Unsupervised Dual Deep Hashing with Semantic-Index and Content-Code for Cross-Modal Retrieval.

Hashing technology has exhibited great cross-modal retrieval potential due to its appealing retrieval efficiency and storage effectiveness. Most current supervised cross-modal retrieval methods heavily rely on accurate semantic supervision, which is intractable for annotations with ever-growing sample sizes. By comparison, the existing unsupervised methods rely on accurate sample similarity preservation strategies with intensive computational costs to compensate for the lack of semantic guidance, which causes these methods to lose the power to bridge the semantic gap.

A Method for Inferring Polymers Based on Linear Regression and Integer Programming.

A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In this paper, we design a new method for inferring a polymer based on the framework. For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers.

A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological networks.

Network controllability is unifying the traditional control theory with the structural network information rooted in many large-scale biological systems of interest, from intracellular networks in molecular biology to brain neuronal networks. In controllability approaches, the set of minimum driver nodes is not unique, and critical nodes are the most important control elements because they appear in all possible solution sets. On the other hand, a common but largely unexplored feature in network control approaches is the probabilistic failure of edges or the uncertainty in the determination of interactions between molecules.

Analysis of Carcinogenic Involvement of MicroRNA Pattern in Peripheral Non-Cancerous Tissues and Chronic Viral Liver Injury.

Risk factors for hepatocarcinogenesis include chronic inflammation due to viral infection, liver fibrosis, and aging. In this study, we separated carcinogenic and non-carcinogenic cases due to hepatitis C virus (HCV) infection, aiming to comprehensively analyze miRNA expression in liver tissues by age, and identify factors that contribute to carcinogenesis. Total RNA was extracted from 360 chronic hepatitis C (CH), 43 HCV infected hepatocellular carcinoma (HCC), and surrounding non-tumor (SNT) tissues.

Target control of linear directed networks based on the path cover problem.

Securing complete control of complex systems comprised of tens of thousands of interconnected nodes holds immense significance across various fields, spanning from cell biology and brain science to human-engineered systems. However, depending on specific functional requirements, it can be more practical and efficient to focus on a pre-defined subset of nodes for control, a concept known as target control. While some methods have been proposed to find the smallest driver node set for target control, they either rely on heuristic approaches based on k-walk theory, lacking a guarantee of optimal solutions, or they are overly complex and challenging to implement in real-world networks.

Distributed Pinning Control: Stabilizing Large Boolean Networks Subjected to Perturbations.

Stability maintenance in systems refers to the capacity to preserve inherent stability characteristics. In this article, stability maintenance of large boolean networks (BNs) subjected to perturbations is investigated using a distributed pinning control (PC) strategy. The concept of edge removal as a form of perturbation is introduced, and several criteria for achieving global stability are established.

Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes.

A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed. The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) that simulates the computation process of two functions: a feature function defined by a two-layered model on chemical graphs and a prediction function constructed by a machine learning method. To improve the learning performance of prediction functions in the framework, we design a method that splits a given data set C into two subsets C,i=1,2 by a hyperplane in a chemical space so that most compounds in the first (resp.

Measuring criticality in control of complex biological networks.

Recent controllability analyses have demonstrated that driver nodes tend to be associated to genes related to important biological functions as well as human diseases. While researchers have focused on identifying critical nodes, intermittent nodes have received much less attention. Here, we propose a new efficient algorithm based on the Hamming distance for computing the importance of intermittent nodes using a Minimum Dominating Set (MDS)-based control model.

DiCleave: a deep learning model for predicting human Dicer cleavage sites.

Background: MicroRNAs (miRNAs) are a class of non-coding RNAs that play a pivotal role as gene expression regulators. These miRNAs are typically approximately 20 to 25 nucleotides long. The maturation of miRNAs requires Dicer cleavage at specific sites within the precursor miRNAs (pre-miRNAs).

On the Size and Width of the Decoder of a Boolean Threshold Autoencoder.

In this brief paper, we study the size and width of autoencoders consisting of Boolean threshold functions, where an autoencoder is a layered neural network whose structure can be viewed as consisting of an encoder, which compresses an input vector to a lower dimensional vector, and a decoder which transforms the low-dimensional vector back to the original input vector exactly (or approximately). We focus on the decoder part and show that [Formula: see text] and O(√{Dn}) nodes are required to transform n vectors in d -dimensional binary space to D -dimensional binary space. We also show that the width can be reduced if we allow small errors, where the error is defined as the average of the Hamming distance between each vector input to the encoder part and the resulting vector output by the decoder.

