New alternatives to the Lennard-Jones potential.

We present a new method for approximating two-body interatomic potentials from existing ab initio data based on representing the unknown function as an analytic continued fraction. In this study, our method was first inspired by a representation of the unknown potential as a Dirichlet polynomial, i.e., the partial sum of some terms of a Dirichlet series. Our method allows for a close and computationally efficient approximation of the ab initio data for the noble gases Xenon (Xe), Krypton (Kr), Argon (Ar), and Neon (Ne), which are proportional to r - 6 and to a very simple d e p t h = 1 truncated continued fraction with integer coefficients and depending on n - r only, where n is a natural number (with n = 13 for Xe, n = 16 for Kr, n = 17 for Ar, and n = 27 for Neon). For Helium (He), the data is well approximated with a function having only one variable n - r with n = 31 and a truncated continued fraction with d e p t h = 2 (i.e., the third convergent of the expansion). Also, for He, we have found an interesting d e p t h = 0 result, a Dirichlet polynomial of the form k 1 6 - r + k 2 48 - r + k 3 72 - r (with k 1 , k 2 , k 3 all integers), which provides a surprisingly good fit, not only in the attractive but also in the repulsive region. We also discuss lessons learned while facing the surprisingly challenging non-linear optimisation tasks in fitting these approximations and opportunities for parallelisation.

Approximating the nuclear binding energy using analytic continued fractions.

Understanding nuclear behaviour is fundamental in nuclear physics. This paper introduces a data-driven approach, Continued Fraction Regression (cf-r), to analyze nuclear binding energy (B(A, Z)). Using a tailored loss function and analytic continued fractions, our method accurately approximates stable and experimentally confirmed unstable nuclides. We identify the best model for nuclides with A ≥ 200 , achieving precise predictions with residuals smaller than 0.15 MeV. Our model's extrapolation capabilities are demonstrated as it converges with upper and lower bounds at the nuclear mass limit, reinforcing its accuracy and robustness. The results offer valuable insights into the current limitations of state-of-the-art data-driven approaches in approximating the nuclear binding energy. This work provides an illustration on the use of analytical continued fraction regression for a wide range of other possible applications.

Continued fractions and the Thomson problem.

We introduce new analytical approximations of the minimum electrostatic energy configuration of n electrons, E(n), when they are constrained to be on the surface of a unit sphere. Using 453 putative optimal configurations, we searched for approximations of the form [Formula: see text] where g(n) was obtained via a memetic algorithm that searched for truncated analytic continued fractions finally obtaining one with Mean Squared Error equal to [Formula: see text] for the model of the normalized energy ([Formula: see text]). Using the Online Encyclopedia of Integer Sequences, we searched over 350,000 sequences and, for small values of n, we identified a strong correlation of the highest residual of our best approximations with the sequence of integers n defined by the condition that [Formula: see text] is a prime. We also observed an interesting correlation with the behavior of the smallest angle [Formula: see text], measured in radians, subtended by the vectors associated with the nearest pair of electrons in the optimal configuration. When using both [Formula: see text] and [Formula: see text] as variables a very simple approximation formula for [Formula: see text] was obtained with MSE= [Formula: see text] and MSE= 73.2349 for E(n). When expanded as a power series in infinity, we observe that an unknown constant of an expansion as a function of [Formula: see text] of E(n) first proposed by Glasser and Every in 1992 as [Formula: see text], and later refined by Morris, Deaven and Ho as [Formula: see text] in 1996, may actually be very close to -1.10462553440167 when the assumed optima for [Formula: see text] are used.

Differential kynurenine pathway metabolism in highly metastatic aggressive breast cancer subtypes: beyond IDO1-induced immunosuppression.

BACKGROUND: Immunotherapy has recently been proposed as a promising treatment to stop breast cancer (BrCa) progression and metastasis. However, there has been limited success in the treatment of BrCa with immune checkpoint inhibitors. This implies that BrCa tumors have other mechanisms to escape immune surveillance. While the kynurenine pathway (KP) is known to be a key player mediating tumor immune evasion and while there are several studies on the roles of the KP in cancer, little is known about KP involvement in BrCa. METHODS: To understand how KP is regulated in BrCa, we examined the KP profile in BrCa cell lines and clinical samples (n = 1997) that represent major subtypes of BrCa (luminal, HER2-enriched, and triple-negative (TN)). We carried out qPCR, western blot/immunohistochemistry, and ultra-high pressure liquid chromatography on these samples to quantify the KP enzyme gene, protein, and activity, respectively. RESULTS: We revealed that the KP is highly dysregulated in the HER2-enriched and TN BrCa subtype. Gene, protein expression, and KP metabolomic profiling have shown that the downstream KP enzymes KMO and KYNU are highly upregulated in the HER2-enriched and TN BrCa subtypes, leading to increased production of the potent immunosuppressive metabolites anthranilic acid (AA) and 3-hydroxylanthranilic acid (3HAA). CONCLUSIONS: Our findings suggest that KMO and KYNU inhibitors may represent new promising therapeutic targets for BrCa. We also showed that KP metabolite profiling can be used as an accurate biomarker for BrCa subtyping, as we successfully discriminated TN BrCa from other BrCa subtypes.

