The development of antimicrobial peptides (AMPs) presents a promising approach to addressing antibiotic-resistant pathogens. Computational methods, such as Feedback Generative Adversarial Networks (FBGANs), have demonstrated strong performance in optimizing AMP design. FBGAN operates as a classifier-guided Generative Adversarial Network (GAN), refining training data by replacing them with the classifier's most accurate predictions based on a predefined threshold. However, this method may introduce bias and constrain the diversity and quality of the generated peptides. To address these limitations, we propose a novel classifier-driven GAN (cdGAN) framework that seamlessly integrates classifier predictions into the generative model's loss function. This enables an adaptive, end-to-end learning process that enhances AMP generation without requiring explicit data modifications. By embedding classifier guidance within the loss computation, cdGAN dynamically optimizes both peptide diversity and functionality. Comparative studies indicate that cdGAN outperforms conventional guided-GAN architectures, such as Conditional GANs and Auxiliary Classifier GANs, while achieving performance comparable to or exceeding established AMP design methods. Additionally, cdGAN's flexible architecture allows for the simultaneous optimization of multiple peptide attributes. To demonstrate this capability, we introduce a multi-task classifier based on the Evolutionary Scale Modeling 2 (ESM2) model, enabling cdGAN to assess both antimicrobial activity and peptide structural properties in parallel. This enhancement improves the likelihood of generating viable therapeutic candidates with enhanced antimicrobial effectiveness and reduced toxicity.

Antimicrobial peptides (AMPs) play a significant role in guiding drug design, advancing targeted therapies, and cancer treatment research. The function of peptides is highly associated with their three-dimensional structure. AMPs particularly favor alpha-helical structures, or alpha-folds, due to their ability to disrupt the protective layers that surround cells effectively and their structural stability. Existing classifiers mainly identify AMPs but overlook their structural fold which can provide valuable insights into their function. To address this limitation, we introduce an innovative multitask classifier that recognizes AMPs and predicts their alphahelical folds simultaneously. Our approach employs k-mers and Transformer networks for efficient, accurate multitask classification. Results on the datasets indicate comparable performance compared to single-task methods in half the time and complexity.

This paper presents an enhanced methodology for Recurrence Quantification Analysis (RQA) designed specifically for video analysis. By utilizing image quality metrics, with a focus on the Peak Signal-to-Noise Ratio (PSNR), we determine meaningful values for the RQA threshold $\varepsilon$, a critical factor for successful image processing. Utilizing the False Nearest Neighbors (FNN) technique, we identify the optimal embedding dimension D for each patch within the video frames. Our approach produces a heatmap that visualizes temporal recurrence information for each video patch.

The goal of this work is to propose a simple yet efficient way to dynamically transform a video stream according to the functional properties of the visual system. To achieve this goal, we extend to video sequences the Retina-Inspired Filter (RIF), which we have recently proposed for still images. Under the assumption that the input signal remains constant for a given time, the RIF decomposition was proven to be in-vertible, meaning that the image could be perfectly recovered. In this paper, we relax this assumption into a piece-wise constant input and analytically prove that the RIF can be applied to a group of pictures (GOP). We experimentally show that the size of GOP is important when motion appears, as some artifacts are generated. However, in the absence of motion among the GOP frames we can still perfectly reconstruct the video frames reducing the computational cost of the whole process.

Predicting the structural class of a protein from its amino acid sequence is among the most significant problems in bioinformatics, especially for proteins with a low sequence similarity. While current methods using recurrent neural networks achieve a notable accuracy in this task, their approach relies on extracting a large quantity of features, impacting the efficiency and reliability of the prediction. In this work, we introduce an efficient and accurate classification scheme based on chaos game representation and recurrent neural networks. The proposed scheme achieves comparable results with state-of-the-art methods, while using a significantly lower-dimensional representation of the feature space.

