Motivation

Every cell uses cellular respiration machinery to oxidize food molecules such as glucose (sugars) to carbon dioxide and water, thus obtaining energy-carrying molecules in the form of adenosine triphosphate. This crucial process can not occur without the aid of electron transport chains, a series of 5 protein complexes embedded in the inner mitochondrial membrane. A variety of human diseases such as Parkinson's disease, pulmonary hypertension, and Alzheimer's disease involve the functional loss of these protein complexes. Thus, investigating these electron complexes is an ongoing concern for biologists to better understand the molecular mechanisms of important human diseases. In this research, we employed two representation learning methods namely word embedding and transfer learning to analyse electron complex sequences and create efficient feature sets before using support vector machine algorithm to classify them.

Result

On an average, our final classification models achieved performance of 96%, 96.1%, 95.3%, and 0.86, respectively on cross-validation data. For the independent test data, those corresponding performance scores are 95.3%, 92.6%, 94%, and 0.87. Using representation learning methods, we show that simple machine learning method is on par with existing deep neural network method on the task of categorizing electron complexes while enjoying much faster way for feature generation.

Dataset

The dataset used in this server were from Le et al’s work which were retrieved from UniProt and GeneOntology. The detail of the dataset is listed in the table below.

If you would like to build a model and evaluate our model, we provide the dataset as the below link

Submission

In order to avoid the errors, please submit the sequence in fasta format (we also give you the fasta file examples). The user can choose two options to submit, including paste the sequence into text area and upload sequence file. The user can submit one single fasta file or multiple fasta file. In the result page, we show the results for the sequences separately.

> A2XVZ1
MATTASPFLSPAKLSLERRLPRATWTARRSVRFPPVRAQDQQQQVKEEEEEAAVENLPPP
PQEEEQRRERKTRRQGPAQPLPVQPLAESKNMSREYGGQWLSCTTRHIRIYAAYINPETN
AFDQTQMDKLTLLLDPTDEFVWTDETCQKVYDEFQDLVDHYEGAELSEYTLRLIGSDLEH
FIRKLLYDGEIKYNMMSRVLNFSMGKPRIKFNSSQIPDVK
> P31039
MSGVAAVSRLWRARRLALTCTKWSAAWQTGTRSFHFTVDGNKRSSAKVSDAISAQYPVVD
HEFDAVVVGAGGAGLRAAFGLSEAGFNTACVTKLFPTRSHTVAAQGGINAALGNMEEDNW
RWHFYDTVKGSDWLGDQDAIHYMTEQAPASVVELENYGMPFSRTEDGKIYQRAFGGQSLK
FGKGGQAHRCCCVADRTGHSLLHTLYGRSLRYDTSYFVEYFALDLLMESGECRGVIALCI
EDGSIHRIRARNTVIATGGYGRTYFSCTSAHTSTGDGTAMVTRAGLPCQDLEFVQFHPTG
IYGAGCLITEGCRGEGGILINSQGERFMERYAPVAKDLASRDVVSRSMTLEIREGRGCGP
EKDHVYLQLHHLPPAQLAMRLPGISETAMIFAGVDVTKEPIPVLPTVHYNMGGIPTNYKG
QVLRHVNGQDQGVPGLYACGEAACASVHGANRLGANSLLDLVVFGRACALSIAESCRPGD
KVPSIKPNAGEESVMNLDKLRFANGSIRTSELRLNMQKSMQSHAAVFRVGSVLQEGCEKI
SSLYGDLRHLKTFDRGMVWNTDLVETLELQNLMLCALQTIYGAEARKESRGGPRREDFKE
RVDEYDYSKPIQGQQKKPFEQHWRKHTLSYVDIKTGKVTLEYRPVIDRTLNETDCATVPP
AIGSY
> O31214
MLASAGGYWPMSAQGVNKMRRRVLVAATSVVGAVGAGYALVPFVASMNPSARARAAGAPV
EADISKLEPGALLRVKWRGKPVWVVHRSPEMLAALSSNDPKLVDPTSEVPQQPDYCKNPT
RSIKPEYLVAIGICTHLGCSPTYRPEFGPDDLGSDWKGGFHCPCHGSRFDLAARVFKNVP
APTNLVIPKHVYLNDTTILIGEDRGSA
> E0TW67
MIFLFRALKPLLVLALLTVVFVLGGCSNASVLDPKGPVAEQQSDLILLSIGFMLFIVGVV
FVLFTIILVKYRDRKGKDNGSYNPKIHGNTFLEVVWTVIPILIVIALSVPTVQTIYSLEK
APEATKDKEPLVVHATSVDWKWVFSYPEQDIETVNYLNIPVDRPILFKISSADSMASLWI
PQLGGQKYAMAGMLMDQYLQADEVGTYQGRNANFTGEHFADQEFDVNAVTEKDFNSWVKK
TQNEAPKLTKEKYDQLMLPENVDELTFSSTHLKYVDHGQDAEYAMEARKRLGYQAVSPHS
KTDPFENVKENEFKKSDDTEE
> A5GCQ9
MGYVELIAALRRDGEEQLEKIRSDAEREAERVKGDASARIERLRAEYAERLASLEAAQAR
AILADAESKASSIRLATESALAVRLFLLARSSLHHLRDEGYEQLFADLVRELPPGEWRRV
VVNPADMALAARHFPNAEIVSHPAIVGGLEVSEEGGSISVVNTLEKRMERAWPELLPEIL
RDIYREL