Radhakrishnan Manavalana

Background: Rice is cultivated worldwide as one of the primary food crops. The responsible factors that rigorously affect rice crops' production are pests and various rice plant diseases, leading to considerable reduction in the agrarian and global economy. More sustainable farming methods for determining disease levels and the quality of paddy plants will be essential in the future. Objective: The disease discovery in rice crops by naked eyes may result in erroneous pesticide measurements. Therefore, early diagnosis of rice diseases can expedite disease control by properly selecting pest management methods to maximize the rice yield to cope with the demand of the world's growing population. A literature search is conducted and identifies 68 peer-reviewed research studies published in the period between 2007 and 2021, focusing on early disease detection of rice crops to maximize productivity. Conclusion: This study has identified several key issues that must be resolved at each step of the computer-assisted diagnostic system to recognize diseases in paddy crops. Study results show that automated disease diagnosing techniques are still immature for rice plants. Hence, the ingenious design and evolution of a novel fully-automated farming system are widely essential as innovative methods for addressing and resolving diseases in the paddy crop to offer sustainability and productivity benefits to the agrarian sector.

: Genome-wide Association Studies (GWAS) give special insight into genetic differences and environmental influences that are part of different human disorders and provide prognostic help to increase the survival of patients. Lung diseases such as lung cancer, asthma, and tuberculosis are detected by analyzing Single Nucleotide Polymorphism (SNP) genetic variations. The key causes of lung-related diseases are genetic factors, environmental and social behaviors. : The epistasis effects act as a blueprint for the researchers to observe the genetic variation associated with lung diseases. The manual examination of the enormous genetic interactions is complicated to detect the lung syndromes for diagnosis of acute respiratory diseases. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all the relevant genetic studies published between 2006 and 2020. In this critical review, various computational approaches are extensively discussed in detecting respondent epistasis effects for various lung diseases such as asthma, tuberculosis, lung cancer, and nicotine drug dependence. : The analysis shows that different computational models identified candidate genes such as CHRNA4, CHRNB2, BDNF, TAS2R16, TAS2R38, BRCA1, BRCA2, RAD21, IL4Ra, IL-13 and IL-1β, have important causes for genetic variants linked to pulmonary disease. These computational approaches' strengths and limitations are described. The issues behind the computational methods while identifying the lung diseases through epistasis effects and the parameters used by various researchers for their evaluation are also presented.

Neurological disorders, such as ALS, Alzheimer’s, epilepsy, Parkinson’s Disease, Autism, Atrial Fibrillation, and Sclerosis, affect the central nervous system, including the brain, nerves, spinal cords, muscles, and Neuromuscular joint. These disorders are investigated by detecting the genetic variations in Single Nucleotide Polymorphism (SNP) in Genome-Wide Association Studies (GWAS). In the human genome sequence, one SNP influences the effects of another SNP. These SNP-SNP interactions or Gene-Gene interaction (Epistasis) significantly increase the risk of disease susceptibility to neurological disorders. The manual analyses of various genetic interactions related to neurological diseases are cumbersome. Hence, the computational system is effective for the discovery of Epistasis effects in neurological syndromes. This study aims to explore various techniques of statistical, machine learning, optimization so far applied to find the epistasis effect for neurological disorders. This study finds several genetic interaction models involving different loci, various candidate genes, and SNP interactions involved in numerous neurological diseases. The gene APOE and its polymorphism increase Alzheimer's disease pathology. The gene GAB2 and its SNPs play a vital role in Alzheimer’s disease. The genes GABRA4, ITGB3, and SLC64A highly influence the genetic interactions for Autism disorder. In schizophrenia, the SNPs of NRG1 increase the disease risk. The benefits, limitations, and issues of the various computational techniques implemented for epistasis evaluation of neurological disease are deeply discussed.

Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of the cancer cell emerges from multi-locus as well as complex genetic interaction. It is impractical for the physician to detect cancer via manual examination of SNPs interaction. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2001 and 2020. In this contemporary review, various computational methods are as follows: multifactor dimensionality reduction–based approaches, statistical strategies, machine learning, and optimization-based techniques are carefully reviewed and presented with their evaluation results. Moreover, these computational approaches’ strengths and limitations are described. The issues behind the computational methods for identifying the cancer disease through genetic interactions and the various evaluation parameters used by researchers have been analyzed. This review is highly beneficial for researchers and medical professionals to learn techniques adapted to discover the epistasis and aids to design novel automatic epistasis detection systems with strong robustness and maximum efficiency to address the different research problems in finding practical solutions effectively. Graphical abstract

Background: The diseases in the heart and blood vessels such as heart attack, Coronary Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/ LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome- Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures of CVD. Objective: Genetic interactions or Epistasis infer the interactions between two or more genes where one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the Epistasis relationship through biological or laboratory methods needs an enormous workforce and more cost. Hence, this paper presents the review of various statistical and Machine learning approaches so far proposed to detect genetic interaction effects for the identification of various Cardiovascular diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid phenotypes data, and Body Mass Index dataset. Conclusion: This study reveals that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits, limitations, and issues of the various computational techniques for the evolution of epistasis responsible for cardiovascular diseases are exhibited.

Two decades. Nonlinear interaction recognition, also known as epistasis identification, plays a crucial part in identifying RA's genetic causes. GWAS recognizes all single nucleotide polymorphisms (SNPs) genetic variants and the interactions between SNPs to identify RA susceptibility. Manual evaluation and interactions of many SNPs were too complicated for physicians. The main objective of this study is to explore various techniques of statistical, machine learning, optimization, so far applied to identify epistasis effect related to arthritis. The challenges behind the computational model and the experimental outcome of various methods were also focused....