Usage of Big Data Prediction Techniques for Predictive Analysis in HIV/AIDS

The term big data refers to the data that exceeds the processing or analyzing capacity of existing database management systems. The inability of existing DBMS to handle big data is due to its large volume, high velocity, pertaining veracity, heterogeneous variety, and on-atomic values. Nowadays, healthcare plays a vital role in everyone's life. It becomes a very large and open platform for everyone to do all kinds of research work without affecting human life. When it comes to disease, there are so many types found all over the world. But among them, AIDS (acquired immunodeficiency syndrome) is a disease that spreads so quickly and can easily turn life to death. There are many studies going on to create drugs to cure this deadly disease, but until now, there has been no success. In cases such as this, big data is implemented for better a result, which will have a good impact on society.

Computational Analysis of Reverse Transcriptase Resistance to Inhibitors in HIV-1

Reverse transcriptase (RT) is a vital enzyme in the process of transcription of HIV-1. The nucleoside analogues of RT inhibitors (NRTIs) act by substrate competition and chain termination as they resemble a nucleotide. To understand the basis of RT resistance in HIV-1, in this chapter, one of the clinically essential mutants Q151M of RT which exhibits multi-resistance to many NRTIs was modeled and docked with NRTIs in comparison to wild type (WT). The results of docking indicate that the WT showed high affinity with all inhibitors compared to the mutant (MT). It can be suggested that the high affinity in WT could be attributed to the favorable interactions with all inhibitors that lacks in MT due to amino acid substitution that leads to structural changes in MT protein, which alters the favorable network of interaction and eventually imparts resistance to all inhibitors.

HIV-Associated Neurocognitive Disorder

In the last two decades, several advancement studies have increased the care of HIV-infected individuals. Specifically, the development for preparation of combination antiretroviral therapy has resulted in a dramatic decline in the rate of deaths from AIDS. The term “HIV-associated neurocognitive disorder” (HAND) has been used to distinguish the spectrum of neurocognitive dysfunction associated with HIV infection. HIV can pass to the CNS during the early stages of infection and last in the CNS. CNS inflammation and infection lead to the development of HAND. The brain can serve as a sanctuary for ongoing HIV replication, even when the systemic viral suppression has been achieved. HAND can remain in patients treated with combination antiretroviral therapy, and its effect on survival, quality of life, and everyday functioning make it a significant unresolved problem. This chapter discusses details of the computational modeling studies on mechanisms and structures of human dopamine transporter (hDAT) and its interaction with HIV-1 trans activator of transcription (Tat).

Dissection of HIV-1 Protease Subtype B Inhibitors Resistance Through Molecular Modeling Approaches

Protease (PR) is an important enzyme required for the posttranslational processing of the viral gene products of type-1 human immunodeficiency virus (HIV-1). Protease inhibitors (PI) act as competitive inhibitors that bind to the active site of PR. The I84V mutation contributes resistance to multiple PIs, and structurally, this mutation affects both sides of the enzyme active site. In order to get insights about this major resistance site to PR inhibitors using in silico approaches, in this chapter, the wild-type (WT) and mutant (MT) I84V of PR were modeled and docked with all PR inhibitors: Atazanavir, Darunavir, Indinavir, Lopinavir, Nelfinavir, Saquinavir, and Tipranavir. Docking results revealed that in comparison to the WT, the binding score was higher for the MT-I84V. Thus, it can be suggested that the high affinity towards inhibitors in the MT could be due to the presence of energetically favorable interactions, which may lead to tight binding of inhibitors with the MT protein, leading to the development of PR resistance against PIs in HIV-1 eventually.

Statistical and Computational Needs for Big Data Challenges

The traditional way of formatting information from transactional systems to make them available for “statistical processing” does not work in a situation where data is arriving in huge volumes from diverse sources, and where even the formats could be changing. Faced with this volume and diversification, it is essential to develop techniques to make best use of all of these stocks in order to extract the maximum amount of information and knowledge. Traditional analysis methods have been based largely on the assumption that statisticians can work with data within the confines of their own computing environment. But the growth of the amounts of data is changing that paradigm, especially which ride of the progress in computational data analysis. This chapter builds upon sources but also goes further in the examination to answer this question: What needs to be done in this area to deal with big data challenges?

Prevalence in MSM Is Enhanced by Role Versatility

In the community of men who have sex with men (MSM) the prevalence of the HIV-1 infection is still high. Promiscuity and condom fatigue are making unprotected anal intercourse (UAI) more common and sexually transmitted infections (STIs) presumably harder to track. Yet, MSM communities are peculiar in the sense that men can adopt fixed (insertive or receptive) or versatile (both practices) roles. Some old theoretical work predicted that the transmission of HIV-1 would be enhanced in MSM populations engaged more in role versatility than in role segregation, in which fixed roles are predominantly adopted. These predictions were based on the assumption that the probability of acquisition from unprotected insertive anal (UIA) sex was neglectable, which is an inappropriate assumption. This chapter shows that the increase of the HIV-1 prevalence among MSM due to role versatility holds under a stronger assumption of bidirectional virus transmission.

Risks, Security, and Privacy for HIV/AIDS Data

Big data has the potential to transform healthcare systems for the prevention and treatment of HIV/AIDS by providing analytic tools that are capable of handling huge and different types of data at very fast speeds. Big data's transformative potential is also introverted by privacy and security requirements for HIV/AIDS patients' sensitive data that restrict health information exchange. Electronic health records provide the opportunity for HIV/AIDS patients to receive improved coordinated care from healthcare providers and easier access to their health information. This chapter discusses the various legal frameworks governing health information, dispels misconceptions about privacy regulations, and highlights how these legal frameworks provide privacy, confidentiality, and security to this sensitive information, and shows how EHRs can maximize the utility of big data to improve HIV/AIDS prevention and treatment.

Computational and Data Mining Perspectives on HIV/AIDS in Big Data Era

HIV/AIDS big data analytics evolved as a potential initiative enabling the connection between three major scientific disciplines: (1) the HIV biology emergence and evolution; (2) the clinical and medical complex problems and practices associated with the infections and diseases; and (3) the computational methods for the mining of HIV/AIDS biological, medical, and clinical big data. This chapter provides a review on the computational and data mining perspectives on HIV/AIDS in big data era. The chapter focuses on the research opportunities in this domain, identifies the challenges facing the development of big data analytics in HIV/AIDS domain, and then highlights the future research directions of big data in the healthcare sector.