BLNet:Bidirectional learning network for point clouds

The key challenge in processing point clouds lies in the inherent lack of ordering and irregularity of the 3D points.By relying on per-point multi-layer perceptions(MLPs),most existing point-based approaches only address the first issue yet ignore the second one.Directly convolving kernels with irregular points will result in loss of shape information.This paper introduces a novel point-based bidirectional learning network(BLNet)to analyze irregular 3D points.BLNet optimizes the learning of 3D points through two iterative operations:feature-guided point shifting and feature learning from shifted points,so as to minimise intra-class variances,leading to a more regular distribution.On the other hand,explicitly modeling point positions leads to a new feature encoding with increased structure-awareness.Then,an attention pooling unit selectively combines important features.This bidirectional learning alternately regularizes the point cloud and learns its geometric features,with these two procedures iteratively promoting each other for more effective feature learning.Experiments show that BLNet is able to learn deep point features robustly and efficiently,and outperforms the prior state-of-the-art on multiple challenging tasks.

Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological contexts.To fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic sequences.Most of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome scale.Here,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing data.We show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types.Furthermore,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific TSSs.Finally,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin states.The source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://ngdc.cncb.ac.cn/biocode/tools/BT007316.