The goal of this work is to propose a simple yet efficient way to dynamically transform a sequence of images according to the functional properties of the visual system. To achieve this goal, we extend to video sequences the Retina-Inspired Filter (RIF), which we have recently proposed for still images. Under the assumption that the input signal remains constant for a given time, the RIF decomposition was proven to be invertible, meaning that the image could be perfectly recovered. In this paper, we relax this assumption into a piece-wise constant input and we prove that RIF can be applied to a Group Of Pictures (GOP). Under the condition that a GOP consists of frames without strong pixel motion, we mathematically prove and experimentally show that when RIF is applied to GOP, whatever the size of the GOP is, we are still able to perfectly recover the video frames and at the same time simplify the complexity of the whole process. In addition, we show that while the GOP size increases, the memory cost required to store this amount of frames is sufficiently reduced.

MOTIVATION: Protein structural class prediction is one of the most significant problems in bioinformatics, as it has a prominent role in understanding the function and evolution of proteins. Designing a computationally efficient but at the same time accurate prediction method remains a pressing issue, especially for sequences that we cannot obtain a sufficient amount of homologous information from existing protein sequence databases. Several studies demonstrate the potential of utilizing chaos game representation along with time series analysis tools such as recurrence quantification analysis, complex networks, horizontal visibility graphs (HVG) and others. However, the majority of existing works involve a large amount of features and they require an exhaustive, time consuming search of the optimal parameters. To address the aforementioned problems, this work adopts the generalized multidimensional recurrence quantification analysis (GmdRQA) as an efficient tool that enables to process concurrently a multidimensional time series and reduce the number of features. In addition, two data-driven algorithms, namely average mutual information and false nearest neighbors, are utilized to define in a fast yet precise manner the optimal GmdRQA parameters. RESULTS: The classification accuracy is improved by the combination of GmdRQA with the HVG. Experimental evaluation on a real benchmark dataset demonstrates that our methods achieve similar performance with the state-of-the-art but with a smaller computational cost. AVAILABILITY AND IMPLEMENTATION: The code to reproduce all the results is available at https://github.com/aretiz/protein_structure_classification/tree/main. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper introduces a novel coding/decoding mechanism that mimics one of the most important properties of the human visual system: its ability to enhance the visual perception quality in time. In other words, the brain takes advantage of time to process and clarify the details of the visual scene. This characteristic is yet to be considered by the state-of-the-art quantization mechanisms that process the visual information regardless the duration of time it appears in the visual scene. We propose a compression architecture built of neuroscience models; it first uses the leaky integrate-and-fire (LIF) model to transform the visual stimulus into a spike train and then it combines two different kinds of spike interpretation mechanisms (SIM), the time-SIM and the rate-SIM for the encoding of the spike train. The time-SIM allows a high quality interpretation of the neural code and the rate-SIM allows a simple decoding mechanism by counting the spikes. For that reason, the proposed mechanisms is called Dual-SIM quantizer (Dual-SIMQ). We show that (i) the time-dependency of Dual-SIMQ automatically controls the reconstruction accuracy of the visual stimulus, (ii) the numerical comparison of Dual-SIMQ to the state-of-the-art shows that the performance of the proposed algorithm is similar to the uniform quantization schema while it approximates the optimal behavior of the non-uniform quantization schema and (iii) from the perceptual point of view the reconstruction quality using the Dual-SIMQ is higher than the state-of-the-art.

Image compression still remains one of the most challenging scientific fields as it is widely used in almost every kind of application. To design ground-breaking architectures the image processing community concentrated at first on visual perception. However, understanding how the visual system senses the semantics of the visual scene is still a big issue. Recently, a neuro-inspired compression (NICE) system was introduced, trying to encode the visual information by mimicking the firing mechanisms of neurons. The performance of this system was promising but inefficient when opposed to the JPEG standard. In this work, we aim at improving the architecture design of the NICE system. Thus, we propose 3 different architectures and we show that the quality of the reconstruction is substantially enhanced. In addition, the NICE system outperforms the JPEG standard according to the assessment of two full-reference metrics; the peak signal-to-noise ratio (PSNR) and the structure similarity index measure (SSIM), except for very low bits per pixel values.

