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“The Rabin-Scott Theorum”
Whether a system is deterministic or nondeterministic is a characteristic of the model, not of the system itself. The question is meaningless for the components of a system that operate based on limited information. We are obligated to choose, or not to choose. Inevitably or not.
決定論的か非決定論的かは、〝システム〟の特性ではなく〝モデル〟の特性であり、制限された情報に基づいて振る舞うシステムの構成要素それ自身にとって、この問いは意味を為さない。どちらにしても我々には選択し、あるいは選択しない義務がある。必然に、不必然に。作為に、不作為に
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□ Sceodesic: Navigating the manifold of single-cell gene coexpression to discover interpretable gene programs
>> https://www.biorxiv.org/content/10.1101/2023.11.09.566448v1
Sceodesic melds a novel blend of differential geometry, spectral analysis, and sparse estimation to pinpoint gene expression programs that are not only specific to cell states but also robust against variations in case-control, longitudinal, or batch conditions.
Sceodesic re-analyzes fate-mapped trajectories. The logarithmic map applied to the Riemannian manifold of positive semi-definitely matrices affords a way to preserve the semantics of gene covariance while employing Euclidean distance metrics.
![](https://blogimg.goo.ne.jp/user_image/4c/23/ed881128cd845b746f61b6ac4e68479a.png)
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□ DANTEml: Multilayer network alignment based on topological assessment via embeddings
>> https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05508-5
DANTE is an algorithm for aligning dynamic networks. DANTE performs the PGNA based on: evaluating the node features for each dynamic network (i.e., temporal embedding), constructing the similarity matrix, and performing the one-to-one node mapping.
DANTEml (DANTE for MultiLayer Networks), a novel software tool for the Pairwise Global NA (PGNA) of multilayer networks, that uses topological assessment to build its own similarity matrix. DANTEml calculates the similarities between all possible pairs.
DANTEml calculates the cosine similarity b/n a simple mean of the projection weight vectors of the given node in the source network, and the vectors for each node in the target network. It employs an iterative APR based on successive permutations to maximize the Node Correctness.
![](https://blogimg.goo.ne.jp/user_image/72/72/6bd337e3261ceea5717408dbec1a377d.jpg)
□ DeLoop: a deep learning model for chromatin loop prediction from sparse ATAC-seq data
>> https://www.biorxiv.org/content/10.1101/2023.11.01.564594v1
DeLoop, a deep learning model by leveraging multitask learning techniques and attention mechanisms to predict CTCF-media chromatin loops from sparse ATAC-seq data and DNA sequence features.
DeLoop task two four channels one-hot encoded DNA sequence with the length of 2048bp and two 1-channel accessibility signals attained from ATAC-seq data.
The DeLoop architecture is characterized by DenseNet-based feature extractors and a transformer-based integration module. DeLoop ensures that each layer directly accesses output gradients during backpropagation, leading to faster network convergence.
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□ NASTRA: Innovative Short Tandem Repeat Analysis through Cluster-Based Structure-Aware Algorithm in Nanopore Sequencing Data
>> https://www.biorxiv.org/content/10.1101/2023.11.04.565630v1
NASTRA, a tool for accurate STR genotyping with nanopore sequencing, which uses an STR-structure-aware algorithm to infer repeat numbers of STR motifs. NASTRA determines homo/heterozygosity on genotyped alleles based on the SN of alleles and the SNR between different alleles.
NASTRA comprise two main sub-algorithms, read clustering and repeat structure inference, which mitigates the potential impact of subtle sequencing errors on accurate genotyping and genotypes STR without the need for allele reference database.
NASTRA retrieves aligned reads that span a designated STR locus based on positional information from the BAM file. The prefix and suffix flanking sequences are individually aligned against the extracted reads, employing an affine-gap penalty.
NASTRA uses a recursive algorithm to infer repeat structure of allele sequences based on the repeat units present within the STR, which ensures swift acquisition of STR genotypes and aids in promptly identifying the locations of SNV in locus.
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□ ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering
>> https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05494-8
ScLSTM, a meta-learning-based single-cell clustering model. ScLSTM transforms the single-cell type detection problem into a hierarchical classification problem based on feature extraction by the siamese long-short term memory (LSTM) network.
