Marvels of Artificial and Computational Intelligence in Life Sciences

Artificial intelligence (AI) is a branch of science that mainly deals with computers. It can store massive data through built-in programs that can accumulate the required data and convert it into intellectual actions with a reason. In recent years, AI has played a vital role in various governmental and non-governmental sectors such as engineering, medicine and economics. The development of AI in the field of infectious diseases is colossal with a spectrum of applications including pathogen detection, public health surveillance, cellular pathways and biomolecules in host-pathogen interactions, drug discovery and vaccine development. Similarly, early detection is the key to controlling any disease outbreak. Systematic collection and analysis of data will yield vital data on the required tools for controlling the outbreak situation. The antibiotic stewardship program is being implemented in very few healthcare institutions due to its intense cost and work. AI is used for tackling the rise in antibiotic use and developing an algorithm that can effectively control the use of antibiotics along with diagnostic and treatment measures.

This chapter explores recent Artificial Intelligence (AI) innovations in core engineering domains, especially in Electrical and Electronics Engineering. The major grounds for these innovations arise due to the engineer's work toward the forefront innovative technologies, by contributing in research, design, development, testing, and manufacturing of next-generation equipment. The Electrical and Electronics Engineering expands its research and development methodology in applications with artificial intelligence subsets such as machine learning, deep learning, and data science algorithms. This as an upshot made an industrial revolution 4.0. In the evolution of new generation areas of research and development, which are discussed in this chapter, AI algorithms are implemented in the field of power systems, power electronics, smart grids, and renewable energy technologies. The experimental verification for these innovations has been executed using Matlab/Simulink design environment. 

Proven history in science shows that natural products play a vital role in drug discovery, specifically for immune deficiencies, infectious diseases, and other therapeutic areas, including cardiovascular diseases and multiple sclerosis. Monk Agastyar and Pandit Ayothidhas contributed more to the field of Siddha through monoand polyherbal medicine and cured many diseases, including oxidative stress and diabetes. Using computational and analytical intelligence methods, this study aims to develop a natural phycobiont (lichens) edible source of metabolites for the chronic and metabolic disorder type II diabetes. The level of docking was ranked based on the iGEMDOCK grading function, with zero being the most accurate ligand. Ultimately, each complex from each fungus that ensured different binding pockets of the 6AK3 had been designated throughout the virtual screening process. Based on the uppermost energy value, the best compounds from each fungus showed accurate molecular docking. Out of the 22 compounds tested, the anthracene-9-one and acetamide found in R. conduplicans showed a high binding capacity. Meanwhile, the binding energy potential of M-Dioxan-4-ol, 2,6-dimethyl, obtained from X. curta, and 2-Chloroethyl Methyl Sulfoxide, obtained from M. fragilis, was enormous. 3, 4-13, 14-dodecahydr-18,18a-dihydroxy-2-methyl-, and 1,4-Bis (trimethylsilyl) benzene were all found in P. reticulatum.

De novo drug design is a computational technique to develop novel chemical compounds from scratch without prior knowledge. Traditionally, structural bioinformatics approaches used either structure-based or ligand-based design; the former uses the active site information of the protein, and the latter uses known active binders. Modern methods based on artificial intelligence help design de novo drugs in less time by using pre-trained models. One of the major bottlenecks of the de novo drug design is the synthetic feasibility of the active compounds, which is addressed using AI-based methods that help reduce the time and cost of analysis of those compounds. Recent success stories from several companies show the strength of the AI-based de novo drug design programs, and many advances can be expected shortly. 

Healthcare is one of many industries where the most modern technologies, such as artificial intelligence and machine learning, have shown a wide range of applications. Cancer, one of the most prevalent non-communicable diseases in modern times, accounts for a sizable portion of worldwide mortality. Investigations are continuously being conducted to find ways to reduce cancer mortality and morbidity. Artificial Intelligence (AI) is currently being used in cancer research, with promising results. Two main features play a vital role in improving cancer prognosis: early detection and proper diagnosis using imaging and molecular techniques. AI's use as a tool in these sectors has demonstrated its capacity to precisely detect and diagnose, which is one of AI's many applications in cancer research. The purpose of this chapter is to review the literature and find AI applications in a range of cancers that are commonly seen.

Energy conservation is the need of the hour for various reasons, including the depletion of fossil fuels. The domestic sector is the major consumer of generated electricity across the globe. Artificial Intelligence is a powerful decision-making tool. Building AI-based techniques will be effective in conserving energy for domestic appliances. The general framework of AI-based lighting, room comfort, refrigerator and other load systems have been addressed in this chapter. The AI-based systems can effectively manage the operation of these loads, thereby reducing energy consumption

Residential Photovoltaic systems (RPV) are flattering and widespread among customers due to government policies. The power sources available in RPV include a grid, a PV system and a battery. The principal cost of residential photovoltaic systems is a bit high. When more power is exported, the customer who has installed it will export more power for their benefit. It can be achieved by efficiently scheduling the three sources and managing the power export. Artificial Intelligence-based systems can effectively take care of it because they provide effective decision-making solutions.

