MBFusion: Multi-modal balanced fusion and multi-task learning for cancer diagnosis and prognosis.

Pathological images and molecular omics are important information for predicting diagnosis and prognosis. The two kinds of heterogeneous modal data contain complementary information, and the effective fusion of the two modals can better reveal the complex mechanisms of cancer. However, due to the different representation learning methods, the expression strength of different modals in different tasks varies greatly, so that many multimodal fusions do not achieve the best results.

Spatio-temporal learning and explaining for dynamic functional connectivity analysis: Application to depression.

Background: Functional connectivity has been shown to fluctuate over time. The present study aimed to identifying major depressive disorders (MDD) with dynamic functional connectivity (dFC) from resting-state fMRI data, which would be helpful to produce tools of early depression diagnosis and enhance our understanding of depressive etiology.

Methods: The resting-state fMRI data of 178 subjects were collected, including 89 MDD and 89 healthy controls.

BrainNPT: Pre-Training Transformer Networks for Brain Network Classification.

Deep learning methods have advanced quickly in brain imaging analysis over the past few years, but they are usually restricted by the limited labeled data. Pre-trained model on unlabeled data has presented promising improvement in feature learning in many domains, such as natural language processing. However, this technique is under-explored in brain network analysis.

Incorporating entity-level knowledge in pretrained language model for biomedical dense retrieval.

In recent years, pre-trained language models (PLMs) have dominated natural language processing (NLP) and achieved outstanding performance in various NLP tasks, including dense retrieval based on PLMs. However, in the biomedical domain, the effectiveness of dense retrieval models based on PLMs still needs to be improved due to the diversity and ambiguity of entity expressions caused by the enrichment of biomedical entities. To alleviate the semantic gap, in this paper, we propose a method that incorporates external knowledge at the entity level into a dense retrieval model to enrich the dense representations of queries and documents.

A Syntax-enhanced model based on category keywords for biomedical relation extraction.

Certain categories in multi-category biomedical relationship extraction have linguistic similarities to some extent. Keywords related to categories and syntax structures of samples between these categories have some notable features, which are very useful in biomedical relation extraction. The pre-trained model has been widely used and has achieved great success in biomedical relationship extraction, but it is still incapable of mining this kind of information accurately.

A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction.

Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosis, and therapy of cancers. By integrating these heterogeneous but complementary data, many multi-modal methods are proposed to study the complex mechanisms of cancers, and most of them achieve comparable or better results from previous single-modal methods. However, these multi-modal methods are restricted to a single task (e.

A Review of Fusion Methods for Omics and Imaging Data.

The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e.

GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification.

Background: Autism spectrum disorders (ASD) imply a spectrum of symptoms rather than a single phenotype. ASD could affect brain connectivity at different degree based on the severity of the symptom. Given their excellent learning capability, graph neural networks (GNN) methods have recently been used to uncover functional connectivity patterns and biological mechanisms in neuropsychiatric disorders, such as ASD.

A Hierarchical Graph Convolution Network for Representation Learning of Gene Expression Data.

The curse of dimensionality, which is caused by high-dimensionality and low-sample-size, is a major challenge in gene expression data analysis. However, the real situation is even worse: labelling data is laborious and time-consuming, so only a small part of the limited samples will be labelled. Having such few labelled samples further increases the difficulty of training deep learning models.

Evaluation of Cell's Passability in the ECM Network.

The microstructure of the extracellular matrix (ECM) plays a key role in affecting cell migration, especially nonproteolytic migration. It is difficult, however, to measure some properties of the ECM, such as stiffness and the passability for cell migration. On the basis of a network model of collagen fiber in the ECM, which has been well applied to simulate mechanical behaviors such as the stress-strain relationship, damage, and failure, we proposed a series of methods to study the microstructural properties containing pore size and pore stiffness and to search for the possible migration paths for cells.

A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction.

Background: With the rapid development of sequencing technologies, collecting diverse types of cancer omics data become more cost-effective. Many computational methods attempted to represent and fuse multiple omics into a comprehensive view of cancer. However, different types of omics are related and heterogeneous.

