paper reading list

A conserved fertilization complex bridges sperm and egg in vertebrates AlphaFold-Multimer This approach employs structural predictions of pairwise protein-protein interactions between each bait protein and the candidate proteins. A high interface-predicted template modeling score (ipTM score) reflects high confidence in the relative positioning of the interfaces.

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data mapping between transcriptome and connectome

neural type

blog

Automatic discovery of cell types and microcircuitry from neural connectomics
Eric Jonas, Konrad Kording
It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses.
Connectivity of single neurons classifies cell subtypes in mouse brains Neuronal Connectivity as a Determinant of Cell Types and Subtypes
Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons
SI Bayesian method

p(\mathbf{y} \mid Q, \mathbf{f}) \propto e^{-\frac{1}{2} \mathbf{f}^{\prime} \mathbf{M f}+\mathbf{y}^{\prime} Q \mathbf{f}}

Molecularly defined and spatially resolved cell atlas of the whole mouse brain Xiaowei Zhuang
Morphological diversity of single neurons in molecularly defined cell types 2021 Nature, Hongkui Zeng, Allen Institute
Probabilistic cell typing enables fine mapping of closely related cell types in situ
Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types 9,200 models are generated that capture multimodal single-cell datasets

neural activity and connectivity

Homogenized C. elegans Neural Activity and Connectivity Data
Homogenized C. elegans Neural Activity and Connectivity Data https://arxiv.org/abs/2411.12091 Konrad P Kording, Edward S Boyden

morphology

Mapping Function Onto Neuronal Morphology Klaus M. Stiefel, Terrence J. Sejnowski
We used a Lindenmayer-system (L-system) (Lindenmayer 1968) for the algorithmic construction of model neuron morphologies. Simulations of electrophysiological dynamics were carried out in the neuronal simulation language NEURON (version 5.7) (Hines and Carnevale 1997, 2000).
Neuronal contact predicts connectivity in the C. elegans brain
Peters’ rule explains synaptic specificity throughout the C. elegans nervous system
Development of Dendritic Form and Function
Review paper, 2015, Julie L. Lefebvre, Joshua R. Sanes, and Jeremy N. Kay
One Rule to Grow Them All: A General Theory of Neuronal Branching and Its Practical Application
2010
Molecular mechanisms of dendrite morphogenesis Review, 2012, Jyothi Arikkath
Automated analysis of neuronal morphology, synapse number and synaptic recruitment
L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies
TreeMoCo: Contrastive Neuron Morphology Representation Learning
Principles of branch dynamics governing shape characteristics of cerebellar Purkinje cell dendrites Growing dendrites are retracted or stalled by contacts with other dendrites.

morphology data

NeuroMorpho.Org is a centrally curated inventorys of digitally reconstructed neurons and glia

maximum weighted independent set (MWIS)

Graph compression or sparsification

Vertex Sparsification for Edge Connectivity

Maximum Dispersion

Maximum Diversity Problem (MDP)

connectome visualization

Network Repository. An Interactive Scientific Network Data Repository. the first scientific network data repository with interactive visual analytics. new GraphVis: interactive visual graph mining and machine learning https://networkrepository.com/

human connectome

gene data

Isoforms of Terminal Selector Mamo Control Axon Guidance during Adult Drosophila Memory Center Construction via Semaphorin-1a
SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models
Benchmarking methods for mapping functional connectivity in the brain
Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously
Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells.

nature neuroscience review 2024

Science Forum: The Human Cell Atlas

gene data for Drosophila

A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain
A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain Cell, 2018
Transcriptional Programs of Circuit Assembly in the Drosophila Visual System S. Lawrence Zipursky
Neuronal diversity and convergence in a visual system developmental atlas Claude Desplan NYU, 2020
Morphological and functional convergence of visual projection neurons from diverse neurogenic origins in Drosophila Mehmet Neset Özel & Claude Desplan, January 2025,Nature Communications
Decoding gene regulation in the fly brain Stein Aerts, Nature, 2022
A complete temporal transcription factor series in the fly visual system Claude Desplan

analysis

logical gene for connectome

gene regulatory network

Decoding gene regulation in the fly brain
Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks Nature Machine Intelligence, 2022
Genome-wide identification of neuronal activity-regulated genes in Drosophila Brandeis eLife, 2016
Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning
Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis
gene coexpression in scCOP and bulkCOP

\begin{array}{|c|c|c|} \hline \text { Gene pairs } & \text { scCOPs } & \neg \text { scCOPs } \\ \hline \text { bulkCOPs } & 313 & 2993 \\ \hline \neg \text { bulkCOPs } & 3245 & 232603 \\ \hline \end{array}

