Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

Cancer stem cells (CSCs) are subpopulation of cells which have been demonstrated in a variety of cancer models and involved in cancer initiation, progression, and development. Indeed, CSCs which seem to form a small percentage of tumor cells, display resembling characteristics to natural stem cells such as self-renewal, survival, differentiation, proliferation, and quiescence. Moreover, they have some characteristics that eventually can demonstrate the heterogeneity of cancer cells and tumor progression. On the other hand, another aspect of CSCs that has been recognized as a central concern facing cancer patients is resistance to mainstays of cancer treatment such as chemotherapy and radiation. Owing to these details and the stated stemness capabilities, these immature progenitors of cancerous cells can constantly persist after different therapies and cause tumor regrowth or metastasis. Further, in both normal development and malignancy, cellular metabolism and stemness are intricately linked and CSCs dominant metabolic phenotype changes across tumor entities, patients, and tumor subclones. Hence, CSCs can be determined as one of the factors that correlate to the failure of common therapeutic approaches in cancer treatment. In this context, researchers are searching out new alternative or complementary therapies such as targeted methods to fight against cancer. Molecular docking is one of the computational modeling methods that has a new promise in cancer cell targeting through drug designing and discovering programs. In a simple definition, molecular docking methods are used to determine the metabolic interaction between two molecules and find the best orientation of a ligand to its molecular target with minimal free energy in the formation of a stable complex. As a comprehensive approach, this computational drug design method can be thought more cost-effective and time-saving compare to other conventional methods in cancer treatment. In addition, increasing productivity and quality in pharmaceutical research can be another advantage of this molecular modeling method. Therefore, in recent years, it can be concluded that molecular docking can be considered as one of the novel strategies at the forefront of the cancer battle via targeting cancer stem cell metabolic processes.

Autism spectrum disorder (ASD) refers to a complicated range of childhood neurodevelopmental disorders which can occur via genetic or non-genetic factors. Clinically, ASD is associated with problems in relationships, social interactions, and behaviors that pose many challenges for children with ASD and their families. Due to the complexity, heterogeneity, and association of symptoms with some neuropsychiatric disorders such as ADHD, anxiety, and sleep disorders, clinical trials have not yielded reliable results and there still remain challenges in drug discovery and development pipeline for ASD patients. One of the main steps in promoting lead compounds to the suitable drug for commercialization is preclinical animal testing, in which the efficacy and toxicity of candidate drugs are examined in vivo. In recent years, zebrafish have been able to attract the attention of many researchers in the field of neurological disorders such as ASD due to their outstanding features. The presence of orthologous genes for ASD modeling, the anatomical similarities of parts of the brain, and similar neurotransmitter systems between zebrafish and humans are some of the main reasons why scientists draw attention to zebrafish as a prominent animal model in preclinical studies to discover highly effective treatment approaches for the ASD through genetic and non-genetic modeling methods.

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Amyotrophic lateral sclerosis is a pernicious neurodegenerative disorder that is associated with the progressive degeneration of motor neurons, the disruption of impulse transmission from motor neurons to muscle cells, and the development of mobility impairments. Clinically, muscle paralysis can spread to other parts of the body. Hence it may have adverse effects on swallowing, speaking, and even breathing, which serves as major problems facing these patients. According to the available evidence, no definite treatment has been found for amyotrophic lateral sclerosis (ALS) that results in a significant outcome, although some pharmacological and non-pharmacological treatments are currently applied that are accompanied by some positive effects. In other words, available therapies are only used to relieve symptoms without any significant treatment effects that highlight the importance of seeking more novel therapies. Unfortunately, the process of discovering new drugs with high therapeutic potential for ALS treatment is fraught with challenges. The lack of a broad view of the disease process from early to late-stage and insufficiency of preclinical studies for providing validated results prior to conducting clinical trials are other reasons for the ALS drug discovery failure. However, increasing the combined application of different fields of regenerative medicine, especially tissue engineering and stem cell therapy can be considered as a step forward to develop more novel technologies. For instance, organ on a chip is one of these technologies that can provide a platform to promote a comprehensive understanding of neuromuscular junction biology and screen candidate drugs for ALS in combination with pluripotent stem cells (PSCs). The structure of this technology is based on the use of essential components such as iPSC- derived motor neurons and iPSC-derived skeletal muscle cells on a single miniaturized chip for ALS modeling. Accordingly, an organ on a chip not only can mimic ALS complexities but also can be considered as a more cost-effective and time-saving disease modeling platform in comparison with others. Hence, it can be concluded that lab on a chip can make a major contribution as a biomimetic micro-physiological system in the treatment of neurodegenerative disorders such as ALS.

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