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner.

Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement: Algorithm and Pretreatment Method Validation Study.

Background: The most current methods applied for intrasentence relation extraction in the biomedical literature are inadequate for document-level relation extraction, in which the relationship may cross sentence boundaries. Hence, some approaches have been proposed to extract relations by splitting the document-level datasets through heuristic rules and learning methods. However, these approaches may introduce additional noise and do not really solve the problem of intersentence relation extraction.

LCQS: an efficient lossless compression tool of quality scores with random access functionality.

Background: Advanced sequencing machines dramatically speed up the generation of genomic data, which makes the demand of efficient compression of sequencing data extremely urgent and significant. As the most difficult part of the standard sequencing data format FASTQ, compression of the quality score has become a conundrum in the development of FASTQ compression. Existing lossless compressors of quality scores mainly utilize specific patterns generated by specific sequencer and complex context modeling techniques to solve the problem of low compression ratio.

A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification.

Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data.

Variant-Kudu: An Efficient Tool kit Leveraging Distributed Bitmap Index for Analysis of Massive Genetic Variation Datasets.

The storage and analysis of massive genetic variation datasets in variant call format (VCF) become a great challenge with the rapid growth of genetic variation data in recent years. Traditional single process based tool kits become increasingly inefficient when analyzing massive genetic variation data. While emerging distributed storage technology such as Apache Kudu offers attractive solution, it is demanded to develop distributed storage tool kit for VCF dataset.

PipeMEM: A Framework to Speed Up BWA-MEM in Spark with Low Overhead.

(1) Background: DNA sequence alignment process is an essential step in genome analysis. BWA-MEM has been a prevalent single-node tool in genome alignment because of its high speed and accuracy. The exponentially generated genome data requiring a multi-node solution to handle large volumes of data currently remains a challenge.

A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records.

Background: The Named Entity Recognition (NER) task as a key step in the extraction of health information, has encountered many challenges in Chinese Electronic Medical Records (EMRs). Firstly, the casual use of Chinese abbreviations and doctors' personal style may result in multiple expressions of the same entity, and we lack a common Chinese medical dictionary to perform accurate entity extraction. Secondly, the electronic medical record contains entities from a variety of categories of entities, and the length of those entities in different categories varies greatly, which increases the difficult in the extraction for the Chinese NER.

ADS-HCSpark: A scalable HaplotypeCaller leveraging adaptive data segmentation to accelerate variant calling on Spark.

Background: The advance of next generation sequencing enables higher throughput with lower price, and as the basic of high-throughput sequencing data analysis, variant calling is widely used in disease research, clinical treatment and medicine research. However, current mainstream variant caller tools have a serious problem of computation bottlenecks, resulting in some long tail tasks when performing on large datasets. This prevents high scalability on clusters of multi-node and multi-core, and leads to long runtime and inefficient usage of computing resources.

A three-dimensional collagen-fiber network model of the extracellular matrix for the simulation of the mechanical behaviors and micro structures.

The extracellular matrix (ECM) provides structural and biochemical support to cells and tissues, which is a critical factor for modulating cell dynamic behavior and intercellular communication. In order to further understand the mechanisms of the interactive relationship between cell and the ECM, we developed a three-dimensional (3D) collagen-fiber network model to simulate the micro structure and mechanical behaviors of the ECM and studied the stress-strain relationship as well as the deformation of the ECM under tension. In the model, the collagen-fiber network consists of abundant random distributed collagen fibers and some crosslinks, in which each fiber is modeled as an elastic beam and a crosslink is modeled as a linear spring with tensile limit, it means crosslinks will fail while the tensile forces exceed the limit of spring.

An effective news recommendation method for microblog user.

Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. Traditional systems strive to satisfy their user by tracing users' reading history and choosing the proper candidate news articles to recommend. However, most of news websites hardly require any user to register before reading news.

Multigrid contact detection method.

Contact detection is a general problem of many physical simulations. This work presents a O(N) multigrid method for general contact detection problems (MGCD). The multigrid idea is integrated with contact detection problems.