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Chapter and Conference Paper
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection
Federated learning has seen increased adoption in recent years in response to the growing regulatory demand for data privacy. However, the opaque local training process of federated learning also sparks rising...
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Article
Open AccessA new and effective two-step clustering approach for single cell RNA sequencing data
The rapid devolvement of single cell RNA sequencing (scRNA-seq) technology leads to huge amounts of scRNA-seq data, which greatly advance the research of many biomedical fields involving tissue heterogeneity, ...
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Article
Open AccessLatent feature reconstruction for unsupervised anomaly detection
Anomalies (or outliers) indicate a minority of data items that are quite different from the majority (inliers) of a dataset in a certain aspect. Unsupervised anomaly detection (UAD) is an important but not yet...
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Article
Open AccessIdentifying essential proteins from protein–protein interaction networks based on influence maximization
Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, ...
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Article
Privacy and efficiency guaranteed social subgraph matching
Due to the increasing cost of data storage and computation, more and more graphs (e.g., web graphs, social networks) are outsourced and analyzed in the cloud. However, there is growing concern on the privacy o...
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Chapter
GANs for Molecule Generation in Drug Design and Discovery
The goal of drug design and discovery is to find new molecules with desirable properties. To this end, molecule generation is usually employed to generate novel molecules to build a virtual molecule library fo...
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Article
Open AccessProtein–protein interaction prediction based on ordinal regression and recurrent convolutional neural networks
Protein protein interactions (PPIs) are essential to most of the biological processes. The prediction of PPIs is beneficial to the understanding of protein functions and thus is helpful to pathological analysi...
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Article
Classification of Marine Plankton Based on Few-shot Learning
The current computer vision usually requires abundant training samples to classify target images, while it requires only a small number of samples if it is the same task for humans. This article attempts to ad...
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Article
Open AccessBoosting scRNA-seq data clustering by cluster-aware feature weighting
The rapid development of single-cell RNA sequencing (scRNA-seq) enables the exploration of cell heterogeneity, which is usually done by scRNA-seq data clustering. The essence of scRNA-seq data clustering is to...
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Chapter and Conference Paper
FedMDR: Federated Model Distillation with Robust Aggregation
This paper presents FedMDR, a federated model distillation framework with a novel, robust aggregation mechanism that exploits transfer learning and knowledge distillation. FedMDR adopts a weighted geometric-me...
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Article
Open AccessComputationally identifying hot spots in protein-DNA binding interfaces using an ensemble approach
Protein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface bindin...
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Chapter and Conference Paper
Optimal Trade Execution Based on Deep Deterministic Policy Gradient
In this paper, we address the Optimal Trade Execution (OTE) problem over the limit order book mechanism, which is about how best to trade a given block of shares at minimal cost or for maximal return. To this end...
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Article
Open AccessDEEPSEN: a convolutional neural network based method for super-enhancer prediction
Super-enhancers (SEs) are clusters of transcriptional active enhancers, which dictate the expression of genes defining cell identity and play an important role in the development and progression of tumors and ...
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Article
Open AccessSingle-cell trajectories reconstruction, exploration and map** of omics data with STREAM
Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several metho...
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Article
Open AccessGenome-wide analysis of epigenetic dynamics across human developmental stages and tissues
Epigenome is highly dynamic during the early stages of embryonic development. Epigenetic modifications provide the necessary regulation for lineage specification and enable the maintenance of cellular identity...
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Chapter and Conference Paper
Simple Is Better: A Global Semantic Consistency Based End-to-End Framework for Effective Zero-Shot Learning
In image recognition, there are many cases where training samples cannot cover all target classes. Zero-shot learning (ZSL) addresses such cases by classifying the samples of unseen categories that have no cor...
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Article
Open AccessClassifying early and late mild cognitive impairment stages of Alzheimer’s disease by fusing default mode networks extracted with multiple seeds
The default mode network (DMN) in resting state has been increasingly used in disease diagnosis since it was found in 2001. Prior work has mainly focused on extracting a single DMN with various techniques. How...
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Article
Open AccessIdentification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method
Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of sing...
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Article
Open AccessLink Prediction based on Quantum-Inspired Ant Colony Optimization
Incomplete or partial observations of network structures pose a serious challenge to theoretical and engineering studies of real networks. To remedy the missing links in real datasets, topology-based link pred...
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Article
Open AccessCPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations
Effectively predicting protein complexes not only helps to understand the structures and functions of proteins and their complexes, but also is useful for diagnosing disease and develo** new drugs. Up to now...