Basal-like breast cancer: molecular profiles, clinical features and survival outcomes.

BACKGROUND: Basal-like constitutes an important molecular subtype of breast cancer characterised by an aggressive behaviour and a limited therapy response. The outcome of patients within this subtype is, however, divergent. Some individuals show an increased risk of dying in the first five years, and others a long-term survival of over ten years after the diagnosis. In this study, we aim at identifying markers associated with basal-like patients' survival and characterising subgroups with distinct disease outcome. METHODS: We explored the genomic and transcriptomic profiles of 351 basal-like samples from the METABRIC and ROCK data sets. Two selection methods, labelled Differential and Survival filters, were employed to determine genes/probes that are differentially expressed in tumour and control samples, and are associated with overall survival. These probes were further used to define molecular subgroups, which vary at the microRNA level and in DNA copy number. RESULTS: We identified the expression signature of 80 probes that distinguishes between two basal-like subgroups with distinct clinical features and survival outcomes. Genes included in this list have been mainly linked to cancer immune response, epithelial-mesenchymal transition and cell cycle. In particular, high levels of CXCR6, HCST, C3AR1 and FPR3 were found in Basal I; whereas HJURP, RRP12 and DNMT3B appeared over-expressed in Basal II. These genes exhibited the highest betweenness centrality and node degree values and play a key role in the basal-like breast cancer differentiation. Further molecular analysis revealed 17 miRNAs correlated to the subgroups, including hsa-miR-342-5p, -150, -155, -200c and -17. Additionally, increased percentages of gains/amplifications were detected on chromosomes 1q, 3q, 8q, 10p and 17q, and losses/deletions on 4q, 5q, 8p and X, associated with reduced survival. CONCLUSIONS: The proposed signature supports the existence of at least two subgroups of basal-like breast cancers with distinct disease outcome. The identification of patients at a low risk may impact the clinical decisions-making by reducing the prescription of high-dose chemotherapy and, consequently, avoiding adverse effects. The recognition of other aggressive features within this subtype may be also critical for improving individual care and for delineating more effective therapies for patients at high risk.

Using the QAPgrid Visualization Approach for Biomarker Identification of Cell-Specific Transcriptomic Signatures.

In this chapter, we illustrate the use of an integrated mathematical method for joint clustering and visualization of large-scale datasets. In applying these clustering methodologies to biological datasets, we aim to identify differentially expressed genes according to cell type by building molecular signatures supported by statistical scores. In doing so, we also aim to find a global map of highly co-expressed clusters. Variations in these clusters may well indicate other pathological trends and changes.

Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.

This chapter introduces a new method for knowledge extraction from databases for the purpose of finding a discriminative set of features that is also a robust set for within-class classification. Our method is generic and we introduce it here in the field of breast cancer diagnosis from digital mammography data. The mathematical formalism is based on a generalization of the k-Feature Set problem called (α, ß)-k-Feature Set problem, introduced by Cotta and Moscato (J Comput Syst Sci 67(4):686-690, 2003). This method proceeds in two steps: first, an optimal (α, ß)-k-feature set of minimum cardinality is identified and then, a set of classification rules using these features is obtained. We obtain the (α, ß)-k-feature set in two phases; first a series of extremely powerful reduction techniques, which do not lose the optimal solution, are employed; and second, a metaheuristic search to identify the remaining features to be considered or disregarded. Two algorithms were tested with a public domain digital mammography dataset composed of 71 malignant and 75 benign cases. Based on the results provided by the algorithms, we obtain classification rules that employ only a subset of these features.

Alzheimer's disease patient groups derived from a multivariate analysis of cognitive test outcomes in the Coalition Against Major Diseases dataset.

