ArXiv最新文献

{"title":"VEPerform: a web resource for evaluating the performance of variant effect predictors.","authors":"Cindy Zhang, Frederick P Roth","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Computational variant effect predictors (VEPs) are providing increasingly strong evidence to classify the pathogenicity of missense variants. Precision vs. recall analysis is useful in evaluating VEP performance, especially when adjusted for imbalanced test sets. Here, we describe VEPerform, a web-based tool for evaluating the performance of VEPs at the gene level using balanced precision vs. recall curve (BPRC) analysis.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11661297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metastability in networks of nonlinear stochastic integrate-and-fire neurons.","authors":"Siddharth Paliwal, Gabriel Koch Ocker, Braden A W Brinkman","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Neurons in the brain continuously process the barrage of sensory inputs they receive from the environment. A wide array of experimental work has shown that the collective activity of neural populations encodes and processes this constant bombardment of information. How these collective patterns of activity depend on single-neuron properties is often unclear. Single-neuron recordings have shown that individual neurons' responses to inputs are nonlinear, which prevents a straight-forward extrapolation from single neuron features to emergent collective states. Here, we use a field-theoretic formulation of a stochastic leaky integrate-and-fire model to study the impact of single-neuron nonlinearities on macroscopic network activity. In this model, a neuron integrates spiking output from other neurons in its membrane voltage and emits spikes stochastically with an intensity depending on the membrane voltage, after which the voltage resets. We show that the interplay between nonlinear spike intensity functions and membrane potential resets can i) give rise to metastable active firing rate states in recurrent networks, and ii) can enhance or suppress mean firing rates and membrane potentials in the same or paradoxically opposite directions.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11213153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141473422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential learning kinetics govern the transition from memorization to generalization during in-context learning.","authors":"Alex Nguyen, Gautam Reddy","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Transformers exhibit in-context learning (ICL): the ability to use novel information presented in the context without additional weight updates. Recent work shows that ICL emerges when models are trained on a sufficiently diverse set of tasks and the transition from memorization to generalization is sharp with increasing task diversity. One interpretation is that a network's limited capacity to memorize favors generalization. Here, we examine the mechanistic underpinnings of this transition using a small transformer applied to a synthetic ICL task. Using theory and experiment, we show that the sub-circuits that memorize and generalize can be viewed as largely independent. The relative <i>rates</i> at which these sub-circuits learn explains the transition from memorization to generalization, rather than capacity constraints. We uncover a memorization scaling law, which determines the task diversity threshold at which the network generalizes. The theory quantitatively explains a variety of other ICL-related phenomena, including the long-tailed distribution of when ICL is acquired, the bimodal behavior of solutions close to the task diversity threshold, the influence of contextual and data distributional statistics on ICL, and the transient nature of ICL.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11661294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sampling-based Continuous Optimization with Coupled Variables for RNA Design.","authors":"Wei Yu Tang, Ning Dai, Tianshuo Zhou, David H Mathews, Liang Huang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The task of RNA design given a target structure aims to find a sequence that can fold into that structure. It is a computationally hard problem where some version(s) have been proven to be NP-hard. As a result, heuristic methods such as local search have been popular for this task, but by only exploring a fixed number of candidates. They can not keep up with the exponential growth of the design space, and often perform poorly on longer and harder-to-design structures. We instead formulate these discrete problems as continuous optimization, which starts with a distribution over all possible candidate sequences, and uses gradient descent to improve the expectation of an objective function. We define novel distributions based on coupled variables to rule out invalid sequences given the target structure and to model the correlation between nucleotides. To make it universally applicable to any objective function, we use sampling to approximate the expected objective function, to estimate the gradient, and to select the final candidate. Compared to <i>the</i> state-of-the-art methods, our work consistently outperforms them in key metrics such as Boltzmann probability, ensemble defect, and energy gap, especially on long and hard-to-design puzzles in the Eterna100 benchmark. Our code is available at: http://github.com/weiyutang1010/ncrna_design.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11661286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-speed scattering polarimetry for correlative nerve fiber imaging and multi-modal analysis.","authors":"Franca Auf der Heiden, Markus Axer, Katrin Amunts, Miriam Menzel","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Three-Dimensional Polarized Light Imaging (3D-PLI) and Computational Scattered Light Imaging (ComSLI) map dense nerve fibers in brain sections with micrometer resolution using visible light. 