ScLSTM employs an improved sigmoid kernel. The “siamese” of a siamese LSTM is achieved by sharing weights between two identical LSTMs. ScLSTM learns how to minimize the distance between single-cell data of the same category and maximize the distance between different categories.
![](https://blogimg.goo.ne.jp/user_image/45/57/60799eff08f774f933cc63e845b8ebee.jpg)
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□ RAFT / CGProb: Telomere-to-telomere assembly by preserving contained reads
>> https://www.biorxiv.org/content/10.1101/2023.11.07.565066v1
CGProb estimates the probability of the occurrence of a gap due to contained read deletion. CGProb takes the genome length, coverage on each haplotype, and read-length distribution as input.
CGProb estimates the probability of the occurrence of a coverage gap after a heterozygous locus on the second haplotype by counting the number of read sequencing outputs which have a coverage gap and dividing it by the total number of read sequencing outputs.
CGProb uses efficient partitioning of the sample space and ordinary generating functions to calculate the probability in polynomial time.
RAFT includes error-corrected long reads and the all-to-all pairwise alignment information. The RAFT algorithm fragments long reads into shorter, uniform-length reads while also taking into consideration the potential usefulness of the longer reads in assembling complex repeats.
![](https://blogimg.goo.ne.jp/user_image/31/e1/45037afffd6a14046b4a75347c951be1.png)
□ Deep convolutional and conditional neural networks for large-scale genomic data generation
>> https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011584
A novel generative adversarial networks with convolutional architecture and Wasserstein loss (WGAN), and Restricted Boltzmann machines with conditional training (CRBM) used together with an out-of-equilibrium procedure.
A WGAN-GP (Gradient Penalty) includes a deep generator and a deep critic architecture, multiple noise inputs at different resolutions, trainable location-specific vectors, residual blocks to prevent vanishing gradients and packing for the critic to eliminate mode collapse.
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□ ActFound: A foundation model for bioactivity prediction using pairwise meta-learning
>> https://www.biorxiv.org/content/10.1101/2023.10.30.564861v1
ActFound, a foundation model for bioactivity prediction trained on 2.3 million experimentally-measured bioactivity compounds and 50, 869 assays from ChEMBL and BindingDB. Pairwise learning is used to address the inherent incompatibility among assays.
Meta-learning is employed to jointly train the model from a large number of diverse assays, making it an initialization for new assays with limited data. ActFound utilizes a Siamese Network architecture to acquire the relative difference in bioactivity values b/n two compounds.
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□ SCALEX: Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space
>> https://www.nature.com/articles/s41467-022-33758-z
SCALEX models the global structure of single-cell data using a VAE framework. SCALEX disentangles the batch-related components away from the batch-invariant components of single-cell data and projects the batch-invariant components into a common cell-embedding space.
SCALEX includes a DSBN layer using multi-branch Batch Normalization in its decoder to support incorporation of batch-specific variations during single-cell data reconstruction. The SCALEX encoder employs a mini-batch strategy that samples data from all batches.
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□ PROLONG: Penalized Regression for Outcome guided Longitudinal Omics analysis with Network and Group constraints
>> https://www.biorxiv.org/content/10.1101/2023.11.06.565845v1
PROLONG, a penalized regression approach on the first differences of the data that extends the lasso + Laplacian method to a longitudinal group lasso + Laplacian approach.
PROLONG addresses the piecewise linear structure and the observed time dependence. PROLONG can jointly select longitudinal features that co-vary with a time-varying outcome on the first-difference scale.
The Laplacian network constraint incorporates the dependence structure of the predictors, and the group lasso constraint induces sparsity while grouping metabolites across their first differenced observations.
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□ TRAFICA: Improving Transcription Factor Binding Affinity Prediction using Large Language Model on ATAC-seq Data
>> https://www.biorxiv.org/content/10.1101/2023.11.02.565416v1
TRAFICA, a deep language model to predict TF-DNA binding affinities by integrating chromatin accessibility from ATAC-seq and known TF-DNA binding data. TRAFICA learns potential TF-DNA binding preferences and contextual relationships within DNA sequences.
TRAFICA is based on the vanilla transformer-encoder, which only utilizes the self-attention mechanism to capture contextual relationships in sequential data. The model structure consists of a token embedding layer, a position embedding layer, and 12 transformer-encoder blocks.
