonal boron nitride (h-BN). It is revealed that, the affinity of g-C3N3 toward lipid membranes is weaker compared to the nanotoxic graphene and h-BN, while being marginally higher than h2D-C2N, which in turn, increases the biocompatibility of the material, thereby brightening its future as a noncytotoxic material for forthcoming biomedical applications.

MEM-FET: Essential protein prediction using membership feature and machine learning approach.

Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab. The results of observation in wet lab always ensure high reliability and accuracy in the biological ground. Essential protein prediction using computational approaches is an alternative choice in research. It proves its significance rapidly in day-to-day life as well as reduces the experimental cost of wet lab effectively. Existing computational methods were implemented using Protein interaction networks (PPIN), Sequence, Gene Expression Dataset (GED), Gene Ontology (GO), Orthologous groups, and Subcellular localized datasets. Machine learning has diverse categories of features that enable to model and predict essential macromolecules of understudied organisms. A novel methodology MEM-FET (membership feature) is predicted based on features, that is, edge clustering coefficient, Average clustering coefficient, subcellular localization, and Gene Ontology within a compartment of common neighbors. The accuracy (ACC) values of the predicted true positive (TP) essential proteins are 0.79, 0.74, 0.78, and 0.71 for YHQ, YMIPS, YDIP, and YMBD datasets. An enriched set of essential proteins are also predicted using the MEM-FET algorithm. Ensemble ML also validated the proposed model with an accuracy of 60%. It has been predicted that MEM-FET algorithms outperform other existing algorithms with an ACC value of 80% for the yeast dataset.

Impact of time varying interaction: Formation and annihilation of extreme events in dynamical systems.

This study investigates the emergence of extreme events in two different coupled systems: the FitzHugh-Nagumo neuron model and the forced Liénard system, both based on time-varying interactions. The time-varying coupling function between the systems determines the duration and frequency of their interaction. Extreme events in the coupled system arise as a result of the influence of time-varying interactions within various parameter regions. We specifically focus on elucidating how the transition point between extreme events and regular events shifts in response to the duration of interaction time between the systems. By selecting the appropriate interaction time, we can effectively mitigate extreme events, which is highly advantageous for controlling undesired fluctuations in engineering applications. Furthermore, we extend our investigation to networks of oscillators, where the interactions among network elements are also time dependent. The proposed approach for coupled systems holds wide applicability to oscillator networks.

Supramolecular Barrel-Rosette Ion Channel Based on 3,5-Diaminobenzoic Acid for Cation-Anion Symport.

The structural tropology and functions of natural cation-anion symporting channels have been continuously investigated due to their crucial role in regulating various physiological functions. To understand the physiological functions of the natural symporter channels, it is vital to develop small-molecule-based biomimicking systems that can provide mechanistic insights into the ion-binding sites and the ion-translocation pathways. Herein, we report a series of bis((R)-(-)-mandelic acid)-linked 3,5-diaminobenzoic acid based self-assembled ion channels with distinctive ion transport ability. Ion transport experiment across the lipid bilayer membrane revealed that compound 1 b exhibits the highest transport activity among the series, and it has interesting selective co-transporting functions, i.e., facilitates K+ /ClO4 - symport. Electrophysiology experiments confirmed the formation of supramolecular ion channels with an average diameter of 6.2±1 Šand single channel conductance of 57.3±1.9 pS. Selectivity studies of channel 1 b in a bilayer lipid membrane demonstrated a permeability ratio of P C l - / P K + = 0 . 053 ± 0 . 02 ${{P}_{{Cl}^{-}}/{P}_{{K}^{+}}=0.053\pm 0.02}$ , P C l O 4 - / P C l - = 2 . 1 ± 0 . 5 ${{P}_{{ClO}_{4}^{-}}/{P}_{{Cl}^{-}}=2.1\pm 0.5}$ , and P K + / P N a + = 1 . 5 ± 1 , ${{P}_{{K}^{+}}/{P}_{{Na}^{+}}=1.5\pm 1,}$ indicating the higher selectivity of the channel towards KClO4 over KCl salt. A hexameric assembly of a trimeric rosette of 1 b was subjected to molecular dynamics simulations with different salts to understand the supramolecular channel formation and ion selectivity pattern.