The study of matter and energy, as well as their relationships with one another, is the focus of the scientific field known as physics. It is possible to describe physics as the study of nature or as that has been belonging to natural things. This branch of science is concerned with the laws and characteristics of matter, in addition to the forces that act upon it. Physics is often recognized as one of the most challenging scientific disciplines-because, it draws concepts and ideas from other academic subfields, such as biology and chemistry. At the beginning of physics, mathematical models had to be meticulously compiled and then evaluated manually. Scientists are now capable of simulating and solving difficult physics problems with notably more speed, precision, and creativity than ever before because of breakthroughs in artificial intelligence and machine learning. Frameworks powered by artificial intelligence are speeding up the research in a wide variety of fields of physics such as nuclear technology, windmill energy production, thermal power plant, space research and energy management. The application of artificial intelligence for the development of new models and solutions for challenging physics problems has the potential to significantly accelerate the rate of progress of scientific advancement across the most basic field of physics.

Solar energy is one of the cleanest renewable energy sources and has no environmental impact. Solar radiation data is important to solar engineers, designers and architects which is also fundamental for efficiently determining irrigation water needs and potential yield of crops, among others. Solar energy is mainly used to meet the growing electricity demand and decline the amount of CO2 emission thus preserving fossil fuels and natural resources. The temperature and sunshine duration are measured by most of the meteorological services all over the world but global solar radiation measurements are limited due to the restricted number of solar radiation measuring stations and some of the data are missing. In order to estimate the solar radiation in the other areas where the meteorological stations are not established, the theoretical solar radiation estimation models proposed by various researchers have proved handy. One of the main important assignments is to recognize the site with high solar energy potential for renewable power generation. This assists in accomplishing the target of the Indian solar mission project by the year 2022. The present work aims at the prediction of solar radiation using artificial neural network models which are applied to four different locations across India. As validation, measured and estimated solar radiation data were analyzed in terms of the square of the correlation coefficient and RMSE. The outcomes of this study will play a vital role in the estimation of global solar radiation with less percentage of error. 

Many cellular communications and cellular activities are regulated by a class of enzyme tyrosine kinases. Mutations or increased expression of these enzymes lead to many proliferative cancers as well as other non-proliferative diseases such as psoriasis, atherosclerosis and some inflammatory diseases. Hence, they are considered vital and prospective therapeutic targets. Over the past decade, considerable research work has been carried out to develop potential inhibitors against these tyrosine kinases. So far, a number of compounds have been identified successfully as tyrosine kinase inhibitors and many compounds were developed as drugs to treat tyrosine kinase-induced diseases. Behind the successful development of these inhibitors, many Computer Aided Drug Design (CADD) (in silico) approaches include molecular modelling, high throughput virtual screening against various chemical databases, and docking (both rigid and flexible method of docking). Further many studies identified the possible features which are responsible for tyrosine kinase inhibition activities for a number of series of compounds through the quantitative structure-activity/property relationship (QSAR/QSPR) process. In this review article, the structural characteristics, mechanism of action, and mode of inhibition of tyrosine kinases are discussed followed by the successful applications of a variety of in silico approaches in tyrosine kinase inhibitors development.

Since ancient times, plants with therapeutic properties play a major role and are used as medicine by several groups of people all over the world. Ethiopia can be considered a hub of medicinal plants due to their diverse species and traditional usage by the local people. Medicinal plants in Ethiopia hold high therapeutic value and hence, most of them are preserved and saved from extinction. Also, most of the plants are yet to be studied due to a lack of documentation and experimental validation. Secondary metabolites from these plants possess numerous pharmacologically active compounds. Computer-aided drug discovery using Artificial Intelligence and high throughput technologies saves time and is more cost-efficient than traditional clinical studies. In this chapter, we discuss the computational studies done on ten important Ethiopian medicinal plants that have antioxidant, antimicrobial, anticancer and antidiabetic properties using phytochemical analysis and In-silico approach for plant-based drug development, which could serve as a potential pharmacological lead against different disease targets.

