High Throughput Virtual Screening with Data Level Parallelism in Multi-core Processors

Improving the throughput of molecular docking, a computationally intensive phase of the virtual screening process, is a highly sought area of research since it has a significant weight in the drug designing process. With such improvements, the world might find cures for incurable diseases like HIV disease and Cancer sooner. Our approach presented in this paper is to utilize a multi-core environment to introduce Data Level Parallelism (DLP) to the Autodock Vina software, which is a widely used for molecular docking software. Autodock Vina already exploits Instruction Level Parallelism (ILP) in multi-core environments and therefore optimized for such environments. However, with the results we have obtained, it can be clearly seen that our approach has enhanced the throughput of the already optimized software by more than six times. This will dramatically reduce the time consumed for the lead identification phase in drug designing along with the shift in the processor technology from multi-core to many-core of the current era. Therefore, we believe that the contribution of this project will effectively make it possible to expand the number of small molecules docked against a drug target and improving the chances to design drugs for incurable diseases.Comment: Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th International Conference o

The p-index: theory and applications

This thesis defines and presents a new index to measure the scientific output of researchers, called the p-index. We compare the p-index to one of the most used indices, the h-index, which is defined as the h number of papers that have at least h citations each. As it was an improvement on the measures used at the time and it allowed one to compress the scientific achievements of a researcher in to a single metric, the h-index garnered respect and has been in constant use ever since. However many have noted the h-index has drawbacks which can lead to the possibility of exploiting the h-index for one's personal gain. The h-index does not attach weights to the citations, which may lead to potential misuse if documents are created purely to cite others. The p-index is computed from the underlying citation network of papers and uses the pagerank algorithm in its computation. In this research, we demonstrate that the p-index provides a fairer ranking of scientists than the h-index and its variants. We do this by, (i) simulating a realistic model of the evolution of citation and collaboration networks in a particular field, (ii) using real world citation datasets, and comparing the h-index and p-index of scientists under a number of scenarios. The results from the simulated system show that the p-index is immune to the author behaviors that can result in artificially bloated h-index values. Analysis is applied to two real-world datasets: a dataset of scientists from the field of quantum game theory and a dataset of scientists from the field of high energy physics - theory (HEP-TH). We show that, while the popularly used h-index rewards authors who collaborate extensively and publish in higher volumes, the p-index rewards hardworking authors who contribute more to each paper they write, as well as authors who publish in high impact and well-cited journals. As such, it could be argued that the p-index is a ‘fairer’ metric of the productivity and impact of scientists

Diagnosis of Cognitive Impairment using Multiple Data Modalities

Decline in cognitive functions including memory, processing speed and executive processes, has been associated with ageing for sometime. It is understood that every human will go through this process, but some will go through it faster, and for some this process starts earlier. Differentiating between cognitive decline due to a pathological process and normal ageing is an ongoing research challenge. According to the definition of the World Health Organization (WHO), dementia is an umbrella term for a number of diseases affecting memory and other cognitive abilities and behaviour that interfere significantly with the ability to maintain daily living activities. Although a cure for dementia has not been found yet, it is often stressed that early identification of individuals at risk of dementia can be instrumental in treatment and management. Mild Cognitive Impairment (MCI) is considered to be a prodromal condition to dementia, and patients with MCI have a higher probability of progressing to certain types of dementia, the most common being Alzheimer's Disease (AD). Epidemiological studies suggest that the progression rate from MCI to dementia is around 10-12\% annually, while much lower in the general elderly population. Therefore, accurate and early diagnosis of MCI may be useful, as those patients can be closely monitored for progression to dementia. Traditionally, clinicians use a number of neuropsychological tests (also called NM features) to evaluate and diagnose cognitive decline in individuals. In contrast, computer aided diagnostic techniques often focus on medical imaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET). This thesis utilises machine learning and deep learning techniques to leverage both of these data modalities in a single end-to-end pipeline that is robust to missing information. A number of techniques have been designed, implemented and validated to diagnose different types of cognitive impairment including mild cognitive impairment and its subtypes as well as dementia, initially directly from NM features, and then in fusion with medical imaging features. The novel techniques proposed by this thesis build end-to-end deep learning pipelines that are capable of learning to extract features and engineering combinations of features to yield the best performance. The proposed deep fusion pipeline is capable of fusing data from multiple disparate modalities of vastly different dimensions seamlessly. Survival analysis techniques are often used to understand the progression and time till an event of interest. In this thesis, the proposed deep survival analysis techniques are used to better understand the progression to dementia. They also enable the use of imaging data seamlessly with NM features, which is the first such approach as far as is known. The techniques are designed, implemented and validated across two datasets; an in-house dataset and a publicly available dataset adding an extra layer of cross validation. The proposed techniques can be used to differentiate between cognitively impaired and cognitively normal individuals and gain better insights on their subsequent progression to dementia

