Rapid Sequence Identification of Potential Pathogens Using Techniques from Sparse Linear Algebra

The decreasing costs and increasing speed and accuracy of DNA sample collection, preparation, and sequencing has rapidly produced an enormous volume of genetic data. However, fast and accurate analysis of the samples remains a bottleneck. Here we present D$^{4}$RAGenS, a genetic sequence identification algorithm that exhibits the Big Data handling and computational power of the Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear algebra and statistical properties to increase computational performance while retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield speed and precision tradeoffs, with applications in biodefense and medical diagnostics. The D$^{4}$RAGenS analysis algorithm is tested over several datasets, including three utilized for the Defense Threat Reduction Agency (DTRA) metagenomic algorithm contest

A transfer learning approach to drug resistance classification in mixed HIV dataset

Funding: This research is funded by the Tertiary Education Trust Fund (TETFund), Nigeria.As we advance towards individualized therapy, the ‘one-size-fits-all’ regimen is gradually paving the way for adaptive techniques that address the complexities of failed treatments. Treatment failure is associated with factors such as poor drug adherence, adverse side effect/reaction, co-infection, lack of follow-up, drug-drug interaction and more. This paper implements a transfer learning approach that classifies patients' response to failed treatments due to adverse drug reactions. The research is motivated by the need for early detection of patients' response to treatments and the generation of domain-specific datasets to balance under-represented classification data, typical of low-income countries located in Sub-Saharan Africa. A soft computing model was pre-trained to cluster CD4+ counts and viral loads of treatment change episodes (TCEs) processed from two disparate sources: the Stanford HIV drug resistant database (https://hivdb.stanford.edu), or control dataset, and locally sourced patients' records from selected health centers in Akwa Ibom State, Nigeria, or mixed dataset. Both datasets were experimented on a traditional 2-layer neural network (NN) and a 5-layer deep neural network (DNN), with odd dropout neurons distribution resulting in the following configurations: NN (Parienti et al., 2004) [32], NN (Deniz et al., 2018) [53] and DNN [9 7 5 3 1]. To discern knowledge of failed treatment, DNN1 [9 7 5 3 1] and DNN2 [9 7 5 3 1] were introduced to model both datasets and only TCEs of patients at risk of drug resistance, respectively. Classification results revealed fewer misclassifications, with the DNN architecture yielding best performance measures. However, the transfer learning approach with DNN2 [9 7 3 1] configuration produced superior classification results when compared to other variants/configurations, with classification accuracy of 99.40%, and RMSE values of 0.0056, 0.0510, and 0.0362, for test, train, and overall datasets, respectively. The proposed system therefore indicates good generalization and is vital as decision-making support to clinicians/physicians for predicting patients at risk of adverse drug reactions. Although imbalanced features classification is typical of disease problems and diminishes dependence on classification accuracy, the proposed system still compared favorably with the literature and can be hybridized to improve its precision and recall rates.Publisher PDFPeer reviewe

A Structure-Based Approach for Mapping Adverse Drug Reactions to the Perturbation of Underlying Biological Pathways

Adverse drug reactions (ADR), also known as side-effects, are complex undesired physiologic phenomena observed secondary to the administration of pharmaceuticals. Several phenomena underlie the emergence of each ADR; however, a dominant factor is the drug's ability to modulate one or more biological pathways. Understanding the biological processes behind the occurrence of ADRs would lead to the development of safer and more effective drugs. At present, no method exists to discover these ADR-pathway associations. In this paper we introduce a computational framework for identifying a subset of these associations based on the assumption that drugs capable of modulating the same pathway may induce similar ADRs. Our model exploits multiple information resources. First, we utilize a publicly available dataset pairing drugs with their observed ADRs. Second, we identify putative protein targets for each drug using the protein structure database and in-silico virtual docking. Third, we label each protein target with its known involvement in one or more biological pathways. Finally, the relationships among these information sources are mined using multiple stages of logistic-regression while controlling for over-fitting and multiple-hypothesis testing. As proof-of-concept, we examined a dataset of 506 ADRs, 730 drugs, and 830 human protein targets. Our method yielded 185 ADR-pathway associations of which 45 were selected to undergo a manual literature review. We found 32 associations to be supported by the scientific literature

Discovery of novel targets for multi-epitope vaccines: Screening of HIV-1 genomes using association rule mining

© 2009 Paul and Piontkivska; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

The use of machine learning to improve the effectiveness of ANRS in predicting HIV drug resistance.