Self-assembled prebiotic amphiphile-mixture exhibits tunable catalytic properties.

Protocellular surface formation via the self-assembly of amphiphiles, and catalysis by simple peptides/proto-RNA are two important pillars in the evolution of protocells. To hunt for prebiotic self-assembly-supported catalytic reactions, we thought that amino-acid-based amphiphiles might play an important role. In this paper, we investigate the formation of histidine-based and serine-based amphiphiles under mild prebiotic conditions from amino acid : fatty alcohol and amino acid : fatty acid mixtures. The histidine-based amphiphiles were able to catalyze hydrolytic reactions at the self-assembled surface (with a rate increase of ∼1000-fold), and the catalytic ability can be tuned by linkage of the fatty carbon part to histidine (N-acylated vs. O-acylated). Moreover, the presence of cationic serine-based amphiphiles on the surface enhances the catalytic efficiency by another ∼2-fold, whereas the presence of anionic aspartic acid-based amphiphiles reduces the catalytic activity. Ester partitioning into the surface, reactivity, and the accumulation of liberated fatty acid explain the substrate selectivity of the catalytic surface, where the hexyl esters were found to be more hydrolytic than other fatty acyl esters. Di-methylation of the -NH2 of OLH increases the catalytic efficacy by a further ∼2-fold, whereas trimethylation reduces the catalytic ability. The self-assembly, charge-charge repulsion, and the H-bonding to the ester carbonyl are likely to be responsible for the superior (∼2500-fold higher rate than the pre-micellar OLH) catalytic efficiency of O-lauryl dimethyl histidine (OLDMH). Thus, prebiotic amino-acid-based surfaces served as an efficient catalyst that exhibits regulation of catalytic function, substrate selectivity, and further adaptability to perform bio-catalysis.

Numerical analysis of predatory potentiality of Toxorhynchites splendens against larval Aedes albopictus in laboratory and semi-field conditions.

Larvae of the elephant mosquitoes, Toxorhynchites spp. (Diptera: Culicidae) are predacious on larvae of other mosquito species and some small aquatic organisms; this predatory behavior can be applied in (mosquito) vector control. The present study examined the feeding behavior of Toxorhynchites splendens on Aedes albopictus in relation to search area [volume of water (X1)] and prey density (X2), prey instars, predatory preference and larvae's functional response on variable prey densities. Experiments were conducted to determine changes in the feeding activity of T. splendens with different search areas and showed that rate of prey consumption was inversely proportional to the search area as evidenced by a negative value of X1 in the regression equation and positively related to prey density. The non-linear polynomial logistic regression estimated a significant linear parameter (P1 < 0) for the functional response analysis suggesting a Type II functional response. Differences in feeding response related to the different combinations of prey instars were statistically not significant (p > 0.05), expressing that all the instars of prey were equally susceptible to the predator. Toxorhynchites splendens preferred to consume Ae. albopictus larvae rather than Tubifex when supplied together as a food source.

MM-CCNB: Essential protein prediction using MAX-MIN strategies and compartment of common neighboring approach.

BACKGROUND AND OBJECTIVE: Proteins are indispensable for the flow of the life of living organisms. Protein pairs in interaction exhibit more functional activities than individuals. These activities have been considered an essential measure in predicting their essentiality. Neighborhood approaches have been used frequently in the prediction of essentiality scores. All paired neighbors of the essential proteins are nominated for the suitable candidate seeds for prediction. Still now Jaccard's coefficient is limited to predicting functions, homologous groups, sequence analysis, etc. It really motivate us to predict essential proteins efficiently using different computational approaches. METHODS: In our work, we proposed modified Jaccard's coefficient to predict essential proteins. We have proposed a novel methodology for predicting essential proteins using MAX-MIN strategies and modified Jaccard's coefficient approach. RESULTS: The performance of our proposed methodology has been analyzed for Saccharomyces cerevisiae datasets with an accuracy of more than 80%. It has been observed that the proposed algorithm is outperforms with an accuracy of 0.78, 0.74, 0.79, and 0.862 for YDIP, YMIPS, YHQ, and YMBD datasets respectivly. CONCLUSIONS: There are several computational approaches in the existing state-of-art model of essential protein prediction. It has been noted that our predicted methodology outperforms other existing models viz. different centralities, local interaction density combined with protein complexes, modified monkey algorithm and ortho_sim_loc methods.

