Abstract
We present a solution submitted to the Social Media and Harassment Competition held in collaboration with ECML PKDD 2019 Conference. The dataset used is as set of tweets and the first task was on the detection of harassment tweets. To deal with this problem, we proposed a solution based on a gradient tree-boosting algorithm. The second task was categorization harassment tweets according to the type of harassment, a multiclass classification problem. For this problem we proposed a LSTM network model. The solutions proposed for these tasks presented good predictive accuracy.

Abstract
With the advances of the big data era in biology, deep learning have been incorporated in analysis pipelines trying to transform biological information into valuable knowledge. Deep learning demonstrated its power in promoting bioinformatics field including sequence analysis, bio-molecular property and function prediction, automatic medical diagnosis and to analyse cell imaging data. The ambition of this work is to create an approach that can fully explore the relationships across modalities and subjects through mining and fusing features from multi-modality data for cell state classification. The system should be able to classify cell state through multimodal deep learning techniques using heterogeneous data such as biological images, genomics and clinical annotations. Our pilot study addresses the data acquisition process and the framework capable to extract biological parameters from cell images.

Abstract

Abstract
Cancer has an important and considerable gender differential susceptibility confirmed by several epidemiological studies. Gastric (GC) and thyroid cancer (TC) are examples of malignancies with a higher incidence in males and females, respectively. Beyond environmental predisposing factors, it is expected that gender-specific gene deregulation contributes to this differential incidence. We performed a detailed characterization of the transcriptomic differences between genders in normal and tumor tissues from stomach and thyroid using Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) data. We found hundreds of sex-biased genes (SBGs). Most of the SBGs shared by normal and tumor belong to sexual chromosomes, while the normal and tumor-specific tend to be found in the autosomes. Expression of several cancer-associated genes is also found to differ between sexes in both types of tissue. Thousands of differentially expressed genes (DEGs) between paired tumor-normal tissues were identified in GC and TC. For both cancers, in the most susceptible gender, the DEGs were mostly under-expressed in the tumor tissue, with an enrichment for tumor-suppressor genes (TSGs). Moreover, we found gene networks preferentially associated to males in GC and to females in TC and correlated with cancer histological subtypes. Our results shed light on the molecular differences and commonalities between genders and provide novel insights in the differential risk underlying these cancers.

Abstract
The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

Abstract
Gastric Cancer (GC) is one of the most common and deadliest types of cancer in the world. To improve GC prognosis, increasing efforts are being made to develop new targeted therapies. Although FGFR2 genetic amplification and protein overexpression in GC have been targeted in clinical trials, so far no improvement in patient overall survival has been found. To address this issue, we studied genetic and epigenetic events affecting FGFR2 and its splicing regulator ESRP1 in GC that could be used as new therapeutic targets or predictive biomarkers. We performed copy number variation (CNV), DNA methylation, and RNA expression analyses of FGFR2/ESRP1 across several cohorts. We discovered that both genes were frequently amplified and demethylated in GC, resulting in increased ESRP1 expression and of a specific FGFR2 isoform: FGFR2-IIIb. We also showed that ESRP1 amplification in GC correlated with a significant decreased expression of FGFR2-IIIc, an alternative FGFR2 splicing isoform. Furthermore, when we performed a survival analysis, we observed that patients harboring diffuse-type tumors with low FGFR2-IIIc expression revealed a better overall survival than patients with FGFR2-IIIc high-expressing diffuse tumors. Our results encourage further studies on the role of ESRP1 in GC and support FGFR2-IIIc as a relevant biomarker in GC.