AIM: The mini-mental state examination, commonly used to measure cognitive impairment of Alzheimer's disease (AD) patients, consists of five test categories. The final score is calculated as their total sum, implying a loss of information. MATERIALS & METHODS: In this study, we propose a new multivariate approach to address this issue. RESULTS: We analyzed the current largest AD-related coalition against major diseases dataset comprising 3717 patients of interest. Our clustering approach revealed five groups of patients associated with distinct characteristics and prognosis. Interestingly, only three cognitive test categories significantly contribute to their determination: registration, attention and recall. CONCLUSION: The insight that only these categories are critical for AD group determination may help to resolve the patients' educational background issue often discussed in relation to the mini-mental state examination assessment.

A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays.

In this study we propose a novel, unsupervised clustering methodology for analyzing large datasets. This new, efficient methodology converts the general clustering problem into the community detection problem in graph by using the Jensen-Shannon distance, a dissimilarity measure originating in Information Theory. Moreover, we use graph theoretic concepts for the generation and analysis of proximity graphs. Our methodology is based on a newly proposed memetic algorithm (iMA-Net) for discovering clusters of data elements by maximizing the modularity function in proximity graphs of literary works. To test the effectiveness of this general methodology, we apply it to a text corpus dataset, which contains frequencies of approximately 55,114 unique words across all 168 written in the Shakespearean era (16th and 17th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays.

Extensive Transcriptomic and Genomic Analysis Provides New Insights about Luminal Breast Cancers.

Despite constituting approximately two thirds of all breast cancers, the luminal A and B tumours are poorly classified at both clinical and molecular levels. There are contradictory reports on the nature of these subtypes: some define them as intrinsic entities, others as a continuum. With the aim of addressing these uncertainties and identifying molecular signatures of patients at risk, we conducted a comprehensive transcriptomic and genomic analysis of 2,425 luminal breast cancer samples. Our results indicate that the separation between the molecular luminal A and B subtypes-per definition-is not associated with intrinsic characteristics evident in the differentiation between other subtypes. Moreover, t-SNE and MST-kNN clustering approaches based on 10,000 probes, associated with luminal tumour initiation and/or development, revealed the close connections between luminal A and B tumours, with no evidence of a clear boundary between them. Thus, we considered all luminal tumours as a single heterogeneous group for analysis purposes. We first stratified luminal tumours into two distinct groups by their HER2 gene cluster co-expression: HER2-amplified luminal and ordinary-luminal. The former group is associated with distinct transcriptomic and genomic profiles, and poor prognosis; it comprises approximately 8% of all luminal cases. For the remaining ordinary-luminal tumours we further identified the molecular signature correlated with disease outcomes, exhibiting an approximately continuous gene expression range from low to high risk. Thus, we employed four virtual quantiles to segregate the groups of patients. The clinico-pathological characteristics and ratios of genomic aberrations are concordant with the variations in gene expression profiles, hinting at a progressive staging. The comparison with the current separation into luminal A and B subtypes revealed a substantially improved survival stratification. Concluding, we suggest a review of the definition of luminal A and B subtypes. A proposition for a revisited delineation is provided in this study.

Identification of Differentially Expressed Genes through Integrated Study of Alzheimer's Disease Affected Brain Regions.

BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia in older adults that damages the brain and results in impaired memory, thinking and behaviour. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. In the past decade, several studies have reported many genes that are associated with AD. This wealth of information has become difficult to follow and interpret as most of the results are conflicting. In that case, it is worth doing an integrated study of multiple datasets that helps to increase the total number of samples and the statistical power in detecting biomarkers. In this study, we present an integrated analysis of five different brain region datasets and introduce new genes that warrant further investigation. METHODS: The aim of our study is to apply a novel combinatorial optimisation based meta-analysis approach to identify differentially expressed genes that are associated to AD across brain regions. In this study, microarray gene expression data from 161 samples (74 non-demented controls, 87 AD) from the Entorhinal Cortex (EC), Hippocampus (HIP), Middle temporal gyrus (MTG), Posterior cingulate cortex (PC), Superior frontal gyrus (SFG) and visual cortex (VCX) brain regions were integrated and analysed using our method. The results are then compared to two popular meta-analysis methods, RankProd and GeneMeta, and to what can be obtained by analysing the individual datasets. RESULTS: We find genes related with AD that are consistent with existing studies, and new candidate genes not previously related with AD. Our study confirms the up-regualtion of INFAR2 and PTMA along with the down regulation of GPHN, RAB2A, PSMD14 and FGF. Novel genes PSMB2, WNK1, RPL15, SEMA4C, RWDD2A and LARGE are found to be differentially expressed across all brain regions. Further investigation on these genes may provide new insights into the development of AD. In addition, we identified the presence of 23 non-coding features, including four miRNA precursors (miR-7, miR570, miR-1229 and miR-6821), dysregulated across the brain regions. Furthermore, we compared our results with two popular meta-analysis methods RankProd and GeneMeta to validate our findings and performed a sensitivity analysis by removing one dataset at a time to assess the robustness of our results. These new findings may provide new insights into the disease mechanisms and thus make a significant contribution in the near future towards understanding, prevention and cure of AD.

Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset.

BACKGROUND: Multi-gene lists and single sample predictor models have been currently used to reduce the multidimensional complexity of breast cancers, and to identify intrinsic subtypes. The perceived inability of some models to deal with the challenges of processing high-dimensional data, however, limits the accurate characterisation of these subtypes. Towards the development of robust strategies, we designed an iterative approach to consistently discriminate intrinsic subtypes and improve class prediction in the METABRIC dataset. FINDINGS: In this study, we employed the CM1 score to identify the most discriminative probes for each group, and an ensemble learning technique to assess the ability of these probes on assigning subtype labels using 24 different classifiers. Our analysis is comprised of an iterative computation of these methods and statistical measures performed on a set of over 2000 samples. The refined labels assigned using this iterative approach revealed to be more consistent and in better agreement with clinicopathological markers and patients' overall survival than those originally provided by the PAM50 method. CONCLUSIONS: The assignment of intrinsic subtypes has a significant impact in translational research for both understanding and managing breast cancer. The refined labelling, therefore, provides more accurate and reliable information by improving the source of fundamental science prior to clinical applications in medicine.

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification.

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, ß) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals.

INTRODUCTION: Recently, quantitative metabolomics identified a panel of 10 plasma lipids that were highly predictive of conversion to Alzheimer's disease (AD) in cognitively normal older individuals (n = 28, area under the curve [AUC] = 0.92, sensitivity/specificity of 90%/90%). METHODS: Quantitative targeted metabolomics in serum using an identical method as in the index study. RESULTS: We failed to replicate these findings in a substantially larger study from two independent cohorts-the Baltimore Longitudinal Study of Aging ([BLSA], n = 93, AUC = 0.642, sensitivity/specificity of 51.6%/65.7%) and the Age, Gene/Environment Susceptibility-Reykjavik Study ([AGES-RS], n = 100, AUC = 0.395, sensitivity/specificity of 47.0%/36.0%). In analyses applying machine learning methods to all 187 metabolite concentrations assayed, we find a modest signal in the BLSA with distinct metabolites associated with the preclinical and symptomatic stages of AD, whereas the same methods gave poor classification accuracies in the AGES-RS samples. DISCUSSION: We believe that ours is the largest blood biomarker study of preclinical AD to date. These findings underscore the importance of large-scale independent validation of index findings from biomarker studies with relatively small sample sizes.

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set.

BACKGROUND: The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction. METHODS AND FINDINGS: The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method. CONCLUSIONS: The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes.

A New Combinatorial Optimization Approach for Integrated Feature Selection Using Different Datasets: A Prostate Cancer Transcriptomic Study.

BACKGROUND: The joint study of multiple datasets has become a common technique for increasing statistical power in detecting biomarkers obtained from smaller studies. The approach generally followed is based on the fact that as the total number of samples increases, we expect to have greater power to detect associations of interest. This methodology has been applied to genome-wide association and transcriptomic studies due to the availability of datasets in the public domain. While this approach is well established in biostatistics, the introduction of new combinatorial optimization models to address this issue has not been explored in depth. In this study, we introduce a new model for the integration of multiple datasets and we show its application in transcriptomics. METHODS: We propose a new combinatorial optimization problem that addresses the core issue of biomarker detection in integrated datasets. Optimal solutions for this model deliver a feature selection from a panel of prospective biomarkers. The model we propose is a generalised version of the (α,ß)-k-Feature Set problem. We illustrate the performance of this new methodology via a challenging meta-analysis task involving six prostate cancer microarray datasets. The results are then compared to the popular RankProd meta-analysis tool and to what can be obtained by analysing the individual datasets by statistical and combinatorial methods alone. RESULTS: Application of the integrated method resulted in a more informative signature than the rank-based meta-analysis or individual dataset results, and overcomes problems arising from real world datasets. The set of genes identified is highly significant in the context of prostate cancer. The method used does not rely on homogenisation or transformation of values to a common scale, and at the same time is able to capture markers associated with subgroups of the disease.

Clustering consumers based on trust, confidence and giving behaviour: data-driven model building for charitable involvement in the Australian not-for-profit sector.

