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Search · four archives
26 papers · ranked by Valyu relevance
Liming Cheng, Jiaqi Xiong, Junwei Duan, Yuhang Zhang + 4 more
'Jingxin Zhong' 'Zhiguo Zhou' 'Yujuan Quan'] Introduction Epilepsy is a common neurological condition that affects a large number of individuals worldwide. One of the primary challenges in epilepsy is the accurate and timely detection of seizure. Recently, the graph regularized broad learning system (GBLS) has achieved…
Zhiyi Zhang, Mingyi Yang, Cheng Xie, Zhigang Xu + 2 more
To address the nonlinear dynamics and strong multivariate coupling inherent in complex industrial data, while overcoming the high computational costs and deployment challenges of deep learning, this paper proposes a Channel-Independent Anchor Graph-Regularized Broad Learning System (CI-GBLS). First, a Channel…
Yogesh Kumar, Manju, Mudasir Ganaie
The Broad Learning System (BLS) has been widely used for data classification and is based on a layer-by-layer feed-forward structure. However, it gives the same importance to all data points, which reduces its effectiveness on real-world datasets with noise and outliers. In addition, it does not consider the geometric…
Shuangxia Ren, Yifeng Tao, Ke Yu, Yifan Xue + 2 more
Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unseen by AI models) will respond to any of a set of existing anticancer drugs, based on responses of previous training cell samples to those drugs. To expand the repertoire…
Ethan Shen, Maria Brbić, Nicholas Monath, Jiaqi Zhai + 2 more
'Jure Leskovec'] In many domains, relationships between categories are encoded in the knowledge graph. Recently, promising results have been achieved by incorporating knowledge graph as side information in hard classification tasks with severely limited data. However, prior models consist of highly complex…
Huimin Luo, Hui Yang, Ge Zhang, Jianlin Wang + 2 more
'Chaokun Yan'] Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep…
Xiaoyu Li, Shaobo Li, Peng Zhou, Guanglin Chen + 9 more
'Yunfei Gao' 'Marcin Sosnowski' 'Karolina Grabowska' 'Dorian Skrobek' 'Ghulam Moeen Uddin' 'Anna Kulakowska' 'Anna Zylka' 'Bachil El Fil'] In this paper, we propose a broad learning system based on the sparrow search algorithm. Firstly, in order to avoid the complicated manual parameter tuning process and obtain the…
Qiyu Gong, Kai Cao, Jackson A. Weir, Qitian Wu + 2 more
Spatial omics map gene and protein expression in situ, demanding methods that recover cellular and tissue architecture from noisy, high-dimensional data. This need spans two scales: (i) subcellular, where high-resolution measurements must be grouped into coherent cells, and (ii) tissue-wide, where the goal is to…
MingXue Liu, Min Wang, Baolei Li, Qi Zhong + 1 more
Recommender systems based on collaborative filtering (CF) have been a prominent area of research. In recent years, graph neural networks (GNN) based CF models have effectively addressed the limitations of nonlinearity and higher-order feature interactions in traditional recommendation methods, such as matrix…
Jiao Liu, Mingbo Zhao, Weijian Kong
Dimensionality reduction has always been a major problem for handling huge dimensionality datasets. Due to the utilization of labeled data, supervised dimensionality reduction methods such as Linear Discriminant Analysis tend achieve better classification performance compared with unsupervised methods. However…
Minxiang Ye, Vladimir Stanković, Lina Stanković, Gene Cheung
—Convolutional neural network (CNN)-based feature learning has become state of the art, since given sufficient training data, CNN can significantly outperform traditional methods for various classification tasks. However, feature learning becomes more difficult if some training labels are noisy. With traditional…
Haiyang Liu, Hanlin Zhang, Li Guo, Jia Yu + 1 more
With the rapid development of artificial intelligence and the advent of the 5G era, deep learning has received extensive attention from researchers. Broad Learning System (BLS) is a new deep learning model proposed recently, which shows its effectiveness in many fields, such as image recognition and fault detection.…
Cheng Shi, Liming Pan, Hong Hu, Ivan Dokmanić
Title: Significance Graph neural networks (GNNs) have been applied with great success across science and engineering, but we do not understand why they work so well. Motivated by experimental evidence of a rich phase diagram of generalization behaviors, we analyzed simple GNNs on a community graph model and derived…
Hufei Zhu
—The decremental learning algorithms are required in machine learning, to prune redundant nodes and remove obsolete inline training samples. In this paper, an efficien t decremental learning algorithm to prune redundant nodes is deduced from the incremental learning algorithm 1 proposed in [9] for added nodes, and two…
Daniel Walke, Daniel Micheel, Kay Schallert, Thilo Muth + 3 more
'David Broneske' 'Gunter Saake' 'Robert Heyer'] Title: Abstract The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving…
Diba Behnoudfar, Cory Simon, Joshua Schrier
Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas…
Sanjar Adilov
Machine learning models for molecular-property prediction typically work with molecular representations in the form of fingerprints, descriptors, or graphs. In case of fingerprints and descriptors, molecular representations usually comprise thousands of features, which causes the curse of dimensionality for many…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Renming Liu, Arjun Krishnan
Over the past decades, network biology has been a major driver of computational methods developed to better understand the functional roles of each gene in the human genome in their cellular context. Following the application of traditional semi-supervised and supervised machine learning (ML) techniques, the next wave…
Srijani Bagchi, Anasua Sarkar, Ujjwal Maulik
In times like this, it is imperative to be cautious about the effects of drugs or vaccination doses on patients who are already suffering from other serious diseases. It’s not only the virus which can affect the body metabolisms, drugs to encounter the virus may also end up having unwanted negative effects. Therapeutic…
Authors not listed
Graph Neural Networks (GNNs) have emerged as a powerful tool in predicting molecular properties based on structural data. While GNNs excel in identifying local patterns within molecules, their ability to capture global properties remains limited due to inherent structural challenges such as oversmoothing and their…
Raphaël Mourad
Genome-wide association studies have systematically identified thousands of single nucleotide polymorphisms (SNPs) associated with complex genetic diseases. However, the majority of those SNPs were found in non-coding genomic regions, preventing the understanding of the underlying causal mechanism. Predicting molecular…
Katherine H. Shutta, Laura B. Balzer, Denise M. Scholtens, Raji Balasubramanian
Multivariate biological data are often modeled using networks in which nodes represent a biological variable (e.g., genes) and edges represent associations (e.g., coexpression). A Gaussian graphical model (GGM), or partial correlation network, is an undirected graphical model in which a weighted edge between two nodes…
Diane Duroux, Kristel Van Steen
Many problems in life sciences can be brought back to a comparison of graphs. Even though a multitude of such techniques exist, often, these assume prior knowledge about the partitioning or the number of clusters and fail to provide statistical significance of observed between-network heterogeneity. Addressing these…
Cailum Stienstra, Liam Hebert, Patrick Thomas, Alexander Haack + 2 more
Given that Infrared (IR) spectroscopy is a crucial tool in various chemical and forensic domains, improved in silico methods for predicting experimental spectra are needed due to the time and accuracy limitations of ab initio methods. We employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra…
Amer El-Samman, Stijn De Baerdemacker
In deep learning methods, especially in the context of chemistry, there is an increasing urgency to uncover the hidden learning mechanisms often dubbed as ``black box." In this work, we show that graph models built on computational chemical data behave similar to natural language processing (NLP) models built on text…