Editorial Introduction: Industrialisation, Knowledge Societies and Education

Quantifying and comparing the scientific output of researchers has become critical for governments, funding agencies and universities. Comparison by reputation and direct assessment of contributions to the field is no longer possible, as the number of scientists increases and traditional definitions about scientific fields become blurred. The h-index is often used for comparing scientists, but has several well-documented shortcomings. In this paper, we introduce a new index for measuring and comparing the publication records of scientists: the pagerank-index (symbolised as π). The index uses a version of pagerank algorithm and the citation networks of papers in its computation, and is fundamentally different from the existing variants of h-index because it considers not only the number of citations but also the actual impact of each citation. We adapt two approaches to demonstrate the utility of the new index. Firstly, we use a simulation model of a community of authors, whereby we create various 'groups' of authors which are different from each other in inherent publication habits, to show that the pagerank-index is fairer than the existing indices in three distinct scenarios: (i) when authors try to 'massage' their index by publishing papers in low-quality outlets primarily to self-cite other papers (ii) when authors collaborate in large groups in order to obtain more authorships (iii) when authors spend most of their time in producing genuine but low quality publications that would massage their index. Secondly, we undertake two real world case studies: (i) the evolving author community of quantum game theory, as defined by Google Scholar (ii) a snapshot of the high energy physics (HEP) theory research community in arXiv. In both case studies, we find that the list of top authors vary very significantly when h-index and pagerank-index are used for comparison. We show that in both cases, authors who have collaborated in large groups and/or published less impactful papers tend to be comparatively favoured by the h-index, whereas the pagerank-index highlights authors who have made a relatively small number of definitive contributions, or written papers which served to highlight the link between diverse disciplines, or typically worked in smaller groups. Thus, we argue that the pagerank-index is an inherently fairer and more nuanced metric to quantify the publication records of scientists compared to existing measures

Variation of average h-index and pagerank-index for quantity oriented authors and quality oriented authors at each timestep in scenario 3.

<p>The difference between groups is much smaller when pagerank-index is used.</p

The h-index and pagerank-index of the best 5% authors (according to h-index) in the field of quantum game theory.

<p>Since the pagerank-index is a percentile, percentile values were used for the h-index as well, rather than actual h-index values. Note here that the pagerank-index value varies from 70% to 100%. That is, some authors who are among the top 5% in terms of h-index are not even among the top 25% when pagerank-index is considered.</p

A citation network of documents with differing impacts.

<p> Document ‘A’ is a document with seemingly high impact: therefore citation from document A to document X could be weighed by the ‘impact’ of document A. However, it is clear that document A receives its high impact status from documents P, Q, R and S, which are themselves low impact documents. The documents P, Q, R and S could have been deliberately created to give more credibility to A. Similarly, document B which is also a high impact document receives its high impact status from low impact documents. Therefore, the citation counts of documents cannot be directly used to weigh the citations, since these weights themselves could be manipulated. A more nuanced approach is therefore necessary.</p

The spread of h-index for collaborative and non-collaborative authors (as absolute values) in scenario 2.

<p>For authors of a similar level of seniority (as indicated by their IDs), the ‘collaborative’ authors have a clear advantage.</p

The spread of h-index for quantity oriented authors and quality oriented authors (as absolute values) in scenario 3.

<p>Considering authors with the same level of seniority (as indicated by the IDs), the ‘quantity-oriented’ authors have a clear advantage over ‘quality-oriented’ authors.</p