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

GAMES: A new Scenario for Software and Knowledge Reuse

Games are a well-known test bed for testing search algorithms and learning methods, and many authors have presented numerous reasons for the research in this area. Nevertheless, they have not received the attention they deserve as software projects. In this paper, we analyze the applicability of software and knowledge reuse in the games domain. In spite of the need to find a good evaluation function, search algorithms and interface design can be said to be the primary concerns. In addition, we will discuss the current state of the main statistical learning methods and how they can be addressed from a software engineering point of view. So, this paper proposes a reliable environment and adequate tools, necessary in order to achieve high levels of reuse in the games domain

Penghasilan manual rjngkas penggunaan alat Total Station Sokkia Set5f dan Perisian Sdr Mapping & Design untuk automasi ukur topografi

Projek ini dilaksanakan untuk menghasilkan manual ringkas penggunaan alat Total Station Sokkia SET5F dan Perisian SDR Mapping & Design dalam menghasilkan pelan topografi yang lengkap mengikut konsep field to finish. Manual telah dihasilkan dalam dua bentuk iaitu buku dan CD-ROM. Manual ini telah dinilai berdasarkan data yang diperolehi daripada 7 orang responden melalui kaedah Borang Penilaian Manual. Analisis data dilakukan menggunakan perisian SPSS versi 11.0. Hasil analisis skor min menunjukkan kesemua responden bersetuju bahawa manual dalam bentuk buku ini menarik Min ( M ) ^ ^ dan Sisihan Piawai (SD) = .535 tetapi kurang interaktif (M) = 2.29 dan (SD) = 0.488. Berbanding dengan manual dalam format CD-ROM yang mencatat nilai (M) = 3.57 dan (SD) = 0.535 semua responden bersetuju bahawa manual ini mesra pengguna dan lebih interakti

The Matrix Ridge Approximation: Algorithms and Applications

We are concerned with an approximation problem for a symmetric positive semidefinite matrix due to motivation from a class of nonlinear machine learning methods. We discuss an approximation approach that we call {matrix ridge approximation}. In particular, we define the matrix ridge approximation as an incomplete matrix factorization plus a ridge term. Moreover, we present probabilistic interpretations using a normal latent variable model and a Wishart model for this approximation approach. The idea behind the latent variable model in turn leads us to an efficient EM iterative method for handling the matrix ridge approximation problem. Finally, we illustrate the applications of the approximation approach in multivariate data analysis. Empirical studies in spectral clustering and Gaussian process regression show that the matrix ridge approximation with the EM iteration is potentially useful