Two dimensional materials are non-nanotoxic and biocompatible towards cyclotides: evidence from classical molecular dynamics simulations.

Cyclotides are backbone-cyclized peptides of plant origin enriched with disulfide bonds, having exceptional stability towards thermal denaturation and proteolytic degradation. They have a plethora of activities like antibacterial, antifungal, anti-tumor and anti-HIV properties predominantly owing to their selective interaction with certain phospholipids, thereby leading to the disruption of cellular membranes. On the other hand, low-dimensional materials like graphene and hexagonal boron nitride (h-BN) are also known to show membrane-proliferating activities through lipid extraction. A plausible and more effective antibacterial, anti-tumor and antifungal agent would be a composite of these 2D materials and cyclotides, provided the structures of the peptides remain unperturbed upon adsorption and interaction. In this study, classical molecular dynamics simulations are performed to understand the nature of adsorption of cyclotides belonging to different families on graphene and h-BN and analyze the resulting structural changes. It is revealed that, due to their exceptional structural stability, cyclotides maintain their structural integrity upon adsorption on the 2D materials. In addition, the aggregated states of the cyclotides, which are ubiquitous in plant organs, are also not disrupted upon adsorption. Extensive free energy calculations show that the adsorption strength of the cyclotides is moderate in comparison to those of other similar-sized biomolecules, and the larger the size of the aggregates, the weaker the binding of individual peptides with the 2D materials, thereby leading to their lower release times from the materials. It is predicted that graphene and h-BN may safely be used for the preparation of composites with cyclotides, which in turn may be envisaged to be probable candidates for manufacturing next-generation bionano agents for agricultural, antibacterial and therapeutic applications.

Occasional coupling enhances amplitude death in delay-coupled oscillators.

This paper aims to study amplitude death in time delay coupled oscillators using the occasional coupling scheme that implies intermittent interaction among the oscillators. An enhancement of amplitude death regions (i.e., an increment of the width of the amplitude death regions along the control parameter axis) can be possible using the occasional coupling in a pair of delay-coupled oscillators. Our study starts with coupled limit cycle oscillators (Stuart-Landau) and coupled chaotic oscillators (Rössler). We further examine coupled horizontal Rijke tubes, a prototypical model of thermoacoustic systems. Oscillatory states are highly detrimental to thermoacoustic systems such as combustors. Consequently, a state of amplitude death is always preferred. We employ the on-off coupling (i.e., a square wave function), as an occasional coupling scheme, to these coupled oscillators. On monotonically varying the coupling strength (as a control parameter), we observe an enhancement of amplitude death regions using the occasional coupling scheme compared to the continuous coupling scheme. In order to study the contribution of the occasional coupling scheme, we perform a detailed linear stability analysis and analytically explain this enhancement of the amplitude death region for coupled limit cycle oscillators. We also adopt the frequency ratio of the oscillators and the time delay between the oscillators as the control parameters. Intriguingly, we obtain a similar enhancement of the amplitude death regions using the frequency ratio and time delay as the control parameters in the presence of the occasional coupling. Finally, we use a half-wave rectified sinusoidal wave function (motivated by practical reality) to introduce the occasional coupling in time delay coupled oscillators and get similar results.

Ferroelectricity in a hydrogen-bonded alternating donor-acceptor supramolecular copolymer.

This communication reports synergistic H-bonding and charge-transfer (CT) interaction-promoted alternating supramolecular copolymerization of amide-functionalized pyrene (Py) and naphthalene-diimide (NDI) building blocks and the emergence of ferroelectricity with saturation polarization ∼3.2 µC cm-2, Curie temperature ∼304 K, and coercive field ∼8.5 kV cm-1 at 100 Hz. The Py or NDI molecules on their own do not exhibit any ferroelectric hysteresis, indicating an essential role of both CT-interaction and H-bonding in ferroelectricity. Computational studies provide insight into the origin of the polarization and the importance of the NDI/Py ratio. This study, showing room temperature ferroelectricity in purely organic systems, is of high relevance for flexible electronics and sensors. It opens up new opportunities for soft FE-materials with ample scope for further structural optimization.

