Graphical abstract

In this letter, a surface plasmon resonance (SPR) biosensor is numerically investigated that used Graphene-MoS2-Au-TiO2-SiO2 hybrid structure for the detection of formalin. This developed sensor sensed the presence of formalin based on attenuated total reflection (ATR) method by observing the change of "surface plasmon resonance (SPR) angle versus the change of minimum reflectance" attributor and "the surface plasmon resonance frequency (SPRF) versus maximum transmittance" attributor. Chitosan is used as probe legend to perform particular reaction with the formalin (formaldehyde) as target legend. Here, graphene as well as MoS2 are used as biomolecular recognition element (BRE), TiO2-SiO2 bilayer as the improvement of sensitivity and Gold (Au) as the sharp SPR curve. Numerical results are appeared that the variation of SPRF and SPR angle for improper sensing of formalin is quite negligible that confirms absence of formalin whereas for proper sensing is considerably countable that confirms the presence of formalin. It is also shown that the sensitivity of conventional SPR sensor is 70.74% and the graphene-MoS2-based sensor is enhanced to 77% with respect conventional SPR sensor. The sensitivity is further enhanced to 79 % by including TiO2-SiO2 composite layer with respect to conventional SPR sensor. At the end of this letter, a comparative study of the sensitivity of the proposed work with the existing works is discussed.

Motivation Chemical–genetic interaction profiling is a genetic approach that quantifies the susceptibility of a set of mutants depleted in specific gene product(s) to a set of chemical compounds. With the recent advances in artificial intelligence, chemical–genetic interaction profiles (CGIPs) can be leveraged to predict mechanism of action of compounds. This can be achieved by using machine learning, where the data from a CGIP is fed into the machine learning platform along with the chemical descriptors to develop a chemogenetically trained model. As small molecules can be considered non-structural data, graph convolutional neural networks, which can learn from the chemical structures directly, can be used to successfully predict molecular properties. Clustering analysis, on the other hand, is a critical approach to get insights into the underlying biological relationships between the gene products in the high-dimensional chemical-genetic data. Methods and results In this study, we proposed a comprehensive framework based on the large-scale chemical-genetics dataset built in Mycobacterium tuberculosis for predicting CGIPs using graph-based deep learning models. Our approach is structured into three parts. First, by matching M. tuberculosis genes with homologous genes in Escherichia coli (E. coli) according to their gene products, we grouped the genes into clusters with distinct biological functions. Second, we employed a directed message passing neural network to predict growth inhibition against M. tuberculosis gene clusters using a collection of 50,000 chemicals with the profile. We compared the performance of different baseline models and implemented multi-label tasks in binary classification frameworks. Lastly, we applied the trained model to an externally curated drug set that had experimental results against M. tuberculosis genes to examine the effectiveness of our method. Overall, we demonstrate that our approach effectively created M. tuberculosis gene clusters, and the trained classifier is able to predict activity against essential M. tuberculosis targets with high accuracy. Conclusion This work provides an analytical framework for modeling large-scale chemical-genetic datasets for predicting CGIPs and generating hypothesis about mechanism of action of novel drugs. In addition, this work highlights the importance of graph-based deep neural networks in drug discovery.

Computational analysis of high-throughput omics data, such as gene expressions, copy number alterations and DNA methylation (DNAm), has become popular in disease studies in recent decades because such analyses can be very helpful to predict whether a patient has certain disease or its subtypes. However, due to the highdimensional nature of the data sets with hundreds of thousands of variables and very small number of samples, traditional machine learning approaches, such as support vector machines (SVMs) and random forests, have limitations to analyze these data efficiently. In this chapter, we reviewed the progress in applying deep learning algorithms to solve some biological questions. The focus is on potential software tools and public data sources for the tasks. Particularly, we show some case studies using deep neural network (DNN) models for classifying molecular subtypes of breast cancer and DNN-based regression models to account for interindividual variation in triglyceride concentrations measured at different visits of peripheral blood samples using DNAm profiles. We show that integration of multi-omics profiles into DNN-based learning methods could improve the prediction of the molecular subtypes of breast cancer. We also demonstrate the superiority of our proposed DNN models over the SVM model for predicting triglyceride concentrations.

This article illustrates a design and finite difference time domain (FDTD) method based on analysis of fiber optic surface plasmon resonance (SPR) biosensor for biomedical application especially for DNA-DNA hybridization. The fiber cladding at the middle portion is constructed with the proposed hybrid of gold (Au), graphene, and a sensing medium. This sensor can be recognized adsorption of DNA biomolecules onto sensing medium of PBS saline using attenuated total reflection (ATR) technique. The refractive index (RI) is varied owing to the adsorption of different concentration of biomolecules.  Result states that the sensitivity with a monolayer of graphene will be improved up to 40% than bare graphene layer. Owing to increased adsorption capability of DNA molecules on graphene, sensitivity increases compared to the conventional gold thin film SPR biosensor. Numerical analysis shows that the variation of the SPR angle for mismatched DNA strands is quite negligible, whereas that for complementary DNA strands is considerable, which is essential for proper detection of DNA hybridization.  Finally, the effect of Electric field distribution on inserting graphene layer is analyzed incorporating the FDTD technique by using Lumerical FDTD solution software.