SMG: self-supervised masked graph learning for cancer gene identification.

Cancer genomics is dedicated to elucidating the genes and pathways that contribute to cancer progression and development. Identifying cancer genes (CGs) associated with the initiation and progression of cancer is critical for characterization of molecular-level mechanism in cancer research. In recent years, the growing availability of high-throughput molecular data and advancements in deep learning technologies has enabled the modelling of complex interactions and topological information within genomic data.

Multi-shelled ECIF: improved extended connectivity interaction features for accurate binding affinity prediction.

Motivation: Extended connectivity interaction features (ECIF) is a method developed to predict protein-ligand binding affinity, allowing for detailed atomic representation. It performed very well in terms of Comparative Assessment of Scoring Functions 2016 (CASF-2016) scoring power. However, ECIF has the limitation of not being able to adequately account for interatomic distances.

Whole-Brain Evaluation of Cortical Microconnectomes.

The brain is an organ that functions as a network of many elements connected in a nonuniform manner. In the brain, the neocortex is evolutionarily newest and is thought to be primarily responsible for the high intelligence of mammals. In the mature mammalian brain, all cortical regions are expected to have some degree of homology, but have some variations of local circuits to achieve specific functions performed by individual regions.

ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction.

Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data.

AQDnet: Deep Neural Network for Protein-Ligand Docking Simulation.

We have developed an innovative system, AI QM Docking Net (AQDnet), which utilizes the three-dimensional structure of protein-ligand complexes to predict binding affinity. This system is novel in two respects: first, it significantly expands the training dataset by generating thousands of diverse ligand configurations for each protein-ligand complex and subsequently determining the binding energy of each configuration through quantum computation. Second, we have devised a method that incorporates the atom-centered symmetry function (ACSF), highly effective in describing molecular energies, for the prediction of protein-ligand interactions.

iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.

Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic.

eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio-temporal architectures of cells.

Background: Bioinformatics capability to analyze spatio-temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided.

Common Attractors in Multiple Boolean Networks.

Analyzing multiple networks is important to understand relevant features among different networks. Although many studies have been conducted for that purpose, not much attention has been paid to the analysis of attractors (i.e.

ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning.

Lysine 2-hydroxyisobutylation (Khib), which was first reported in 2014, has been shown to play vital roles in a myriad of biological processes including gene transcription, regulation of chromatin functions, purine metabolism, pentose phosphate pathway and glycolysis/gluconeogenesis. Identification of Khib sites in protein substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein 2-hydroxyisobutylation. Experimental identification of Khib sites mainly depends on the combination of liquid chromatography and mass spectrometry.

PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.

Motivation: The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations.

Results: Here, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins.

Metrics for RNA Secondary Structure Comparison.

RNA secondary structure comparison is one of the important analyses for elucidating individual functions of RNAs since it is widely accepted that their functions and structures are strongly correlated. However, although the RNA secondary structures with pseudoknot play important roles in vivo, it is difficult to deal with such structures in silico due to their structural complexity, which is a major obstacle to the analysis of RNA functions.Here, we introduce an algorithm and a metric for comparing pseudoknotted RNA secondary structures based on topological centroid identification and tree edit distance and describe the usage protocol of a software enabling us to run the comparison.

Densest subgraph-based methods for protein-protein interaction hot spot prediction.

Background: Hot spots play an important role in protein binding analysis. The residue interaction network is a key point in hot spot prediction, and several graph theory-based methods have been proposed to detect hot spots. Although the existing methods can yield some interesting residues by network analysis, low recall has limited their abilities in finding more potential hot spots.

MSNet-4mC: learning effective multi-scale representations for identifying DNA N4-methylcytosine sites.

Motivation: N4-methylcytosine (4mC) is an essential kind of epigenetic modification that regulates a wide range of biological processes. However, experimental methods for detecting 4mC sites are time-consuming and labor-intensive. As an alternative, computational methods that are capable of automatically identifying 4mC with data analysis techniques become a reasonable option.