This paper aims to build an image coding system based on a model of the mammalian retina. The retina is the light-sensitive layer of tissue located on the inner coat of the eye and it is responsible for vision. Inspired by the way the retina handles and compresses visual information and based on previous studies we aim to build and analytically study a retinal-inspired image quantizer, based on the Leaky Integrate-and-Fire (LIF) model, a neural model approximating the behavior of the ganglion cells of the Ganglionic retinal layer that is responsible for visual data compression. In order to have a more concrete view of the encoder's behavior, in our experiments, we make use of the spatiotemporal decomposition layers provided by extensive studies on a previous retinal layer, the Outer Plexiform Layer (OPL). The decomposition layers produced by the OPL, are being encoded using our LIF image encoder and then, they are reconstructed to observe the encoder's efficiency.

This paper presents a novel neuro-inspired quantization model which is the extension of the recently released perfect-Leaky Integrate and Fire (LIF) model. We propose that the LIF, which is a very efficient neuromathematical model that describes the spike generation neural mechanism, can lead to a groundbreaking and above all dynamic compression algorithm which is called LIF encoder/decoder. We also prove that under some assumptions, there is a link between the novel LIF encoder/decoder and the conventional Uniform Deadzone Quantizer (UDQ).

This paper introduces a novel filter, which is inspired by the human retina. The human retina consists of three different layers: the Outer Plexiform Layer (OPL), the inner plexiform layer, and the ganglionic layer. Our inspiration is the linear transform which takes place in the OPL and has been mathematically described by the neuroscientific model "virtual retina." This model is the cornerstone to derive the non-separable spatio-temporal OPL retina-inspired filter, briefly renamed retina-inspired filter, studied in this paper. This filter is connected to the dynamic behavior of the retina, which enables the retina to increase the sharpness of the visual stimulus during filtering before its transmission to the brain. We establish that this retina-inspired transform forms a group of spatio-temporal Weighted Difference of Gaussian (WDoG) filters when it is applied to a still image visible for a given time. We analyze the spatial frequency bandwidth of the retina-inspired filter with respect to time. It is shown that the WDoG spectrum varies from a lowpass filter to a bandpass filter. Therefore, while time increases, the retina-inspired filter enables to extract different kinds of information from the input image. Finally, we discuss the benefits of using the retina-inspired filter in image processing applications such as edge detection and compression.

In this paper, we aim to propose a video codec based on the novel retina-inspired filter and retina-inspired quantizer which both perform according to the early visual system. The recently released non-separable spatiotemporal OPL retina-inspired filter enables to progressively extract different kind of information from the input signal which is the sequence of pictures of a video stream. This retina inspired transform has been proven to be a redundant frame which ensures a perfect reconstruction when no quantization appears. The reduction of this redundancy is achieved by a quantization which is inspired by the spike generation mechanism of ganglion cells. This mechanism has been approximated by the Rank Order Coder (ROC) and the Leaky-Integrate and Fire (LIF) models. The ROC model encodes the rank of the spikes and it has been proposed as a complete and very efficient codec for still-images. However, its limitations concerning the reconstruction method forced us to focus our attention on LIF which encodes the spike delays. We approximate the LIF by a scalar quantizer with a dead-zone. This is the first attempt to build a complete retina-inspired video codec which gives promising reconstruction results at low bitrate and high reconstruction quality.

This paper introduces a novel non-Separable sPAtioteMporal filter (non-SPAM) which enables the spatiotemporal decomposition of a still-image. The construction of this filter is inspired by the model of the retina which is able to selectively transmit information to the brain. The non-SPAM filter mimics the retinal-way to extract necessary information for a dynamic encoding/decoding system. We applied the non-SPAM filter on a still image which is flashed for a long time. We prove that the non-SPAM filter decomposes the still image over a set of time-varying difference of Gaussians, which form a frame. We simulate the analysis and synthesis system based on this frame. This system results in a progressive reconstruction of the input image. Both the theoretical and numerical results show that the quality of the reconstruction improves while the time increases.