An Efficient Algorithm for Upper Bound on the Partition Function of Nucleic Acids

It has been shown that minimum free energy structure for RNAs and RNA-RNA interaction is often incorrect due to inaccuracies in the energy parameters and inherent limitations of the energy model. In contrast, ensemble based quantities such as melting temperature and equilibrium concentrations can be more reliably predicted. Even structure prediction by sampling from the ensemble and clustering those structures by Sfold [7] has proven to be more reliable than minimum free energy structure prediction. The main obstacle for ensemble based approaches is the computational complexity of the partition function and base pairing probabilities. For instance, the space complexity of the partition function for RNA-RNA interaction is $O(n^4)$ and the time complexity is $O(n^6)$ which are prohibitively large [4,12]. Our goal in this paper is to give a fast algorithm, based on sparse folding, to calculate an upper bound on the partition function. Our work is based on the recent algorithm of Hazan and Jaakkola [10]. The space complexity of our algorithm is the same as that of sparse folding algorithms, and the time complexity of our algorithm is $O(MFE(n)\ell)$ for single RNA and $O(MFE(m, n)\ell)$ for RNA-RNA interaction in practice, in which $MFE$ is the running time of sparse folding and $\ell \leq n$ ($\ell \leq n + m$) is a sequence dependent parameter

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

Microbial community pattern detection in human body habitats via ensemble clustering framework

The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural patterns. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201

Adaptive Online Sequential ELM for Concept Drift Tackling

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect underfitting condition.Comment: Hindawi Publishing. Computational Intelligence and Neuroscience Volume 2016 (2016), Article ID 8091267, 17 pages Received 29 January 2016, Accepted 17 May 2016. Special Issue on "Advances in Neural Networks and Hybrid-Metaheuristics: Theory, Algorithms, and Novel Engineering Applications". Academic Editor: Stefan Hauf

NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS

Skills-based hiring is a talent management approach that empowers employers to align recruitment around business results, rather than around credentials and title. It starts with employers identifying the particular skills required for a role, and then screening and evaluating candidates’ competencies against those requirements. With the recent rise in employers adopting skills-based hiring practices, it has become integral for students to take courses that improve their marketability and support their long-term career success. A 2017 survey of over 32,000 students at 43 randomly selected institutions found that only 34% of students believe they will graduate with the skills and knowledge required to be successful in the job market. Furthermore, the study found that while 96% of chief academic officers believe that their institutions are very or somewhat effective at preparing students for the workforce, only 11% of business leaders strongly agree [11]. An implication of the misalignment is that college graduates lack the skills that companies need and value. Fortunately, the rise of skills-based hiring provides an opportunity for universities and students to establish and follow clearer classroom-to-career pathways. To this end, this paper presents a course recommender system that aims to improve students’ career readiness by suggesting relevant skills and courses based on their unique career interests

Weighted Heuristic Ensemble of Filters

Feature selection has become increasingly important in data mining in recent years due to the rapid increase in the dimensionality of big data. However, the reliability and consistency of feature selection methods (filters) vary considerably on different data and no single filter performs consistently well under various conditions. Therefore, feature selection ensemble has been investigated recently to provide more reliable and effective results than any individual one but all the existing feature selection ensemble treat the feature selection methods equally regardless of their performance. In this paper, we present a novel framework which applies weighted feature selection ensemble through proposing a systemic way of adding different weights to the feature selection methods-filters. Also, we investigate how to determine the appropriate weight for each filter in an ensemble. Experiments based on ten benchmark datasets show that theoretically and intuitively adding more weight to ‘good filters’ should lead to better results but in reality it is very uncertain. This assumption was found to be correct for some examples in our experiment. However, for other situations, filters which had been assumed to perform well showed bad performance leading to even worse results. Therefore adding weight to filters might not achieve much in accuracy terms, in addition to increasing complexity, time consumption and clearly decreasing the stability

Partitioning Complex Networks via Size-constrained Clustering

The most commonly used method to tackle the graph partitioning problem in practice is the multilevel approach. During a coarsening phase, a multilevel graph partitioning algorithm reduces the graph size by iteratively contracting nodes and edges until the graph is small enough to be partitioned by some other algorithm. A partition of the input graph is then constructed by successively transferring the solution to the next finer graph and applying a local search algorithm to improve the current solution. In this paper, we describe a novel approach to partition graphs effectively especially if the networks have a highly irregular structure. More precisely, our algorithm provides graph coarsening by iteratively contracting size-constrained clusterings that are computed using a label propagation algorithm. The same algorithm that provides the size-constrained clusterings can also be used during uncoarsening as a fast and simple local search algorithm. Depending on the algorithm's configuration, we are able to compute partitions of very high quality outperforming all competitors, or partitions that are comparable to the best competitor in terms of quality, hMetis, while being nearly an order of magnitude faster on average. The fastest configuration partitions the largest graph available to us with 3.3 billion edges using a single machine in about ten minutes while cutting less than half of the edges than the fastest competitor, kMetis