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Many computational tools have been proposed during the two last decades for predicting piRNAs, which are molecules with important role in post-transcriptional gene regulation. However, these tools are mostly based on only one feature that is generally related to the sequence. Discoveries in the domain of piRNAs are still in their beginning stages, and recent publications have shown many new properties. Here, we propose an integrative approach for piRNA prediction in which several types of genomic and epigenomic properties that can be used to characterize these molecules are examined. We reviewed and extracted a large number of piRNA features from the literature that have been observed experimentally in several species. These features are represented by different kernels, in a Multiple Kernel Learning based approach, implemented within an object-oriented framework. The obtained tool, called IpiRId, shows prediction results that attain more than 90% of accuracy on different tested species (human, mouse and fly), outperforming all existing tools. Besides, our method makes it possible to study the validity of each given feature in a given species. Finally, the developed tool is modular and easily extensible, and can be adapted for predicting other types of ncRNAs. The IpiRId software and the user-friendly web-based server of our tool are now freely available to academic users at: https://evryrna.ibisc.univ-evry.fr/evryrna/.

In this paper, we suggest a novel method for ECG baseline correction, exclusively based on pattern recognition tools, namely, dominant points (DPs). The DPs are computed by the Douglas-Peucker curve simplification algorithm. The so-computed DPs include peak and baseline points, the discrimination of which yields gradual piecewise linear estimation of the baseline wander (BaselineW) in two iterations. At each iteration, the current BaselineW is subtracted from the input signal according to the decomposition scheme: ECG approximately ECG(ZBLW) + BaselineW, where ECG(ZBLW) is the underlying baseline wander free ECG. The method targets many types of baseline deviations in a unified approach: baseline drift due to respiration, amplitude modulation due to perspiration and abrupt potential change due to electrode loose contact. We tested the developed method on a variety of ECG records including half synthesized records contaminated with different types of baseline deviations (simulated) noise, and on records from the MITBIH database presenting important baseline deviations, including normal and abnormal heart beats cases. The method showed good performance in computing a piecewise linear estimation of the baseline deviation and in extracting the ECG(ZBLW), which represents the clinically significant electrocardiogram information.

Electroencephalography is an important clinical tool for the evaluation and treatment of neurophysiological disorders related to epilepsy. However, the analysis and interpretation of the electroencephalogram (EEG) is not an easy task due to the variety of waveforms that are possible. Consequently, EEG analysis is in need of an objective and quantitative methodology. In this paper, an automated system for diagnosing epilepsy is presented. The system combines both the electrocerebral activity related to epilepsy resulting from EEG and other neurophysiological expertise, mainly based on clinical symptoms that occur during the patient's clinical attack, to avoid misdiagnosis. The system consists of two major stages. The first is a feature extractor in which half-waves are detected and artefacts are eliminated. The second and most important stage is a knowledge-based system for recognising and classifying epileptiform events. In particular, the analysis is based on the detected EEG patterns representing epileptiform activity, localization information of discharge focus and clinical symptoms. Once a diagnosis is established, the system also proposes a therapy. The proposed system has been tested using many different clinical cases, and the obtained experimental results are acceptable.

For young people, scoliosis deformities are an evolving process which must be detected and treated as early as possible. The moiré technique is simple, inexpensive, not aggressive and especially convenient for detecting spinal deformations. Doctors make their diagnosis by analysing the symmetry of fringes obtained by such techniques. In this paper, we present a computer vision system for help diagnosing spinal deformations using noisy moiré images of the human back. The approach adopted in this paper consists of extracting fringe contours from moiré images, then localizing some anatomical features (the spinal column, lumbar hollow and shoulder blades) which are crucial for 3D surface generation carried out using Mota's relaxation operator. Finally, rules furnished by doctors are used to derive the kind of spinal deformation and to yield the diagnosis. The proposed system has been tested on a set of noisy moiré images, and the experimental result have shown its robustness and reliability for the recognition of most scoliosis deformities.