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1. Accurately modeling biased random walks on weighted networks using node2vec+

   https://doi.org/10.1093/bioinformatics/btad047  

   Liu, Renming; Hirn, Matthew; Krishnan, Arjun; Martelli, ed., Pier_Luigi (January 2023, Bioinformatics)

   Abstract MotivationAccurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction networks, are dense, weighted graphs, node2vec is fundamentally limited in its ability to use edge weights during the biased random walk generation process, thus under-using all the information in the network. ResultsHere, we present node2vec+, a natural extension of node2vec that accounts for edge weights when calculating walk biases and reduces to node2vec in the cases of unweighted graphs or unbiased walks. Using two synthetic datasets, we empirically show that node2vec+ is more robust to additive noise than node2vec in weighted graphs. Then, using genome-scale functional gene networks to solve a wide range of gene function and disease prediction tasks, we demonstrate the superior performance of node2vec+ over node2vec in the case of weighted graphs. Notably, due to the limited amount of training data in the gene classification tasks, graph neural networks such as GCN and GraphSAGE are outperformed by both node2vec and node2vec+. Availability and implementationThe data and code are available on GitHub at https://github.com/krishnanlab/node2vecplus_benchmarks. All additional data underlying this article are available on Zenodo at https://doi.org/10.5281/zenodo.7007164. Supplementary informationSupplementary data are available at Bioinformatics online. 

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2. The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent

   Sankararaman, Karthik A; De, Soham; Xu, Zheng; Huang, Ronny; Goldstein, Tom (March 2020, International Conference on Machine Learning)

   This paper studies how neural network architecture affects the speed of training. We introduce a simple concept called gradient confusion to help formally analyze this. When gradient confusion is high, stochastic gradients produced by different data samples may be negatively correlated, slowing down convergence. But when gradient confusion is low, data samples interact harmoniously, and training proceeds quickly. Through theoretical and experimental results, we demonstrate how the neural network architecture affects gradient confusion, and thus the efficiency of training. Our results show that, for popular initialization techniques, increasing the width of neural networks leads to lower gradient confusion, and thus faster model training. On the other hand, increasing the depth of neural networks has the opposite effect. Our results indicate that alternate initialization techniques or networks using both batch normalization and skip connections help reduce the training burden of very deep networks. 

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3. A multimodal graph neural network framework for cancer molecular subtype classification

   https://doi.org/10.1186/s12859-023-05622-4  

   Li, Bingjun; Nabavi, Sheida (January 2024, BMC Bioinformatics)

   Abstract BackgroundThe recent development of high-throughput sequencing has created a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtypes. Integrating multi-omics data has been proven to be effective for building more precise classification models. Most current multi-omics integrative models use either an early fusion in the form of concatenation or late fusion with a separate feature extractor for each omic, which are mainly based on deep neural networks. Due to the nature of biological systems, graphs are a better structural representation of bio-medical data. Although few graph neural network (GNN) based multi-omics integrative methods have been proposed, they suffer from three common disadvantages. One is most of them use only one type of connection, either inter-omics or intra-omic connection; second, they only consider one kind of GNN layer, either graph convolution network (GCN) or graph attention network (GAT); and third, most of these methods have not been tested on a more complex classification task, such as cancer molecular subtypes. ResultsIn this study, we propose a novel end-to-end multi-omics GNN framework for accurate and robust cancer subtype classification. The proposed model utilizes multi-omics data in the form of heterogeneous multi-layer graphs, which combine both inter-omics and intra-omic connections from established biological knowledge. The proposed model incorporates learned graph features and global genome features for accurate classification. We tested the proposed model on the Cancer Genome Atlas (TCGA) Pan-cancer dataset and TCGA breast invasive carcinoma (BRCA) dataset for molecular subtype and cancer subtype classification, respectively. The proposed model shows superior performance compared to four current state-of-the-art baseline models in terms of accuracy, F1 score, precision, and recall. The comparative analysis of GAT-based models and GCN-based models reveals that GAT-based models are preferred for smaller graphs with less information and GCN-based models are preferred for larger graphs with extra information. 

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4. Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers

   Tsoi, Nathan; Candon, Kate; Li, Deyuan; Milkessa, Yofti; Vázquez, Marynel (October 2022, Advances in neural information processing systems)

   Koyejo, S.; Mohamed, S.; Agarwal, A.; Belgrave, D.; Cho, K.; Oh, A. (Ed.)

   While neural network binary classifiers are often evaluated on metrics such as Accuracy and F1-Score, they are commonly trained with a cross-entropy objective. How can this training-evaluation gap be addressed? While specific techniques have been adopted to optimize certain confusion matrix based metrics, it is challenging or impossible in some cases to generalize the techniques to other metrics. Adversarial learning approaches have also been proposed to optimize networks via confusion matrix based metrics, but they tend to be much slower than common training methods. In this work, we propose a unifying approach to training neural network binary classifiers that combines a differentiable approximation of the Heaviside function with a probabilistic view of the typical confusion matrix values using soft sets. Our theoretical analysis shows the benefit of using our method to optimize for a given evaluation metric, such as F1-Score, with soft sets, and our extensive experiments show the effectiveness of our approach in several domains. 

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5. #Confused and beyond: detecting confusion in course forums using students' hashtags

   https://doi.org/10.1145/3375462.3375485  

   Geller, Shay A.; Hoernle, Nicholas; Gal, Kobi; Segal, Avi; Zhang, Amy X.; Karger, David; Facciotti, Marc T.; Igo, Michele (March 2020, LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge)

   Students' confusion is a barrier for learning, contributing to loss of motivation and to disengagement with course materials. However, detecting students' confusion in large-scale courses is both time and resource intensive. This paper provides a new approach for confusion detection in online forums that is based on harnessing the power of students' self-reported affective states (reported using a set of pre-defined hashtags). It presents a rule for labeling confusion, based on students' hashtags in their posts, that is shown to align with teachers' judgement. We use this labeling rule to inform the design of an automated classifier for confusion detection for the case when there are no self-reported hashtags present in the test set. We demonstrate this approach in a large scale Biology course using the Nota Bene annotation platform. This work lays the foundation to empower teachers with better support tools for detecting and alleviating confusion in online courses. 

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