The feed-forward module is a stack of two fully connected layers with a non-linear activation function called Gaussian Error Linear Units (GELU), enabling the model to learn intricate dependencies between tokens.
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□ VI-VS: Calibrated Identification of Feature Dependencies in Single-cell Multiomics
>> https://www.biorxiv.org/content/10.1101/2023.11.03.565520v1
VI-VS (Variational Inference for Variable Selection) is a comprehensive framework for strike a balance b/n robustness & interpretability. VI-VS harnesses the distributional expressivity of latent variable models, allowing for a variety of noise models, incl. count distributions.
VI-VS employs deep generative models to identify conditionally dependent features, all while maintaining control over false discovery rates. These conditional dependencies are more stringent and more likely to represent genuine causal relationships.
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□ SimMCMC: Inferring delays in partially observed gene regulation processes
>> https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btad670/7342241
SimMCMC infers kinetic and delay parameters of a non-Markovian system. This method employs an approximate likelihood for the efficient and accurate inference of GRN parameters when only some of their products are observed.
A continuous-time Markov Chain efficiently explains a biochemical reaction network with a low copy number of molecules, one can also use a stochastic differential equation which is accurate when the copy numbers are higher, an agent-based model, or a delay differential equation.
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□ Generative learning for nonlinear dynamic
>>: https://arxiv.org/pdf/2311.04128.pdf
![](https://blogimg.goo.ne.jp/thumbnail/09/4c/a6fca0a6e527e6e5966c2597a1c22e1d_s.jpg)
Conversely, a completely stochastic system like a random number generator seemingly produces information, but without any underlying structure.
The complexity of a system's generator plotted against the entropy of its outputs therefore exhibits non-monotonicity with an intermediate peak suggestively termed the "edge of chaos" that can, at different times, switch between fully-ordered and seemingly random outputs.
A complexity-entropy relation could describe the intricacy of latent representations learned by large models in unsupervised settings, or the complexity of the underlying architectures necessary to achieve a given accuracy on supervised learning problems.
This dynamical refinement of the bias-variance tradeoff could inform future developments, bridging Wheeler's physical bits with the practicalities of modern large-scale learning systems.
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□ SimReadUntil for Benchmarking Selective Sequencing Algorithms on ONT Devices
>> https://www.biorxiv.org/content/10.1101/2023.11.01.565133v1
SimReadUntil simulates an ONT device w/ support for the ReadUntil, accessible both directly and via gRPC from a wide range of programming languages. It only needs FASTA files of reads, and allows to focus on the SSDA and removes the need for a GPU required by modern basecallers.
SimReadUntil takes as input a set of full reads. The reads may include adapter and barcode sequences. The (shuffled) full reads are distributed to the channels and short and long gaps are inserted between reads, where a long gap signifies a temporarily inactive channel.
SimReadUntil enables benchmarking and hyperparameter tuning of selective sequencing algorithms. The hyperparameters can be tuned to different ONT devices, e.g., a GridION with a GPU can compute more than a portable MinION/Flongle that relies on a computer.
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□ SSLpheno: A Self-Supervised Learning Approach for Gene-Phenotype Association Prediction Using Protein-Protein Interactions and Gene Ontology Data
>> https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btad662/7371298
SSLpheno utilizes an attributed network that integrates protein-protein interactions and gene ontology data. They apply a Laplacian-based filter to ensure feature smoothness and use self-supervised training to optimize node feature representation.
SSLpheno calculates the cosine similarity of feature vectors and select positive and negative sample nodes for reconstruction training labels. SSLpheno employs a deep neural network for multi-label classification of phenotypes in the downstream task.
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□ CONGAS+: A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing
>> https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011557
CONGAS+ is a Bayesian model to infer and cluster, from scRNA-seq and scATAC-seq of independents or multiomics assays, phylogenetically related clones with distinct Copy Number Alterations.
CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.