3D-PLI reconstructs single fiber orientations, while ComSLI captures multiple directions per pixel, offering deep insights into brain tissue structure. Here, we introduce the Scattering Polarimeter, a high-speed correlative microscope to leverage the strengths of both methods. Based on a Müller polarimeter, it incorporates variable retarders and a large-area light source for direct and oblique illumination, enabling rapid 3D-PLI and ComSLI measurements as well as measuring the Müller matrix per pixel. Applied to human and vervet monkey brain sections, the Scattering Polarimeter generates results comparable to state-of-the-art 3D-PLI and ComSLI setups and creates a multi-modal fiber direction map, integrating the robust fiber orientations obtained from 3D-PLI with fiber crossings from ComSLI. Furthermore, we discuss applications of the Scattering Polarimeter for unprecedented correlative and multi-modal brain imaging.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11661287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepNose: An Equivariant Convolutional Neural Network Predictive Of Human Olfactory Percepts.","authors":"Sergey Shuvaev, Khue Tran, Khristina Samoilova, Cyrille Mascart, Alexei Koulakov","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The olfactory system employs responses of an ensemble of odorant receptors (ORs) to sense molecules and to generate olfactory percepts. Here we hypothesized that ORs can be viewed as 3D spatial filters that extract molecular features relevant to the olfactory system, similarly to the spatio-temporal filters found in other sensory modalities. To build these filters, we trained a convolutional neural network (CNN) to predict human olfactory percepts obtained from several semantic datasets. Our neural network, the DeepNose, produced responses that are approximately invariant to the molecules' orientation, due to its equivariant architecture. Our network offers high-fidelity perceptual predictions for different olfactory datasets. In addition, our approach allows us to identify molecular features that contribute to specific perceptual descriptors. Because the DeepNose network is designed to be aligned with the biological system, our approach predicts distinct perceptual qualities for different stereoisomers. The architecture of the DeepNose relying on the processing of several molecules at the same time permits inferring the perceptual quality of odor mixtures. We propose that the DeepNose network can use 3D molecular shapes to generate high-quality predictions for human olfactory percepts and help identify molecular features responsible for odor quality.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11661275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the linear scaling of entropy vs. energy in human brain activity, the Hagedorn temperature and the Zipf law.","authors":"Dante R Chialvo, Romuald A Janik","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>It is well established that the brain spontaneously traverses through a very large number of states. Nevertheless, despite its relevance to understanding brain function, a formal description of this phenomenon is still lacking. To this end, we introduce a machine learning based method allowing for the determination of the probabilities of all possible states at a given coarse-graining, from which all the thermodynamics can be derived. This is a challenge not unique to the brain, since similar problems are at the heart of the statistical mechanics of complex systems. This paper uncovers a linear scaling of the entropies and energies of the brain states, a behaviour first conjectured by Hagedorn to be typical at the limiting temperature in which ordinary matter disintegrates into quark matter. Equivalently, this establishes the existence of a Zipf law scaling underlying the appearance of a wide range of brain states. Based on our estimation of the density of states for large scale functional magnetic resonance imaging (fMRI) human brain recordings, we observe that the brain operates asymptotically at the Hagedorn temperature. The presented approach is not only relevant to brain function but should be applicable for a wide variety of complex systems.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11275682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141790316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation-weighted ²³Na magnetic resonance fingerprinting in the brain.","authors":"Lauren F O'Donnell, Gonzalo G Rodriguez, Gregory Lemberskiy, Zidan Yu, Olga Dergachyova, Martijn Cloos, Guillaume Madelin","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We developed a new sodium magnetic resonance fingerprinting (²³Na MRF) method for the simultaneous mapping of <math> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn></mrow> </msub> <mo>,</mo> <mspace></mspace> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn> <mo>,</mo> <mtext>long</mtext></mrow> <mrow><mi>*</mi></mrow> </msubsup> <mo>,</mo> <mspace></mspace> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn> <mo>,</mo> <mtext>short</mtext></mrow> <mrow><mi>*</mi></mrow> </msubsup> </math> and sodium density with built-in <math><mi>Δ</mi> <msubsup><mrow><mi>B</mi></mrow> <mrow><mn>1</mn></mrow> <mrow><mo>+</mo></mrow> </msubsup> </math> (radiofrequency transmission inhomogeneities) and <math><mi>Δ</mi> <msub><mrow><mi>f</mi></mrow> <mrow><mn>0</mn></mrow> </msub> </math> corrections (frequency offsets). We based our ²³Na MRF implementation on a 3D FLORET sequence with 23 radiofrequency pulses. To capture the complex spin <math> <mfrac><mrow><mn>3</mn></mrow> <mrow><mn>2</mn></mrow> </mfrac> </math> dynamics of the ²³Na nucleus, the fingerprint dictionary was simulated using the irreducible spherical tensor operators formalism. The dictionary contained 831,512 entries covering a wide range of <math> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn></mrow> </msub> <mo>,</mo> <mspace></mspace> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn> <mo>,</mo> <mtext>long</mtext></mrow> <mrow><mi>*</mi></mrow> </msubsup> <mo>,</mo> <mspace></mspace> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn> <mo>,</mo> <mtext>short</mtext></mrow> <mrow><mi>*</mi></mrow> </msubsup> <mo>,</mo> <mspace></mspace> <mi>Δ</mi> <msubsup><mrow><mi>B</mi></mrow> <mrow><mn>1</mn></mrow> <mrow><mo>+</mo></mrow> </msubsup> </math> factor and <math><mi>Δ</mi> <msub><mrow><mi>f</mi></mrow> <mrow><mn>0</mn></mrow> </msub> </math> parameters. Fingerprint matching was performed using the Pearson correlation and the resulting relaxation maps were weighted with a subset of the highest correlation coefficients corresponding to signal matches for each voxel. Our ²³Na MRF method was compared against reference methods in a 7-compartment phantom, and applied in brain in five healthy volunteers at 7 T. In phantoms, ²³Na MRF produced values comparable to those obtained with reference methods. Average sodium relaxation time values in cerebrospinal fluid, gray matter and white matter across five healthy volunteers were in good agreement with values previously reported in the literature.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11661289/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multimodal ensemble approach for clear cell renal cell carcinoma treatment outcome prediction.","authors":"Meixu Chen, Kai Wang, Payal Kapur, James Brugarolas, Raquibul Hannan, Jing Wang","doi":"","DOIUrl":"","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;A reliable and comprehensive cancer prognosis model for clear cell renal cell carcinoma (ccRCC) could better assist in personalizing treatment. In this work, we developed a multi-modal ensemble model (MMEM) which integrates pretreatment clinical information, multi-omics data, and histopathology whole slide image (WSI) data to learn complementary information to predict overall survival (OS) and disease-free survival (DFS) for patients with ccRCC.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods and materials: &lt;/strong&gt;We collected 226 patients from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma dataset (TCGA-KIRC). These patients have OS and DFS follow up data available and five different data modalities provided, including clinical information, pathology data in the form of WSI, and three multi-omics data, which comprise mRNA expression, miRNA expression (miRSeq), and DNA methylation data. Five sets of separate survival prediction models were constructed separately for OS and DFS. We used a traditional Cox-proportional hazards (CPH) model with iterative forward feature selection for clinical and multi-omics data. Four different types of pre-trained encoder models, comprising ResNet and three recently developed general purpose foundation models for computational pathology, were utilized to extract features from processed WSI patches. A deep learning-based CPH model was constructed to predict survival outcomes using these encoded WSI features. For each of the survival outcomes of interest, we weigh and combine the predicted risk scores from all the five models to generate the final prediction. Model weighting was based on the training performance. Five-fold cross validation was performed to train and test the proposed workflow.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We employed the concordance index (C-index) and area under the receiver operating characteristic curve (AUROC) metrics to assess the performance of our models for time-to-event prediction and time-specific binary prediction, respectively. Among the sub-models, the clinical feature based CPH model has the highest weight for both prediction tasks. For WSI-based prediction, the encoded feature using an image-based general purpose foundation model (UNI) showed the best prediction performance over other pretrained feature encoders. Our final model outperformed corresponding single-modality models on all prediction labels, achieving C-indices of 0.820 and 0.833 for OS and DFS, respectively. The AUROC values for binary prediction at follow-up of 3 year were 0.831 and 0.862 for patient death and cancer recurrence, respectively. Using the medians of predicted risks as thresholds to identify high-risk and low-risk patient groups, we performed log-rank tests, which revealed improved performance in both OS and DFS compared to single-modality models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;We developed the first multi-modal prediction model MMEM for ccRCC patients that integrates features across fi","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11661283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Poisson Variational Autoencoder.","authors":"Hadi Vafaii, Dekel Galor, Jacob L Yates","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Variational autoencoders (VAEs) employ Bayesian inference to interpret sensory inputs, mirroring processes that occur in primate vision across both ventral (Higgins et al., 2021) and dorsal (Vafaii et al., 2023) pathways. Despite their success, traditional VAEs rely on continuous latent variables, which deviates sharply from the discrete nature of biological neurons. Here, we developed the Poisson VAE (P-VAE), a novel architecture that combines principles of predictive coding with a VAE that encodes inputs into discrete spike counts. Combining Poisson-distributed latent variables with predictive coding introduces a metabolic cost term in the model loss function, suggesting a relationship with sparse coding which we verify empirically. Additionally, we analyze the geometry of learned representations, contrasting the P-VAE to alternative VAE models. We find that the P-VAE encodes its inputs in relatively higher dimensions, facilitating linear separability of categories in a downstream classification task with a much better (5x) sample efficiency. Our work provides an interpretable computational framework to study brain-like sensory processing and paves the way for a deeper understanding of perception as an inferential process.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11661288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}