Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space.

Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of 'near-drug' properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence.

Dispersion relations of elastic waves in one-dimensional piezoelectric/piezomagnetic phononic crystal with functionally graded interlayers.

The effects of functionally graded interlayers on dispersion relations of elastic waves in a one-dimensional piezoelectric/piezomagnetic phononic crystal are studied in this paper. First, the state transfer equation of the functionally graded interlayer is derived from the motion equation by the reduction of order (from second order to first order). The transfer matrix of the functionally graded interlayer is obtained by solving the state transfer equation with the spatial-varying coefficient.

[SNP of rs17321515 homologous with Trib1 in Han population and its correlation with blood lipid].

Objective: To study on genotype distribution of rs17321515 which is homologous with Trib1 and its associativity with blood lipid parameters and diagnosis.

Method: Collecting 1 014 blood samples to measure its lipid levels, then detecting rs17321515 locus's SNP by MALDI-TOF mass spectrometry technology.

Results: Three genotypes (A/A, A/G, G/G) proportions in the population were 13.

Empirical investigations into full-text protein interaction Article Categorization Task (ACT) in the BioCreative II.5 Challenge.

The selection of protein interaction documents is one important application for biology research and has a direct impact on the quality of downstream BioNLP applications, i.e., information extraction and retrieval, summarization, QA, etc.

Feature generation and representations for protein-protein interaction classification.

Automatic detecting protein-protein interaction (PPI) relevant articles is a crucial step for large-scale biological database curation. The previous work adopted POS tagging, shallow parsing and sentence splitting techniques, but they achieved worse performance than the simple bag-of-words representation. In this paper, we generated and investigated multiple types of feature representations in order to further improve the performance of PPI text classification task.

Supervised and traditional term weighting methods for automatic text categorization.

In vector space model (VSM), text representation is the task of transforming the content of a textual document into a vector in the term space so that the document could be recognized and classified by a computer or a classifier. Different terms (i.e.