Biology subjects

Rahmani, B.

Publications and source records attributed to Rahmani, B..

3 recordsLinked to original sources

bioRxiv2023

An actor-model framework for visual sensory encoding

A fundamental challenge in neuroengineering is determining a proper input to a sensory system that yields the desired functional output. In neuroprosthetics, this process is known as sensory encoding, and it holds a crucial role in prosthetic devices restoring sensory perception in individuals with disabilities. For example, in visual prostheses, one key aspect of image encoding is to down-sample the images captured by a camera to a size matching the number of inputs and resolution of the prosthesis. Here, we show that down-sampling an image using the inherent computation of the retinal network yields better performance compared to a learning-free down-sampling encoding. We validated a learning-based approach (actor-model framework) that exploits the signal transformation from photoreceptors to retinal ganglion cells measured in explanted retinas. The actor-model framework generates down-sampled images eliciting a neuronal response in-silico and ex-vivo with higher neuronal reliability to the one produced by original images compared to a learning-free approach (i.e. pixel averaging). In addition, the actor-model learned that contrast is a crucial feature for effective down-sampling. This methodological approach could serve as a template for future image encoding strategies. Ultimately, it can be exploited to improve encoding strategies in visual prostheses or other sensory prostheses such as cochlear or limb.

bioengineering↗

bioRxiv2019

Automation of (Macro)molecular Properties Using a Bootstrapping Swarm Artificial Neural Network Method: Databases for Machine Learning

In this study, we employed a novel method for prediction of (macro)molecular properties using a swarm artificial neural network method as a machine learning approach. In this method, a (macro)molecular structure is represented by a so-called description vector, which then is the input in a so-called bootstrapping swarm artificial neural network (BSANN) for training the neural network. In this study, we aim to develop an efficient approach for performing the training of an artificial neural network using either experimental or quantum mechanics data. In particular, we aim to create different user-friendly online accessible databases of well-selected experimental (or quantum mechanics) results that can be used as proof of the concepts. Furthermore, with the optimized artificial neural network using the training data served as input for BSANN, we can predict properties and their statistical errors of new molecules using the plugins provided from that web-service. There are four databases accessible using the web-based service. That includes a database of 642 small organic molecules with known experimental hydration free energies, the database of 1475 experimental pKa values of ionizable groups in 192 proteins, the database of 2693 mutants in 14 proteins with given values of experimental values of changes in the Gibbs free energy, and a database of 7101 quantum mechanics heat of formation calculations.\n\nAll the data are prepared and optimized in advance using the AMBER force field in CHARMM macromolecular computer simulation program. The BSANN is code for performing the optimization and prediction written in Python computer programming language. The descriptor vectors of the small molecules are based on the Coulomb matrix and sum over bonds properties, and for the macromolecular systems, they take into account the chemical-physical fingerprints of the region in the vicinity of each amino acid.\n\nO_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY Graphical TOC Entry C_FIG_DISPLAY

biophysics↗

bioRxiv2019

Predicting failures of Molteno and Baerveldt glaucoma drainage devices using machine learning models

The purpose of this retrospective study is to measure machine learning models ability to predict glaucoma drainage device failure based on demographic information and preoperative measurements. The medical records of sixty-two patients were used. Potential predictors included the patients race, age, sex, preoperative intraocular pressure, preoperative visual acuity, number of intraocular pressure-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final intraocular pressure greater than 18 mm Hg, reduction in intraocular pressure less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine. Recursive feature elimination was used to shrink the number of predictors and grid search was used to choose hyperparameters. To prevent leakage, nested cross-validation was used throughout. Overall, the best classifier was logistic regression.

bioinformatics↗