Anticipating synchrony in dynamical systems using information theory.

Synchronization in coupled dynamical systems has been a well-known phenomenon in the field of nonlinear dynamics for a long time. This phenomenon has been investigated extensively both analytically and experimentally. Although synchronization is observed in different areas of our real life, in some cases, this phenomenon is harmful; consequently, an early warning of synchronization becomes an unavoidable requirement. This paper focuses on this issue and proposes a reliable measure ( R), from the perspective of the information theory, to detect complete and generalized synchronizations early in the context of interacting oscillators. The proposed measure R is an explicit function of the joint entropy and mutual information of the coupled oscillators. The applicability of R to anticipate generalized and complete synchronizations is justified using numerical analysis of mathematical models and experimental data. Mathematical models involve the interaction of two low-dimensional, autonomous, chaotic oscillators and a network of coupled Rössler and van der Pol oscillators. The experimental data are generated from laboratory-scale turbulent thermoacoustic systems.

Platinum nanoparticle sensitized plasmonic-enhanced broad spectral photodetection in large area vertical-aligned MoS2flakes.

2D MoS2holds immense potential for electronic and optoelectronic applications due to its unique characteristics. However, the atomic-scale thickness of MoS2hinders the optical absorbance, thereby limiting its photodetection capability. Vertically-aligned MoS2(VA-MoS2) has an advantage of strong optical absorption and quick intra-layer transport, offering high speed operation. The coupling of plasmonic metal nanostructure with MoS2can further enhance the light-matter interaction. Pt/Pd (as opposed to Ag/Au) are more promising to design next-generation nano-plasmonic devices due to their intense interband activity over a broad spectral range. Herein, we report Pt nanoparticle (NPs) enhanced broadband photoresponse in VA-MoS2. The optical absorbance of MoS2is enhanced after the integration of Pt NPs, with a four-fold enhancement in photocurrent. The formation of Schottky junction at Pt-MoS2interface inhibits electron transmission, suppressing the dark current and substantially reducing NEP. The plasmonic-enabled photodetector shows enhanced responsivity (432 A W-1, 800 nm) and detectivity (1.85 × 1014Jones, 5 V) with a low response time (87 ms/84 ms), attributed to faster carrier transport. Additionally, a theoretical approach is adopted to calculate wavelength-dependent responsivity, which matches well with experimental results. These findings offer a facile approach to modulate the performance of next-generation optoelectronic devices for practical applications.

A Hierarchical (Macro)molecular Assembly Assisted by Donor-Acceptor Charge-Transfer Interactions Exhibiting Room-Temperature Ferroelectricity.

This communication reveals co-assembly of an electron-deficient naphthalene-diimide (NDI)-appended polyurethane (P1) and electron-rich pyrene (Py), forming an organogel with prominent room-temperature ferroelectricity. In a non-polar medium, intra-chain hydrogen-bonding among the urethane groups of P1 produces a folded structure with an array of the NDIs in the periphery, which intercalate Py by charge-transfer (CT)-interaction. Such CT-complexation enables slow crystallization of the peripheral hydrocarbons, causing gelation with nanotubular morphology, in which the wall consists of the alternating NDI-Py stack. Such D-A assembly exhibits ferroelectricity (saturation polarization Ps ≈0.8 µC cm-2 and coercive field Ec ≈8 kV cm-1 at 500 V and 10 Hz frequency) with Curie temperature (Tc ) of ≈350 K, which can be related to the disassembly of the CT-complex. In the absence of Py, P1 forms spherical aggregates, showing dielectric behaviour.

Stable room temperature ferroelectricity in hydrogen-bonded supramolecular assemblies of ambipolar π-systems.