![](https://blogimg.goo.ne.jp/user_image/7e/09/4b4df2f48ca59c4305a280583832cf53.jpg)
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□ pantas: Differential quantification of alternative splicing events on spliced pangenome graphs
>> https://www.biorxiv.org/content/10.1101/2023.11.06.565751v1
pantas performs AS events differential quantification on a spliced pangenome. pantas quantifies the events by combining the results obtained from each replicate. pantas represents each AS event as a pair of sets of edges, representing the two junctions sets.
pantas also surjects the positions of the edges involved in the events back to the reference genome. This is simply done by mapping the positions of the vertices linked by each edge from the graph space to the reference genome.
![](https://blogimg.goo.ne.jp/user_image/4f/30/fd77c8a394f86356ef26d190263f5c83.jpg)
□ hipFG: High-throughput harmonization and integration pipeline for functional genomics data
>> https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btad673/7382207
hipFG (the Harmonization and Integration Pipeline for Functional Genomics), a robust and scalable pipeline for harmonizing FG datasets of diverse assay types and formats. hipFG can quickly integrate FG datasets for use with high-throughput analytical workflows.
hipFG includes datatype-specific pipelines to process diverse types of FG data. These FG datatypes are categorized into three groups: annotated genomic intervals, quantitative trait loci (QTLs), and chromatin interactions.
![](https://blogimg.goo.ne.jp/user_image/48/02/1bab35823e93c96f04900fb1e6287119.jpg)
□ Amalga: Designable Protein Backbone Generation with Folding and Inverse Folding Guidance
>> https://www.biorxiv.org/content/10.1101/2023.11.07.565939v1
Amalga, a simple yet effective inference-time technique to enhance the designability of diffusion-based backbone generators. By harnessing off-the-shelf folding and inverse folding models, Amalga guides backbone generation towards more designable conformations.
Amalga generates a set of "folded-from-inverse-folded" (FIF) structures by folding the sequences which are inverse folded from step-wise predicted backbones.
These FIF structures, being inherently designable, are aligned to the predicted backbone and input into RFdiffusion's self-conditioning channel. Intuitively, this encourages RFdiffusion to match the distribution of designable structures.
![](https://blogimg.goo.ne.jp/user_image/58/c8/d5c9182424350313738265af6aa94820.jpg)
□ MultiSTAAR: A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies
>> https://www.biorxiv.org/content/10.1101/2023.10.30.564764v1
MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits. MultiSTAAR enables the incorporation of multiple variant functional annotations as weights to improve the power of RVASs.
By fitting a null Multivariate Linear Mixed Model (MLMM) for multiple quantitative traits, adjusting for ancestry principal components and using a sparse genetic relatedness matrix (GRM), MultiSTAAR scales well but also accounts for relatedness and population structure.
![](https://blogimg.goo.ne.jp/user_image/55/eb/912e8d94cbb782cf94c34e6a3f88725a.jpg)
□ Chromoscope: interactive multiscale visualization for structural variation in human genomes
>> https://www.nature.com/articles/s41592-023-02056-x
Chromoscope enables a user to analyze structural variants at multiple scales, using four main views. Each view uses different visual representations that can facilitate the interpretation for a given level of scale.
In Chromoscope, the genomic signature is apparent as hundreds of scattered deletions and duplications are shown in the genome. In the variant view, the footprint on the copy number profiles is consistent with losses and gains caused by deletions and tandem duplications.
Chromoscope delineates other patterns of rearrangements including chromothripsis, chromoplexy, and multi-chromosomal amplifications. Chromoscope's multiscale design allowed the user to analyze both genome-wide and local manifestations of SV patterns.
![](https://blogimg.goo.ne.jp/user_image/77/dd/5168ec3289f412ede2bf94d81e6794e1.jpg)
□ RADO: Robust and Accurate Doublet Detection of Single-Cell Sequencing Data via Maximizing Area Under Precision-Recall Curve
>> https://www.biorxiv.org/content/10.1101/2023.10.30.564840v1
RADO (Robust and Accurate DOublets detection) is based on components analysis and AUPRC maximization. RADO effectively tackles data imbalance and enhances model robustness, especially when the simulated data ratio varies and the positive sample ratio is extremely low.
RADO starts with single-cell data, and then simulates doublets by averaging two random droplets. Subsequently, the KNN score is computed and integrated with the top 10 principal components to form the input features.
A logistic regression classifier is then trained using the AUPRC loss. The whole dataset's doublet annotation is finished in a cross-validation way by splitting data into many folds and making training and prediction iteratively.