Master of TeleHealth in Medical Informatics. University of KwaZulu-Natal, Durban, 2016.BACKGROUD HIV has placed a large burden of disease in developing countries. HIV drug resistance is inevitable due to selective pressure. Computer algorithms have been proven to help in determining optimal treatment for HIV drug resistance patients. One such algorithm is the ANRS gold standard interpretation algorithm developed by the French National Agency for AIDS Research AC11 Resistance group. OBJECTIVES The aim of this study is to investigate the possibility of improving the accuracy of the ANRS gold standard in predicting HIV drug resistance. METHODS Data consisting of genome sequence and a HIV drug resistance measure was obtained from the Stanford HIV database. Machine learning factor analysis was performed to determine sequence positions where mutations lead to drug resistance. Sequence positions not found in ANRS were added to the ANRS rules and accuracy was recalculated. RESULTS The machine learning algorithm did find sequence positions, not associated with ANRS, but the model suggests they are important in the prediction of HIV drug resistance. Preliminary results show that for IDV 10 sequence positions where found that were not associated with ANRS rules, 4 for LPV, and 8 for NFV. For NFV, ANRS misclassified 74 resistant profiles as being susceptible to the ARV. Sixty eight of the 74 sequences (92%) were classified as resistance with the inclusion of the eight new sequence positions. No change was found for LPV and a 78% improvement was associated with IDV. CONCLUSION The study shows that there is a possibility of improving ANRS accuracy

Associative stigma among families of alcohol and other drug users

Stigma is the devaluation of groups and individuals because of traits or behaviours that deviate from social norms. Drug use is a highly stigmatised behaviour, as it is mainly viewed as a controllable behaviour or character weakness. Stigma may occur by association and this is known as courtesy or associative stigma. A comprehensive review investigated associative stigma among families of psychoactive substance users. Searches of psychological databases located articles pertaining to associative stigma among families. Articles located indicated that associative stigma occurs toward families in other populations, such as those living with mental illness and HIV. A lack of research exists with regard to stigma among families of alcohol and other drug (AOD) users. Exploratory studies are needed to ascertain how stigma is experienced by families of AOD users and what impact this has on emotional and psychosocial wellbeing and to inform policy makers regarding service needs

Intolerance of uncertainty and impulsivity in opioid dependency

Opioid abuse has reached epidemic status in the United States, and opioids are the leading cause of drug-related deaths in Australia and worldwide. One factor that has not received attention in the addiction literature is intolerance of uncertainty (IU). IU is personality trait characterised by exaggerated negative beliefs about uncertainty and its consequences. This thesis investigates the links between IU and impulsive decision-making in the context of opioid-dependency. Four experimental studies examined impulsive decision-making from multiple perspectives, and assessed for the first time how impulsivity interacts with IU in opioid-dependent individuals. Across all four studies, opioid-dependent adults reported markedly higher levels of IU compared to a healthy control group. This consistent result provides strong evidence that IU is a personality trait that is related to drug addiction, whether it may be a pre-morbid risk factor, a result of chronic drug use or a co-occurring phenomenon based on shared neural correlates. A common thread between studies was that IU and impulsivity were meaningfully related in opioid-dependent individuals, but not in control groups. Specifically, IU was correlated with self-reported impulsive personality traits, poor attentional control, risk taking for monetary losses and risk-aversion for health improvements. No meaningful correlations were found between IU and impulsivity in control participants. These findings have important implications for addiction prevention and therapy. It is commonly accepted that pharmaceutical opioids are a driving factor for the upsurge in heroin abuse, and IU may be helpful to screen for at-risk individuals. Furthermore, addiction treatment could benefit by addressing IU in order to improve faulty beliefs about and reactions to uncertainty

Frequent associations between CTL and T-Helper epitopes in HIV-1 genomes and implications for multi-epitope vaccine designs

<p>Abstract</p> <p>Background</p> <p>Epitope vaccines have been suggested as a strategy to counteract viral escape and development of drug resistance. Multiple studies have shown that Cytotoxic T-Lymphocyte (CTL) and T-Helper (Th) epitopes can generate strong immune responses in Human Immunodeficiency Virus (HIV-1). However, not much is known about the relationship among different types of HIV epitopes, particularly those epitopes that can be considered potential candidates for inclusion in the multi-epitope vaccines.</p> <p>Results</p> <p>In this study we used association rule mining to examine relationship between different types of epitopes (CTL, Th and antibody epitopes) from nine protein-coding HIV-1 genes to identify strong associations as potent multi-epitope vaccine candidates. Our results revealed 137 association rules that were consistently present in the majority of reference and non-reference HIV-1 genomes and included epitopes of two different types (CTL and Th) from three different genes (<it>Gag, Pol </it>and <it>Nef</it>). These rules involved 14 non-overlapping epitope regions that frequently co-occurred despite high mutation and recombination rates, including in genomes of circulating recombinant forms. These epitope regions were also highly conserved at both the amino acid and nucleotide levels indicating strong purifying selection driven by functional and/or structural constraints and hence, the diminished likelihood of successful escape mutations.</p> <p>Conclusions</p> <p>Our results provide a comprehensive systematic survey of CTL, Th and Ab epitopes that are both highly conserved and co-occur together among all subtypes of HIV-1, including circulating recombinant forms. Several co-occurring epitope combinations were identified as potent candidates for inclusion in multi-epitope vaccines, including epitopes that are immuno-responsive to different arms of the host immune machinery and can enable stronger and more efficient immune responses, similar to responses achieved with adjuvant therapies. Signature of strong purifying selection acting at the nucleotide level of the associated epitopes indicates that these regions are functionally critical, although the exact reasons behind such sequence conservation remain to be elucidated.</p