Organisations in the Not-for-Profit and charity sector face increasing competition to win time, money and efforts from a common donor base. Consequently, these organisations need to be more proactive than ever. The increased level of communications between individuals and organisations today, heightens the need for investigating the drivers of charitable giving and understanding the various consumer groups, or donor segments, within a population. It is contended that `trust' is the cornerstone of the not-for-profit sector's survival, making it an inevitable topic for research in this context. It has become imperative for charities and not-for-profit organisations to adopt for-profit's research, marketing and targeting strategies. This study provides the not-for-profit sector with an easily-interpretable segmentation method based on a novel unsupervised clustering technique (MST-kNN) followed by a feature saliency method (the CM1 score). A sample of 1,562 respondents from a survey conducted by the Australian Charities and Not-for-profits Commission is analysed to reveal donor segments. Each cluster's most salient features are identified using the CM1 score. Furthermore, symbolic regression modelling is employed to find cluster-specific models to predict `low' or `high' involvement in clusters. The MST-kNN method found seven clusters. Based on their salient features they were labelled as: the `non-institutionalist charities supporters', the `resource allocation critics', the `information-seeking financial sceptics', the `non-questioning charity supporters', the `non-trusting sceptics', the `charity management believers' and the `institutionalist charity believers'. Each cluster exhibits their own characteristics as well as different drivers of `involvement'. The method in this study provides the not-for-profit sector with a guideline for clustering, segmenting, understanding and potentially targeting their donor base better. If charities and not-for-profit organisations adopt these strategies, they will be more successful in today's competitive environment.

Evolving robust gene regulatory networks.

Design and implementation of robust network modules is essential for construction of complex biological systems through hierarchical assembly of 'parts' and 'devices'. The robustness of gene regulatory networks (GRNs) is ascribed chiefly to the underlying topology. The automatic designing capability of GRN topology that can exhibit robust behavior can dramatically change the current practice in synthetic biology. A recent study shows that Darwinian evolution can gradually develop higher topological robustness. Subsequently, this work presents an evolutionary algorithm that simulates natural evolution in silico, for identifying network topologies that are robust to perturbations. We present a Monte Carlo based method for quantifying topological robustness and designed a fitness approximation approach for efficient calculation of topological robustness which is computationally very intensive. The proposed framework was verified using two classic GRN behaviors: oscillation and bistability, although the framework is generalized for evolving other types of responses. The algorithm identified robust GRN architectures which were verified using different analysis and comparison. Analysis of the results also shed light on the relationship among robustness, cooperativity and complexity. This study also shows that nature has already evolved very robust architectures for its crucial systems; hence simulation of this natural process can be very valuable for designing robust biological systems.

Big data for big questions: it is time for data analysts to act.

Pablo Moscato speaks to Francesca Lake, Managing Editor Australian Research Council Future Fellow Prof. Pablo Moscato was born in 1964 in La Plata, Argentina. Obtaining his B.Sc. in Physics at University of La Plata, his PhD was defended at UNICAMP, Brazil. While at the California Institute of Technology Concurrent Computation Program he developed, in collaboration with Michael Norman, the first application of a methodology later called 'memetic algorithms', which is now widely used internationally. He is the founding co-director of the Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-based Medicine (CIBM) (2006-present) and the funding director of the Newcastle Bioinformatics Initiative (2002-2006) of The University of Newcastle (Australia). He is also Chief Investigator of the Australian Research Council Centre in Bioinformatics. He is one of Australia's most cited computer scientists. Over the past 7 years, he has introduced a unifying hallmark of cancer progression based on the changes of information theory quantifiers, and developed a novel mathematical model and an associated solution procedure based on combinatorial optimization techniques to identify drug combinations for cancer therapeutics. In addition, he has identified proteomic signatures to predict the clinical symptoms of Alzheimer's disease, among other 'firsts'. He is a member of the Editorial Board of Future Science OA.

Identification of genome-wide SNP-SNP and SNP-clinical Boolean interactions in age-related macular degeneration.

We propose here a methodology to uncover modularities in the network of SNP-SNP interactions most associated with disease. We start by computing all possible Boolean binary SNP interactions across the whole genome. By constructing a weighted graph of the most relevant interactions and via a combinatorial optimization approach, we find the most highly interconnected SNPs. We show that the method can be easily extended to find SNP/environment interactions. Using a modestly sized GWAS dataset of age-related macular degeneration (AMD), we identify a group of only 19 SNPs, which include those in previously reported regions associated to AMD. We also uncover a larger set of loci pointing to a matrix of key processes and functions that are affected. The proposed integrative methodology extends and overlaps traditional statistical analysis in a natural way. Combinatorial optimization techniques allow us to find the kernel of the most central interactions, complementing current methods of GWAS analysis and also enhancing the search for gene-environment interaction.