Multiomic foundation model predicts epigenetic regulation by zero-shot
An atlas of gene regulatory elements in adult mouse cerebrum An anatomically comprehensive atlas of the adult human brain transcriptome https://www.nature.com/articles/nature11405

review paper

Gene regulatory network inference in the era of single-cell multi-omicsJulio Saez-Rodriguez Nature Reviews Genetics, 2023
Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data npj Systems Biology and Applications, review, 2023
A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data Briefings in Bioinformatics, 2021

sota models

application

gene normalization

functional and structure conenctome

[Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in health and disease] https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002314
[The cell-type underpinnings of the human functional cortical connectome] https://www.nature.com/articles/s41593-024-01812-2
Complex brain networks: graph theoretical analysis of structural and functional systems Ed Bullmore & Olaf Sporns (2009).
Nature reviews neuroscience
Engel, A. K., Gerloff, C., Hilgetag, C. C., & Nolte, G. (2013). Intrinsic coupling modes: multiscale interactions in ongoing brain activity. Neuron, 80(4), 867-886.
Biswal, B., F. Zerrin Yetkin, Victor M. Haughton, James S. Hyde (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magnetic Resonance in Medicine 34(4): 537-541.
Markov NT, et al. (2014) A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb Cortex 24:17–36.
Donahue, C.J., Sotiropoulos, S.N., Jbabdi, S., Hernandez-Fernandez, M., Behrens, T.E., Dyrby, T.B., Coalson, T., Kennedy, H., Knoblauch, K., Van Essen, D.C., Glasser, M.F. (2016). Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey. J. Neurosci. 36, 6758–6770. doi:10.1523/JNEUROSCI.0493-16.2016.
Honey, C., Thivierge JP, Sporns O (2010). Can structure predict function in the human brain? Neuroimage 52(3): 766-776.
Deco, G., McIntosh, A. R., Shen, K., Hutchison, R. M., Menon, R. S., Everling, S., Hagmann, P., & Jirsa, V. K. (2014). Identification of optimal structural connectivity using functional connectivity and neural modeling. The Journal of neuroscience : the official journal of the Society for Neuroscience, 34(23), 7910–7916.
Dawson, DA, Cha K, Lewis LB, Mendola JD, Shmuel A (2013). "Evaluation and calibration of functional network modeling methods based on known anatomical connections." Neuroimage 67: 331-343.
Predicting brain structural network using functional connectivity. Medical image analysis, 79, 102463. https://doi.org/10.1016/j.media.2022.102463.
Predicting brain structural network using functional connectivityj.media
multi-GCN based GAN, AD
Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model
Topographic Axes of Wiring Space Converge to Genetic Topography in Shaping the Human Cortical Layout
A practical guide to linking brain-wide gene expression and neuroimaging data https://www.sciencedirect.com/science/article/abs/pii/S1053811919300114

other connectome model:

Modelling brain connectomes networks: Solv is a worthy competitor to hyperbolic geometry! https://openreview.net/forum?id=dqWobzlAGb

Reinforcement learning (RL)

Learning Symbolic Rules for Interpretable Deep Reinforcement Learning https://arxiv.org/abs/2103.08228

Applying Logical Rules to Reinforcement Learning for Interpretable Knowledge Graph Reasoning https://ieeexplore.ieee.org/abstract/document/10650177

A Logical Framework to Reinforcement Learning Using Hybrid Probabilistic Logic Programs https://link.springer.com/chapter/10.1007/978-3-540-87993-0_27

A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks https://ieeexplore.ieee.org/document/8431181

Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming https://arxiv.org/abs/2106.11417

Reinforcement Logic Rule Learning for Temporal Point Processes https://arxiv.org/abs/2308.06094

graph

JC: https://docs.google.com/spreadsheets/d/10R6poOIIqA4OCsiSGs5mK4Zkq9pm8aPxk1IQ3Zl0NPU
DGL 2.0: Streamlining Your GNN Data Pipeline from Bottleneck to Boost https://www.dgl.ai/ https://github.com/dmlc/dgl/blob/master/examples/README.md#agnn
The Graph Neural Network Model
Variational Graph Auto-Encoders Bayesian Deep Learning Workshop (NIPS 2016) code
Graph Attention Networks Yoshua Bengio
Graph Attention Networks
Attention-based Graph Neural Network for Semi-supervised Learning
GraphFM: A Scalable Framework for Multi-Graph Pretraining
A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions
Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective within-community connection prediction

Link prediction

https://paperswithcode.com/task/link-prediction

GraphSAGE: Inductive Representation Learning on Large Graphs GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data.
Inductive Link Prediction via Interactive Learning Across Relations in Multiplex Networks
Community-guided link prediction in multiplex networks

review

Graph Embedding

review

Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks we construct the Heterogeneous Graph Benchmark (HGB), consisting of 11 diverse datasets with three tasks. | Dataset | Nodes | Node Types | Edges | Edge Types | Target | |---------|-------|------------|-------|------------|--------| | Amazon | 10,099| 1 | 148,659| 2 | product-product | | LastFM | 20,612| 3 | 141,521| 3 | user-artist | | PubMed | 63,109| 4 | 244,986| 10 | disease-disease |