Prediction of DNA i-motifs via machine learning

i-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative iM forming sequences are widely distributed in the human genome, the folding status and strength of putative iMs vary dramatically. Much previous research on iM has focused on assessing the iM folding properties using biophysical experiments. However, there are no dedicated computational tools for predicting the folding status and strength of iM structures. Here, we introduce a machine learning pipeline, iM-Seeker, to predict both folding status and structural stability of DNA iMs. The programme iM-Seeker incorporates a Balanced Random Forest classifier trained on genome-wide iMab antibody-based CUT&Tag sequencing data to predict the folding status and an Extreme Gradient Boosting regressor to estimate the folding strength according to both literature biophysical data and our in-house biophysical experiments. iM-Seeker predicts DNA iM folding status with a classification accuracy of 81% and estimates the folding strength with coefficient of determination (R2) of 0.642 on the test set. Model interpretation confirms that the nucleotide composition of the C-rich sequence significantly affects iM stability, with a positive correlation with sequences containing cytosine and thymine and a negative correlation with guanine and adenine

iM-Seeker: a webserver for DNA i-motifs prediction and scoring via automated machine learning

DNA, beyond its canonical B-form double helix, adopts various alternative conformations, among which the i-motif, emerging in cytosine-rich sequences under acidic conditions, holds significant biological implications in transcription modulation and telomere biology. Despite recognizing the crucial role of i-motifs, predictive software for i-motif forming sequences has been limited. Addressing this gap, we introduce 'iM-Seeker', an innovative computational platform designed for the prediction and evaluation of i-motifs. iM-Seeker exhibits the capability to identify potential i-motifs within DNA segments or entire genomes, calculating stability scores for each predicted i-motif based on parameters such as the cytosine tracts number, loop lengths, and sequence composition. Furthermore, the webserver leverages automated machine learning (AutoML) to effortlessly fine-tune the optimal i-motif scoring model, incorporating user-supplied experimental data and customised features. As an advanced, versatile approach, 'iM-Seeker' promises to advance genomic research, highlighting the potential of i-motifs in cell biology and therapeutic applications. The webserver is freely available at https://im-seeker.org

A Combined Theoretical and Photoelectron Spectroscopy Study of Al3Hn- (n=1-9) clusters

Combined photoelectron spectroscopic experiments and computational studies have been performed on Al3Hn- (n=1-9) clusters. Three modes of hydrogen bonding to the Al-3 moiety have been observed: terminal, bridging, and capping. Among various hydrides, Al3H5- and Al3H8- clusters have highest HOMO-LUMO gap and largest electron affinity, respectively. Our studies indicate that as the number of hydrogen atoms increase the presence of AlH2 groups, representing the tetrahedral coordination of the Al atom, which in turn led to the stoichiometric ring structure

Facial acne recognition system based on machine learning

Facial acne plagues many people, causing appearance anxiety and even psychological problems. However, the skin detector or software using traditional image processing technology on the market cannot give consideration to both low cost and high precision. This research aims to develop a low-cost and efficient method to detect facial acne through machine learning. We use hundreds of facial acne patients' pictures collected on the network, use Photoshop to split into thousands of pictures of appropriate size and manually label them as data sets and verification sets, and train them in YOLOX model to finally identify and label skin problems such as facial pustules, acne marks, etc. through one person's facial photos. At present, we have run the system on the desktop (AMD R7 4800H+GTX1650) normally, using the latest YOLOX framework of the open-source YOLO series. In order to improve the learning quality under limited training data, image preprocessing including sharpening and flipping is introduced. The experimental results show that the recognition rate of this method for some skin problems can reach 80%. By further expanding the data set, it can achieve low-cost facial problem recognition. At the same time, this research is also a good case of applying deep learning technology to product design.</jats:p

Deep Learning in RNA Structure Studies

Deep learning, or artificial neural networks, is a type of machine learning algorithm that can decipher underlying relationships from large volumes of data and has been successfully applied to solve structural biology questions, such as RNA structure. RNA can fold into complex RNA structures by forming hydrogen bonds, thereby playing an essential role in biological processes. While experimental effort has enabled resolving RNA structure at the genome-wide scale, deep learning has been more recently introduced for studying RNA structure and its functionality. Here, we discuss successful applications of deep learning to solve RNA problems, including predictions of RNA structures, non-canonical G-quadruplex, RNA-protein interactions and RNA switches. Following these cases, we give a general guide to deep learning for solving RNA structure problems.</jats:p