This article reports H-bonding driven supramolecular polymerization of naphthalimide (A)-thiophene (D)-naphthalimide (A) (AD n A, n = 1-4) conjugated ambipolar π-systems and its remarkable impact on room temperature ferroelectricity. Electrochemical studies confirm the ambipolar nature of these AD n A molecules with the HOMO-LUMO gap varying between 2.05 and 2.29 eV. Electron density mapping from ESP calculations reveals intra-molecular charge separation as typically observed in ambipolar systems. In the aggregated state, AD1A and AD2A exhibit bathochromically shifted absorption bands while AD3A and AD4A show typical H-aggregation with a hypsochromic shift. Polarization vs. electric field (P-E) measurements reveal stable room temperature ferroelectricity for these supramolecular assemblies, most prominent for the AD2A system, with a Curie temperature (T c) ≈ 361 K and saturation polarization (P s) of ∼2 µC cm-2 at a rather low coercive field of ∼2 kV cm-1. Control molecules, lacking either the ambipolar chromophore or the amide functionality, do not show any ferroelectricity, vindicating the present molecular and supramolecular design. Computational studies enable structural optimization of the stacked oligomer(s) of AD2A molecules and reveal a significant increase in the macro-dipole moment (in the range of 10-12 Debye) going from the monomer to the oligomer(s), which provides the rationale for the origin of ferroelectricity in these supramolecular polymers.

ENResNet: A novel residual neural network for chest X-ray enhancement based COVID-19 detection.

Recently, people around the world are being vulnerable to the pandemic effect of the novel Corona Virus. It is very difficult to detect the virus infected chest X-ray (CXR) image during early stages due to constant gene mutation of the virus. It is also strenuous to differentiate between the usual pneumonia from the COVID-19 positive case as both show similar symptoms. This paper proposes a modified residual network based enhancement (ENResNet) scheme for the visual clarification of COVID-19 pneumonia impairment from CXR images and classification of COVID-19 under deep learning framework. Firstly, the residual image has been generated using residual convolutional neural network through batch normalization corresponding to each image. Secondly, a module has been constructed through normalized map using patches and residual images as input. The output consisting of residual images and patches of each module are fed into the next module and this goes on for consecutive eight modules. A feature map is generated from each module and the final enhanced CXR is produced via up-sampling process. Further, we have designed a simple CNN model for automatic detection of COVID-19 from CXR images in the light of 'multi-term loss' function and 'softmax' classifier in optimal way. The proposed model exhibits better result in the diagnosis of binary classification (COVID vs. Normal) and multi-class classification (COVID vs. Pneumonia vs. Normal) in this study. The suggested ENResNet achieves a classification accuracy 99.7 % and 98.4 % for binary classification and multi-class detection respectively in comparison with state-of-the-art methods.

Ortho_Sim_Loc: Essential protein prediction using orthology and priority-based similarity approach.

Proteins are the essential macro-molecules of living organism. But all proteins cannot be considered as essential in different relevant studies. Essentiality of a protein is thus computed by computation methods rather than biological experiments which in turn save both time and effort. Different computational approaches are already predicted to select essential proteins successfully with different biological significances by researchers. Most of the experimental approaches return higher false negative outcomes with respect to others. In order to retain the prediction accuracy level, a novel methodology "Ortho_Sim_Loc"has been proposed which is a combined approach of Orthology, Similarity (using clustering and priority based GO-Annotation) and Subcellular localization. Ortho_Sim_Loc can predict enriched functional set essential proteins. The predicted results are validated with other existing methods like different centrality measures, LIDC. The validation results exhibits better performance of Ortho_Sim_Loc in compare to other existing computational approaches.

Identifying the degree of genetic interactions using Restricted Boltzmann Machine-A study on colorectal cancer.

The phenomenon of two or more genes affecting the expression of each other in various ways in the development of a single character of an organism is known as gene interaction. Gene interaction not only applies to normal human traits but to the diseased samples as well. Thus, an analysis of gene interaction could help us to differentiate between the normal and the diseased samples or between the two/more phases any diseased samples. At the first stage of this work we have used restricted Boltzmann machine model to find such significant interactions present in normal and/or cancer samples of every gene pairs of 20 genes of colorectal cancer data set (GDS4382) along with the weight/degree of those interactions. Later on, we are looking for those interactions present in adenoma and/or carcinoma samples of the same 20 genes of colorectal cancer data set (GDS1777). The weight/degree of those interactions represents how strong/weak an interaction is. At the end we will create a gene regulatory network with the help of those interactions, where the regulatory genes are identified by using Naïve Bayes Classifier. Experimental results are validated biologically by comparing the interactions with NCBI databases.