In massive Machine Type Communication (mMTC), an enormous number of user equipments (UEs) try to access the network in a short period. It is usually challenging to support such massive access with limited resources. Fortunately, directional beams can offer spatial degrees of freedom for random access (RA). In this article, a novel technique called beam splitting/merging is proposed to improve the performance of RA in mMTC network slice with limited resources such as preambles. We describe the framework of beam splitting/merging with the preambles reused, derive the RA throughput of the split beams which is approximately linearly increased with the number of beams, and give a method to obtain beam configuration ensuring the global RA success probability. With the derived analytic results, we further give an analysis of beam configuration in a typical scenario of beam splitting. Moreover, we design an RA control algorithm based on beam splitting/merging technique, by which the mMTC slice can dynamically provision RA capacity according to current access intensity. The updated RA configuration of each beam is broadcast by system information [e.g., system information block 1 (SIB1)]. Detailed analysis and simulation results show that the proposed beam splitting/merging algorithm can effectively improve system efficiency and guarantee RA performance of mMTC network slice with little increase of signaling overhead and delay. The proposed beam splitting/merging framework is applicable to both grant-based and grant-free RA procedures.

MOTIVATION: In microarray studies, numerous tools are available for functional enrichment analysis based on GO categories. Most of these tools, due to their requirement of a prior threshold for designating genes as differentially expressed genes (DEGs), are categorized as threshold-dependent methods that often suffer from a major criticism on their changing results with different thresholds. RESULTS: In the present article, by considering the inherent correlation structure of the GO categories, a continuous measure based on semantic similarity of GO categories is proposed to investigate the functional consistence (or stability) of threshold-dependent methods. The results from several datasets show when simply counting overlapping categories between two groups, the significant category groups selected under different DEG thresholds are seemingly very different. However, based on the semantic similarity measure proposed in this article, the results are rather functionally consistent for a wide range of DEG thresholds. Moreover, we find that the functional consistence of gene lists ranked by SAM metric behaves relatively robust against changing DEG thresholds. AVAILABILITY: Source code in R is available on request from the authors.

MOTIVATION: Microarrays datasets frequently contain a large number of missing values (MVs), which need to be estimated and replaced for subsequent data mining. The focus of the paper is to study the effects of different MV treatments for cDNA microarray data on disease classification analysis. RESULTS: By analyzing five datasets, we demonstrate that among three kinds of classifiers evaluated in this study, support vector machine (SVM) classifiers are robust to varied MV imputation methods [e.g. replacing MVs by zero, K nearest-neighbor (KNN) imputation algorithm, local least square imputation and Bayesian principal component analysis], while the classification and regression tree classifiers are sensitive in terms of classification accuracy. The KNNclassifiers built on differentially expressed genes (DEGs) are robust to the varied MV treatments, but the performances of the KNN classifiers based on all measured genes can be significantly deteriorated when imputing MVs for genes with larger missing rate (MR) (e.g. MR > 5%). Generally, while replacing MVs by zero performs relatively poor, the other imputation algorithms have little difference in affecting classification performances of the SVM or KNN classifiers. We further demonstrate the power and feasibility of our recently proposed functional expression profile (FEP) approach as means to handle microarray data with MVs. The FEPs, which are derived from the functional modules that are enriched with sets of DEGs and thus can be consistently identified under varied MV treatments, achieve precise disease classification with better biological interpretation. We conclude that the choice of MV treatments should be determined in context of the later approaches used for disease classification. The suggested exclusion criterion of ignoring the genes with larger MR (e.g. >5%), while justifiable for some classifiers such as KNN classifiers, might not be considered as a general rule for all classifiers.