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□ GraCoal: Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networks
>> https://www.biorxiv.org/content/10.1101/2023.10.27.564419v1
GraCoal (Graphlet Coalescent ) embedding maps a network onto a disk so that: (1) nodes that tend to be frequently connected by that graphlet are assigned a similar angle, and (2) so that nodes with high counts of that graphlet are near the disks' centre.
GraCoal embeddings capture different topology-function relationships. The best performing GraCoal depends on the species: either triangle-based GraCoal embeddings or GraCoal embeddings void of triangles tend to best capture the functional organisation of GI networks.
Triangle-based GraCoal embeddings capture the functional redundancy of paralogous (i.e., duplicated) genes. So, in species with many paralogs, this leads to high enrichment scores for triangle-based Gracoal embeddings.
![](https://blogimg.goo.ne.jp/user_image/5d/89/291ddf2fcd0cadb733a739e6ac3f1af3.jpg)
□ cisDynet: an integrated platform for modeling gene-regulatory dynamics and networks
>> https://www.biorxiv.org/content/10.1101/2023.10.30.564662v1
The cisDynet enables comprehensive and efficient processing of chromatin accessibility data, including pre-processing, advanced downstream data analysis and visualization.
cisDynet provides a range of analytical features such as processing of time course data, co-accessibility analysis, linking OCRs to genes, building regulatory networks, and GWAS variant enrichment analysis.
cisDynet simplifies the identification of tissue/cell type-specific OCRs or dynamic OCR changes over time and facilitates the integration of RNA-seq data to depict temporal trajectories.
![](https://blogimg.goo.ne.jp/user_image/3a/39/0a8c5fafc8fb1d8930c75c3d2d080a2e.jpg)
□ CELLSTATES: Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
>> https://www.biorxiv.org/content/10.1101/2023.10.31.564980v1
CELLSTATES directly clusters the unnormalized data so that any pre-processing steps are avoided, measurement noise is properly taken into account, and there are no free parameters to tune. The resulting clusters have a clear and simple interpretation.
Because CELLSTATES only groups cells whose expression states are statistically indistinguishable, it divides the data into many more subsets than other clustering algorithms. CELLSTATES performs extremely well on recovering the ground truth, recovering the exact partition.
![](https://blogimg.goo.ne.jp/user_image/7b/53/0b2d472eab6adaede175f83e78d36971.jpg)
□ An explainable model using Graph-Wavelet for predicting biophysical properties of proteins and measuring mutational effects
>> https://www.biorxiv.org/content/10.1101/2023.11.01.565109v1
A method based on the graph-wavelet transform of signals of features of amino acids in protein residue networks derived from their structures to achieve their abstract numerical representations.
This method outperformed graph-Fourier and convolutional neural-network-based methods in predicting the biophysical properties of proteins. This method can summarize the effect of an amino acid based on its location and neighbourhood in protein-structure using graph-wavelet.
![](https://blogimg.goo.ne.jp/user_image/4e/cf/82bf9226c3059b43197df333ca582854.jpg)
□ SPEEDI: Automated single-cell omics end-to-end framework with data-driven batch inference
>> https://www.biorxiv.org/content/10.1101/2023.11.01.564815v1
SPEEDI (Single-cell Pipeline for End-to-End Data Integration) introduces the first automated data-driven batch inference method, overcoming the problem of unknown or under-specified batch effects.
SPEEDI refines cell type annotation by introducing a majority-based voting algorithm. SPEEDI is a fully automated end-to-end QC, data-driven batch identification, data integration, and cell-type labeling that does not require any manual parameter selection or pipeline assembly.
![](https://blogimg.goo.ne.jp/user_image/67/04/8ecd1ab6cd2e5efe9dee3407e5939734.jpg)
□ CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics
>> https://www.biorxiv.org/content/10.1101/2023.11.05.565674v1
CellChat determines major signaling sources / targets, AWA mediators and influencers within a given signaling network. CellChat predicts key I/O signals for specific cell types, as well as coordinated responses among different cell types by leveraging pattern recognition.
CellChat groups signaling pathways by defining similarity measures and performing manifold learning from functional / topological perspectives. CellChat identifies altered signaling pathways and ligand-receptor pairs in terms of network architecture using joint manifold learning.