Artificial Intelligence in agriculture biology plays a vital role in the improvisation of crop production and enhances resistance against plant pathogens. Artificial intelligence brings about changes in crop production by predicting the gene data, showing the ability of plants to resist plant pathogens and environmental conditions. Machine learning methods, namely artificial, neural, and Deep Neural networks. Computational approaches were used to determine Plant Genomics. The main aim of this review study was to understand plant genomics data, predict plant genomes based on machine learning and reduce the cost of fertilizers and side effects. The seven important factors include soil moisture, the electric conductivity of soil solution, evapotranspiration, humidity, soil aeriation, and soil pH and air temperature. The red, green, and infrared channels of sensors in three layers of ANN were used for the determination of genomic data. Chemical fertilizers are used to kill pests damaging crops and affecting the ecosystem. Farmers and agricultural scientists are looking forward to implementing advanced machine learning techniques such as sensors mounted on vegetable and fruit orchards. The traps were manufactured and installed by using sensors to detect parasites infecting crops of agricultural importance. This review study was focused on computational data on plant genomics and promoting less usage of fertilizers to prevent carcinogenic and genomic diseases. The researchers performed an experiment and stated that eight master transcription factors are the most vital to enhance the ability to fix nitrogen from the atmosphere. Farmers are future artificial intelligence Engineers. Based on the review of the literature, it was evident that artificial intelligence enhances crop improvement for better productivity.

Electron Paramagnetic Resonance studies are carried out at room temperature on single crystals of aqualithiumaquabis (malonato) zincate doped with VO(II) using X-band frequencies. Rotations in three mutually orthogonal planes indicate three chemically inequivalent sites, with intensities ratios of 1:2:9. However, only one site, with the highest intensity, could be followed during crystal rotations. The calculated spin Hamiltonian parameters are: gxx=1.976; gyy=1.973; gzz=1.933; Axx= 7.01 mT; Ayy= 6.77 mT; Azz= 18.01 mT. The impurity has entered the lattice in an interstitial position. The analysis of the powder spectrum also reveals the presence of only one site. Admixture coefficients, Fermi contact and dipolar interaction terms have also been evaluated. IR, UV-Visible and powder XRD data of the doped complex confirm the structure and symmetry of the host lattice. 

The morphological and structural characterization of strontium in strontium sulphate in forensic analysis is highlighted in this chapter. Strontium sulphate is a polymeric compound with structural similarities to barium sulphate. The best tool for forensic applications is the SEM's non-destructive microscopic inquiry, which has been utilized as a reference technique to support the study. This study further demonstrated that the main screening of samples using the XRD does not require any special sample preparation. Crystallite size and miller plane for specific peak values are computed using computational data and statistical techniques to obtain accuracy in the forensic investigation. In comparison to previous descriptions of X-rays as tool paintings in forensic analysis, this paper is the one that receives the most citations. A thorough study of these tool coatings might effectively connect an optimistic presumption to particular crime scene locations. 

Methanobrevibacter ruminantium M1 is one of the abundant methanogenic archaea found in ruminants, which is influential in livestock production by enteric methane emission. Several methane mitigation strategies have been employed to curtail enteric methane emissions, most of which have not been successful to date. Hence, it is imperative to discover new targets for the development of organism-specific vaccines and inhibitors of methanogenesis. In this study, we predicted the functions and characterized chemogenomic and vaccine proteins from their operomes using a combined bioinformatics approach. A precise function of 257 hypothetical proteins was assigned based on their sequence-structure-function relationships, as evidenced by the literature. We identified 12 virulence genes and 18 vaccinogenic proteins as reliable antigenic determinants. The predicted virulence proteins were found to promote the survival of this organism in the intestine of ruminant animals. The toll-like receptor, nudix hydrolase, pseudo murein-binding repeat protein, and phosphonoacetate hydrolase identified in this organism have shown more immunogenic and vaccinogenic characteristics. Therefore, the new virulence factors and vaccine candidates identified in this study would provide a quest for new anti-methanogenic drugs to mitigate the methane emitted in ruminant animals. 

Bioelectrochemical technology has been developed to elucidate the mechanisms of electrical interplay systems for electromethanogenesis in microbial electrolysis cells (MEC). In the present study, we evaluated the electrical interplay systems for electromethanogenesis in Methanothermobacter thermautotrophicus ΔH (MTH). The modular structure of its protein-protein interaction (PPI) network was compared with the electrical interplay systems of metal-loving eubacteria (Geobacter metallireducens and G. sulfurreducens). The structure-function-metabolism link of each protein pair was evaluated to mine experimental PPI information from the literature. The results of our study indicate that the topological properties of the PPI networks are robust and consistent for sharing homologous protein interactions across metal-loving eubacteria. A large fraction of genes and associated PPI networks were established in the MTH for direct interspecies electron transfer systems, which were divergent from metal-loving eubacteria. MTH is predicted to generate CH4 by reducing CO2 with hydrogen in the geothermal environment through growth-associated electromethanogenesis. Thus, the present computational study will facilitate an understanding of the proteomic contexts and mechanisms of interspecies electron transfer between thermophilic autotrophic methanogenic archaea and metal-loving Eubacteria for electromethanogenesis.