Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Mining Biological Networks towards Protein complex Detection and Gene-Disease Association

Large amounts of biological data are continuously generated nowadays, thanks to the advancements of high-throughput experimental techniques. Mining valuable knowledge from such data still motivates the design of suitable computational methods, to complement the experimental work which is often bound by considerable time and cost requirements. Protein complexes or groups of interacting proteins, are key players in most cellular events. The identification of complexes not only allows to better understand normal biological processes but also to uncover Disease-triggering malfunctions. Ultimately, findings in this research branch can highly enhance the design of effective medical treatments. The aim of this research is to detect protein complexes in protein-protein interaction networks and to associate the detected entities to diseases. The work is divided into three main objectives: first, develop a suitable method for the identification of protein complexes in static interaction networks; second, model the dynamic aspect of protein interaction networks and detect complexes accordingly; and third, design a learning model to link proteins, and subsequently protein complexes, to diseases. In response to these objectives, we present, ProRank+, a novel complex-detection approach based on a ranking algorithm and a merging procedure. Then, we introduce DyCluster, which uses gene expression data, to model the dynamics of the interaction networks, and we adapt the detection algorithm accordingly. Finally, we integrate network topology attributes and several biological features of proteins to form a classification model for gene-disease association. The reliability of the proposed methods is supported by various experimental studies conducted to compare them with existing approaches. Pro Rank+ detects more protein complexes than other state-of-the-art methods. DyCluster goes a step further and achieves a better performance than similar techniques. Then, our learning model shows that combining topological and biological features can greatly enhance the gene-disease association process. Finally, we present a comprehensive case study of breast cancer in which we pinpoint disease genes using our learning model; subsequently, we detect favorable groupings of those genes in a protein interaction network using the Pro-rank+ algorithm

A rewiring model of intratumoral interaction networks.

Intratumoral heterogeneity (ITH) has been regarded as a key cause of the failure and resistance of cancer therapy, but how it behaves and functions remains unclear. Advances in single-cell analysis have facilitated the collection of a massive amount of data about genetic and molecular states of individual cancer cells, providing a fuel to dissect the mechanistic organization of ITH at the molecular, metabolic and positional level. Taking advantage of these data, we propose a computational model to rewire up a topological network of cell-cell interdependences and interactions that operate within a tumor mass. The model is grounded on the premise of game theory that each interactive cell (player) strives to maximize its fitness by pursuing a rational self-interest strategy, war or peace, in a way that senses and alters other cells to respond properly. By integrating this idea with genome-wide association studies for intratumoral cells, the model is equipped with a capacity to visualize, annotate and quantify how somatic mutations mediate ITH and the network of intratumoral interactions. Taken together, the model provides a topological flow by which cancer cells within a tumor cooperate or compete with each other to downstream pathogenesis. This topological flow can be potentially used as a blueprint for genetically intervening the pattern and strength of cell-cell interactions towards cancer control

Environmental chemical exposures and breast cancer

As a hormone-sensitive condition with no single identifiable cause, breast cancer is a major health problem. It is characterized by a wide range of contributing factors and exposures occurring in different combinations and strengths across a lifetime that may be amplified during periods of enhanced developmental susceptibility and impacted by reproductive patterns and behaviours. The vast majority of cases are oestrogen-receptor positive and occur in women with no family history of the disease suggesting that modifiable risk factors are involved. A substantial body of evidence now links oestrogen-positive breast cancer with environmental exposures. Synthetic chemicals capable of oestrogen mimicry are characteristic of industrial development and have been individually and extensively assessed as risk factors for oestrogen-sensitive cancers. Existing breast cancer risk assessment tools do not take such factors into account. In the absence of consensus on causation and in order to better understand the problem of escalating incidence globally, an expanded, integrated approach broadening the inquiry into individual susceptibility breast cancer is proposed. Applying systems thinking to existing data on oestrogen-modulating environmental exposures and other oestrogenic factors characteristic of Westernisation and their interactions in the exposure, encompassing social, behavioural, environmental, hormonal and genetic factors, can assist in understanding cancer risks and the pursuit of prevention strategies. A new conceptual framework based on a broader understanding of the “system” that underlies the development of breast cancer over a period of many years, incorporating the factors known to contribute to breast cancer risk, could provide a new platform from which government and regulators can promulgate enhanced and more effective prevention strategies

Personalized medicine : the impact on chemistry

An effective strategy for personalized medicine requires a major conceptual change in the development and application of therapeutics. In this article, we argue that further advances in this field should be made with reference to another conceptual shift, that of network pharmacology. We examine the intersection of personalized medicine and network pharmacology to identify strategies for the development of personalized therapies that are fully informed by network pharmacology concepts. This provides a framework for discussion of the impact personalized medicine will have on chemistry in terms of drug discovery, formulation and delivery, the adaptations and changes in ideology required and the contribution chemistry is already making. New ways of conceptualizing chemistry’s relationship with medicine will lead to new approaches to drug discovery and hold promise of delivering safer and more effective therapies

Why are health care interventions delivered over the internet? : a systematic review of the published literature

Background: As Internet use grows, health interventions are increasingly being delivered online. Pioneering researchers are using the networking potential of the Internet, and several of them have evaluated these interventions. Objective: The objective was to review the reasons why health interventions have been delivered on the Internet and to reflect on the work of the pioneers in this field in order to inform future research. Methods: We conducted a qualitative systematic review of peer-reviewed evaluations of health interventions delivered to a known client/patient group using networked features of the Internet. Papers were reviewed for the reasons given for using the Internet, and these reasons were categorized. Results: We included studies evaluating 28 interventions plus 9 interventions that were evaluated in pilot studies. The interventions were aimed at a range of health conditions. Reasons for Internet delivery included low cost and resource implications due to the nature of the technology; reducing cost and increasing convenience for users; reduction of health service costs; overcoming isolation of users; the need for timely information; stigma reduction; and increased user and supplier control of the intervention. A small number of studies gave the existence of Internet interventions as the only reason for undertaking an evaluation of this mode of delivery. Conclusions: One must remain alert for the unintended effects of Internet delivery of health interventions due to the potential for reinforcing the problems that the intervention was designed to help. Internet delivery overcomes isolation of time, mobility, and geography, but it may not be a substitute for face-to-face contact. Future evaluations need to incorporate the evaluation of cost, not only to the health service but also to users and their social networks. When researchers report the outcomes of Internet-delivered health care interventions, it is important that they clearly state why they chose to use the Internet, preferably backing up their decision with theoretical models and exploratory work. Evaluation of the effectiveness of a health care intervention delivered by the Internet needs to include comparison with more traditional modes of delivery to answer the following question: What are the added benefits or disadvantages of Internet use that are particular to this mode of delivery